Handbook of Traffic Psychology

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Handbook of Traffic Psychology

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Handbook of Traffic Psychology

Bryan E. Porter Old Dominion University Norfolk VA, USA

AMSTERDAM l BOSTON l HEIDELBERG l LONDON l NEW YORK l OXFORD l PARIS SAN DIEGO l SAN FRANCISCO l SINGAPORE l SYDNEY l TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 32 Jamestown Road, London NW1 7BY, UK 225 Wyman Street, Waltham, MA 02451, USA 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA First edition 2011 Copyright Ó 2011 Elsevier Inc. All rights reserved with the exception of Chapter 32 which is in the Public Domain No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+ 44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively, visit the Science and Technology Books website at www.elsevierdirect.com/rights for further information Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN : 978-0-12-381984-0 For information on all Academic Press publications visit our website at www.elsevierdirect.com Typeset by TNQ Books and Journals Printed and bound in United States of America 11 12 13 14

10 9 8 7 6 5 4 3 2 1

Contents

Preface List of Contributors

vii ix

9. Neuroscience and Young Drivers

Part I Theories, Concepts, and Methods 1. How Many E’s in Road Safety?

3 13

27

Martha Hı´jar, Ricardo Pe´rez-Nu´n˜ez and Cristina Incla´n-Valadez

4. Self-Report Instruments and Methods

127

Maria T. Schultheis and Kevin J. Manning

11. Visual Attention While Driving

137

12. Social, Personality, and Affective Constructs in Driving

149

Dwight Hennessy

13. Mental Health and Driving 43

Timo Lajunen and Tu¨rker O¨zkan

5. Naturalistic Observational Field Techniques for Traffic Psychology Research

10. Neuroscience and Older Drivers

David Crundall and Geoffrey Underwood

Ray Fuller

3. CaseeControl Studies in Traffic Psychology

109

A. Ian Glendon

John A. Groeger

2. Driver Control Theory

Part II Key Variables to Understand in Traffic Psychology

165

Joanne E. Taylor

14. Person and Environment: Traffic Culture

179

Tu¨rker O¨zkan and Timo Lajunen

61

David W. Eby

15. Human Factors and Ergonomics

193

Ilit Oppenheim and David Shinar

6. Naturalistic Driving Studies and Data Coding and Analysis Techniques

73

Sheila G. Klauer, Miguel Perez and Julie McClafferty

7. Driving Simulators as Research Tools in Traffic Psychology

16. Factors Influencing Safety Belt Use 215 87

Oliver Carsten and A. Hamish Jamson

8. Crash Data Sets and Analysis Young-Jun Kweon

Part III Key Problem Behaviors Jonathon M. Vivoda and David W. Eby

17. Alcohol-Impaired Driving 97

231

Krystall Dunaway, Kelli England Will and Cynthia Shier Sabo v

vi

Contents

18. Speed(ing)

249

Part V Major Countermeasures to Reduce Risk

267

29. Driver Education and Training

Thomas D. Berry, Kristie L. Johnson and Bryan E. Porter

19. Running Traffic Controls Richard Retting

20. Driver Distraction

275

Michael A. Regan and Charlene Hallett

21. Driver Fatigue

403

Esko Keskinen and Kati Hernetkoski

30. Persuasion and Motivational Messaging

423

David S. Anderson

287

Jennifer F. May

31. Enforcement

Part IV Vulnerable and Problem Road Users 22. Young Children and “Tweens” 23. Young Drivers

Part VI Interdisciplinary Issues

315

32. The Intersection of Road Traffic Safety and Public Health

Patty Huang and Flaura Koplin Winston

24. Older Drivers

457

David A. Sleet, Ann M. Dellinger and Rebecca B. Naumann

339

Barbara Freund and Paula Smith

25. Pedestrians

Bryan E. Porter

301

Kelli England Will

441

33. Public Policy 353

471

Rune Elvik

Ron Van Houten

26. Bicyclists

367

34. Travel Mode Choice

Ian Walker

27. Motorcyclists

375

David J. Houston

485

Stephen G. Stradling

35. Road Use Behavior in Sub-Saharan Africa 503 Karl Peltzer

28. Professional Drivers Tova Rosenbloom

389 Index

519

Preface

In compiling the Handbook, I had a vision to place into one work the latest research findings and future questions to be pursued in the field. I wanted the work to reach multiple audiences, including advanced undergraduates learning about applications and methods, graduate students needing the latest reviews and suggestions for research questions, and scholars in the field who benefit from one resource representing the field at-large for ease of reference and background. The final result, I believe, completes the true meaning of “handbook”da “how to” resource to know, and do work in, the field. It can even be adopted as a textbook for courses in traffic psychology. The book’s chapters are organized into six main sections: (1) Theories, Concepts, and Methods; (2) Key Variables to Understand in Traffic Psychology; (3) Key Problem Behaviors; (4) Vulnerable and Problem Road Users; (5) Major Countermeasures to Reduce Risk; and (6) Interdisciplinary Issues. Each chapter is a stand-alone resource for readers who want to start with a particular issue or topic. The chapters within each section also have different purposes and, at times, will attract different audiences whose needs vary depending on experience in traffic psychology. The material within is global, coming as it does from contributors representing 12 countries on five continents. There is also a breadth of interdisciplinary perspective, with experts from psychology, engineering, medicine, political science, and public health. The first section, Theories, Concepts, and Methods, gives readers an overview of traffic psychology as a field (Groeger), theoretical contributions (Fuller), and “how to” chapters to practice common methods. Caseecontrols (Hı´jar, Pe´rez-Nun˜ez, and Incla´n-Valadez), self-report ¨ zkan), direct observation (Eby), in-vehicle (Lajunen and O instrumentation (Klauer, Perez, and McCafferty), simulation (Carsten and Jamson), and crash data set methods (Kweon) are discussed. New students in traffic psychology, or experienced scholars wishing to consider different methods, will particularly benefit. In the second section, Key Variables, a wide range of variables are explored that providedliterallydthe “set” of those thought to be among the most important to understand. Authors explore neuroscience contributions to driving (Glendon for young drivers; Schultheis and

Manning for older drivers), which are becoming very important to the field and its future potential. Visual search patterns (Crundall and Underwood), social, personality, and affect (Hennessy), and mental health impacts (Taylor) are explored. Finally, the person, environment, and culture ¨ zkan and Lajunen) and human factors (Oppenheim and (O Shinar) impacts are reviewed. In these chapters, readers can review the latest information and research questions from within a person through to that person’s interactions with the larger social system. The third section will be very popular with readers interested in particular behaviors. Here, chapters provide what is the latest knowndand unknowndabout major problem behaviors leading to crashes, injuries, and fatalities. These behaviors are critical for traffic safety at-large, not just traffic psychology. These are safety-restraint use (Vivoda and Eby), impaired driving (Dunaway, Will, and Sabo), speeding (Berry, Johnson, and Porter), running traffic controls (Retting), distracted driving (Regan and Hallett), and fatigued driving (May). Vulnerable road users are the focus of the fourth section. Traffic psychology and related fields have a significant interest in reducing harm to subgroups of people who are disproportionately harmed on the roadways or who need particular protections that they cannot provide themselves. The field also focuses on those subgroups that disproportionally create roadway problems. This section’s chapters review young children and “tweens” (Will), young drivers (Huang and Winston), older drivers (Freund and Smith), pedestrians (Van Houten), bicyclists (Walker), motorcyclists (Houston), and professional drivers (Rosenbloom). Traffic psychologists and their colleagues are often called upon to assist in the development and evaluation of countermeasures to reduce roadway risks. The fifth section reviews major countermeasures that have received the most attention to date. Specifically, driver education and training (Keskinen and Hernetkoski), persuasion and motivational messaging (Anderson), and enforcement (Porter) are discussed. Readers in the field, or those practicing in general transportation sciences and policy, will find these chapters useful in their discussions about what questions and countermeasures may or may not be appropriate to address their needs.

vii

viii

Finally, the sixth section provides interdisciplinary perspectives. Readers will find how traffic psychology intersects with public health (Sleet, Dellinger, and Naumann) and public policy (Elvik). Environmental protection by reducing personal vehicle use in favor of public transport or other mode choices has a growing research base (Stradling). Also, traffic psychology’s role to assist worldwide injury prevention, with Africa as an important and critical example, is outlined (Peltzer). Given the ambitious nature of the work, I thank my family, Debbie, Amanda, and Sadie, and my students, whose patience and support I much appreciate. Old Dominion University’s support has also been substantial to my work on the Handbook, including a semester’s research leave to help organize the project. I thank my publisher at Elsevier, Nikki Levy, for her support of this work, and Barbara Makinster, who was my development editor. Finally, I thank my

Preface

colleagues who kindly offered advice on early drafts of the handbook material: David W. Eby, Ian Glendon, Raphael Huguenin, Geoffrey Underwood, and Kelli England Will. I am delighted to share the Handbookdfinally after so much planning and executiondwith readers interested in traffic psychology. I am excited to share how my field can make important contributions to reducing crashes, injuries, and fatalities on our roadways. I am honored to provide a forum for my colleagues to share their tremendous experience with those wanting to know who we are as a discipline. I am also proud to provide this resource to the field to celebrate its accomplishments. On behalf of the Handbook’s authors, I hope you both enjoy the book and find it useful to your own pursuits in our exciting discipline. Bryan E. Porter Old Dominion University

List of Contributors

David S. Anderson Center for the Advancement of Public Health, College of Education and Human Development, George Mason University, Fairfax, VA, USA Thomas D. Berry Christopher Newport University, Newport News, VA, USA Oliver Carsten Institute for University of Leeds, Leeds, UK David Crundall UK

Transport

Studies,

University of Nottingham, Nottingham,

David J. Houston Department of Political Science, University of Tennessee, Knoxville, TN, USA Patty Huang Center for Injury Research and Prevention and Division of Child Development and Rehabilitation Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA Cristina Incla´n-Valadez Department of Geography and Environment, London School of Economics and Political Science, London, UK

Ann M. Dellinger Centers for Disease Control and Prevention, Atlanta, GA, USA

A. Hamish Jamson Institute for Transport Studies, University of Leeds, Leeds, UK

Krystall Dunaway Department of Pediatrics, Eastern Virginia Medical School, Norfolk, VA, USA

Kristie L. Johnson VA, USA

David W. Eby Michigan Center for Advancing Safe Transportation throughout the Lifespan and University of Michigan Transportation Research Institute, Ann Arbor, MI, USA

Esko Keskinen Department of Behavioral Sciences and Philosophy, University of Turku, Turku, Finland

Rune Elvik Norway

Institute of Transport Economics, Oslo,

Young-Jun Kweon Virginia Department of Transportation, Charlottesville, VA, USA

Barbara Freund Health Sciences Division, Pasadena City College, Pasadena, CA, USA

Timo Lajunen Department of Psychology, Middle East Technical University, Ankara, Turkey

Ray Fuller School of Psychology, Trinity College Dublin, Dublin, Ireland

Kevin J. Manning Department of Psychology, Drexel University, Philadelphia, PA, USA

A. Ian Glendon School of Psychology, University, Gold Coast, Queensland, Australia

Griffith

Jennifer F. May Department of Psychology, Old Dominion University, Norfolk, VA, USA

John A. Groeger School of Applied Psychology, University College Cork, Cork, Ireland

Julie McClafferty Center for Automotive Safety Research, Virginia Tech Transportation Institute, Blacksburg, VA, USA

Charlene Hallett French Institute of Science and Technology for Transport, Development and Networks, Lyon, France Dwight Hennessy Department of Psychology, Buffalo State College, Buffalo, NY, USA

Old Dominion University, Norfolk,

Sheila G. Klauer Center for Automotive Safety Research, Virginia Tech Transportation Institute, Blacksburg, VA, USA

Rebecca B. Naumann Centers for Disease Control and Prevention, Atlanta, GA, USA

Kati Hernetkoski Department of Behavioral Sciences and Philosophy, University of Turku, Turku, Finland

Ilit Oppenheim Ben-Gurion University of the Negev, Beer Sheva, Israel ¨ zkan Department of Psychology, Middle East Tu¨rker O Technical University, Ankara, Turkey

Martha Hı´jar Center of Research in Population Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico

Karl Peltzer Human Sciences Research Council, Pretoria, South Africa, and University of the Free State, Bloemfontein, South Africa

ix

x

List of Contributors

Miguel Perez Center for Automotive Safety Research, Virginia Tech Transportation Institute, Blacksburg, VA, USA Ricardo Pe´rez-Nu´n˜ez Center for Health Systems Research, National Institute of Public Health, Cuernavaca, Morelos, Mexico

David A. Sleet Centers for Disease Control and Prevention, Atlanta, GA, USA Paula Smith Health Sciences Division, Pasadena City College, Pasadena, CA, USA Stephen G. Stradling Transport Research Institute, Edinburgh Napier University, Edinburgh, UK

Bryan E. Porter Department of Psychology, Old Dominion University, Norfolk, VA, USA

Joanne E. Taylor School of Psychology, Massey University, Palmerston North, New Zealand

Michael A. Regan French Institute of Science and Technology for Transport, Development and Networks, Lyon, France

Geoffrey Underwood Nottingham, UK

Richard Retting Sam Schwartz Engineering, Arlington, VA, USA

University

of

Nottingham,

Ron Van Houten Department of Psychology, Western Michigan University, Kalamazoo, MI, USA

Tova Rosenbloom Phoenix Road Safety Studies and Department of Management, Bar-Ilan University, Ramat Gan, Israel

Jonathon M. Vivoda Michigan Center for Advancing Safe Transportation throughout the Lifespan and University of Michigan Transportation Research Institute, Ann Arbor, MI, USA

Cynthia Shier Sabo Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA

Ian Walker Department of Psychology, University of Bath, Bath, UK

Maria T. Schultheis Department of Psychology, Drexel University, Philadelphia, PA, USA David Shinar Sheva, Israel

Ben-Gurion University of the Negev, Beer

Kelli England Will Department of Pediatrics, Eastern Virginia Medical School, Norfolk, VA, USA Flaura Koplin Winston Center for Injury Research and Prevention, The Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA

Full author biographies available online on ScienceDirectÒ , www.sciencedirect.com

Part I

Theories, Concepts, and Methods 1. How Many E’s in Road Safety? 2. Driver Control Theory: From Task Difficulty Homeostasis to Risk Allostasis 3. Case–Control Studies in Traffic Psychology 4. Self-Report Instruments and Methods 5. Naturalistic Observational Field Techniques for Traffic Psychology Research

3 13 27 43 61

6. Naturalistic Driving Studies and Data Coding and Analysis Techniques 7. Driving Simulators as Research Tools in Traffic Psychology 8. Crash Data Sets and Analysis

73 87 97

This page intentionally left blank

Chapter 1

How Many E’s in Road Safety? John A. Groeger University College Cork, Cork, Ireland

1. INTRODUCTION I was introduced to driver behavior research by Ivan Brown, with whom I went to work at the Medical Research Council’s Applied Psychology Unit in 1985. Ivan’s knowledge of the field was voluminous, and his proselytizing on behalf of a psychological dimension to road safety was both tireless and remarkably successful in shaping decades of research in the area in the United Kingdom. If Ivan shaped the UK agenda, Talib Rothengatter (d. 2009), with whom I first began to collaborate in 1987 as part of the remarkably foresighted European Union-funded GIDS project (Michon, 1993), gave form and substance to the behavioral aspects of traffic psychology throughout Europe and beyond. Both would have written this overview chapter far better than I can hope to do. Although I was, and remain, more interested in the cognitive underpinnings of complex skilled activity, road safety was much more central to Ivan’s concerns. He was the first person I encountered who invoked the “three E’s” mantra of road safety. It is only recently that I found reference to what is, I believe, the original coining of the phrase “education, enforcement, engineering.” According to Damon (1958), Julien H. Harvey, who was then director of the Kansas City Safety Council, gave a presentation in Topeka in 1923 during which he presented a drawing of a triangle with sides labeled “Education,” “Enforcement,” and “Engineering.” Since then, the three E’s have dominated perspectives on road safety, with occasional forays into the literature by safety experts advocating increasing the number of E’s in road safety. I, too, am going to travel this path in an attempt to overview what I consider some of the most important contributions to the literature in recent years.

2. EDUCATION One of the virtues of the three E’s is the succinct summary they offer of what remain the primary parameters of safety. However, in each case, drawing the remit of each “E” narrowly limits not only the scope but also the extent of the Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10001-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

potential to contribute to safety. This is demonstrably so with respect to “education.” Education has come to mean the transmission of an established body of knowledge and skills to those who lack these. In the road safety context, it has less to do with the developing of individual potential, implicated in wider use of the term “education,” and typically refers to “driver education” and “public education.” Driver education is a term used more widely in North America to cover the preparation of intending drivers for independent driving. It comprises, depending on the jurisdiction, classroom or electronic dissemination of the declarative knowledge base on which driving relies, as well as what is typically referred to as “driver training” (i.e., practical instruction on the operations the driver is required to perform when driving, including the rules that pertain to vehicle operation (Lonero, 2008)). Despite the evident face validity of driver education, the evidence of a direct safety benefit from driver education is scant and equivocal, as a succession of reviews during the past few decades have shown (Brown, Groeger, & Biehl, 1987; Christie, 2001; Ker et al., 2005; Mayhew & Simpson, 2002; Roberts & Kwan, 2001). Evidence with regard to the effectiveness of the skill and declarative knowledge components of driver education is, to some extent, more compelling. For example, there is very good evidence that the driving performance of drivers improves as they gain behind-the-wheel experience with professional driving instructors or accompanying adults (Groeger, 2000; Groeger & Clegg, 2007; Hall & West, 1996). However, there is surprisingly little evidence that the classroom or individual education leads to an increase in knowledge about, and attitudes toward, driving. One study showed that those who were pseudo-randomly assigned to classroom or individual CD-ROM- or Internet-supported study performed similarly on a post-course test of drivingrelated knowledge (Masten & Chapman, 2004). Unfortunately, the study did not include a pre-course assessment of driving knowledge, and thus the comparability of groups before undertaking courses and the relative improvement in knowledge of driving by virtue of course participation are 3

4

unclear. This suggests that the classroom setting per se does not lead to better outcomes than home study, although the educational value overall is difficult to ascertain. Some studies, which are considered later in relation to exposure, are more encouraging with regard to the contribution of driver education to safety. Mass media campaigns are also a means by which education might make a contribution to road safety. In discussing their effectiveness, I separate campaigns that seek to change behavior by emphasizing that the unwanted behavior is antisocial, or where there are safety-related consequences of some unwanted behavior, from campaigns that implicate enforcement. Two related meta-analyses of the effects of carefully conducted, substantial, wellcontrolled media campaigns on alcohol-related accidents (e.g., single-vehicle nighttime crashes) or blood alcohol content levels reveal impressively large reductions in alcohol-involved driving of approximating 13% (Elder et al., 2004; Tay, 2005a). Although impressive, the fact that no more than approximately a dozen studies, worldwide, over several decades met the rigorous standards for inclusion in these meta-analyses is very revealing of the dearth of peer-reviewed studies that demonstrate convincing reductions on relevant outcome measures. Differences between the effectiveness of campaigns against speeding or drunk driving (Tay, 2005b) both show the inherent complexity of evaluating public education campaigns and emphasize the very important point that even carefully constructed and targeted campaigns may not be equally effective as a means of reducing all unsafe/ illegal behaviors, regardless of what these are. Tay’s study also serves to emphasize the importance of message content, in that different types of unsafe/illegal behaviors may not equally support “response efficacy” (i.e., provide useful and effective avoidance strategies). The importance of this and other aspects of message content, delivery, pretesting, as well as audience effects and target offenses, has been more formally investigated in a number of other studies. These experimental studies typically use behavioral intentions, rather than measured change in specified actual behaviors, as outcome measures, but they have allowed investigation of the subtle interplay between the threat implied in campaign messages and consequent fear induced and the likely acceptance or rejection of the message among various groups (Cauberghe, De Pelsmacker, Janssens, & Dens, 2009; Lewis, Watson, & Tay, 2007; Lewis, Watson, & White, 2008, 2010). These and other studies have considerable potential to shape message content and delivery, and they provide a coherent account of how and why messages may have the potential to be effective. However, quantification of the actual safety benefits of these and other variables will be a considerable challenge, just as it has been for driver training.

PART | I

Theories, Concepts, and Methods

It would be remiss not to acknowledge a final sense in which education can make a contribution to road safety. Many who are engaged in this area have benefited from, and seek to pass on, the expertise and experience of others. As such, those of us in educational roles have the ultimate responsibility for maintaining and enhancing the knowledge base of current theory, methods, and research findings available to policymakers, other safety professionals, and society at large.

3. ENFORCEMENT Few studies demonstrate the centrality of enforcement to road safety as well as that by Tay (2005b), in which it was shown that the number of breath tests performed per month and the percentage of drivers arrested were associated with a statistically significant reduction in the number of serious crashes per month. Enforcement is likely to be by far the most important determinant of the likelihood of apprehension for a criminal act, and as such it is critical to deterrence. Thus, for example, classical deterrence theory proposed that criminal acts are less likely to be committed where the certainty of punishment is high and the punishment is both severe and swift (Taxman & Piquero, 1998). Classical deterrence theory emphasizes the importance of direct punishment of the individual. In doing so, classical deterrence theory neglects the potential to increase further offending of offenders’ experience of avoiding punishment, as well as their more vicarious experience of both punishment and punishment avoidance (Stafford & Warr, 1993). Piquero and Paternoster (1998) provide empirical support for this reconceptualization of classical deterrence theory, showing that expressed intentions to drink and drive were affected by personal and vicarious experiences as well as by both punishment and punishment avoidance. Furthermore, very strong deterrent effects were observed where the certainty of punishment for the respondent was high. Interestingly, Piquero and Paternoster also show that “moral beliefs that prohibit drunk driving are an effective source of inhibition” (p. 3), and impulsivity among individuals is associated with whether vicarious experience of punishment and punishment avoidance influences offending (Piquero & Pogarsky, 2002). Watling, Palk, Freeman, and Davey (2010) attempted to extend this analysis to “drug” rather than “drunk” driving. They showed that punishment avoidance and vicarious punishment avoidance were predictors of the propensity to drug drive in the future but note that knowing of others apprehended for drug driving was not a sufficient deterrent. It may be that particular types or patterns of drug use are more prevalent among individuals high in impulsivity, and if so, increased vicarious knowledge of punishment through publicity or media reports of detection might not be effective for specific driving-under-influence

Chapter | 1

How Many E’s in Road Safety?

offenses (in addition to the difficulty of using media appropriate for such offenders). In part because of the influence of vicarious knowledge of detection and punishment on deterrence, publicity campaigns enhance the effect of rigorous enforcement. Miller, Blewden, and Zhang (2004), during the introduction of a zero alcohol tolerance regime for drivers younger than age 20 years, investigated the effects of compulsory roadside breath testing (CBT), CBT twinned with a media campaign, and a subsequent period of greater police presence during CBT (i.e., “booze buses”). They reported a reduction in expected nighttime crashes of 22.1%, with a further reduction of 13.9% due to enhanced media, and that booze buses yielded a further 27.4% reduction where implemented. This almost halving of expected nighttime accidents persisted for several years beyond the life of the intervention. Whereas Miller and colleagues (2004) showed that increasing the apparent seriousness with which offenses are treated by enforcement agencies serves to increase deterrence, diminishing the seriousness of offenses appears to have the opposite effect and has a more general effect on safety. McCarthy (1993) reported that an increase in rural interstate speed limits significantly increased overall the incidence of alcohol-related accidents, and that alcoholrelated accidents became more prevalent in lower speed environments. Blais and Dupont (2005) emphasized the pervasiveness of safety effects resulting from strict police enforcement. Reviewing the international literature on enforcement programs focused on a broad range of offenses (including random breath testing, sobriety checkpoints, random road watch, photo radar, mixed programs, and red light cameras), they concluded that interventions resulted in an average decrease, ranging between 23 and 31%, of injury accidents. On the other hand, the consequences of lax enforcement are demonstrated in a case-crossover study of traffic law enforcement and risk of death from motor vehicle crashes (Redelmeier, Tibshirani, & Evans, 2003). These authors showed that there was a protective effect from recent convictions on individual drivers, such that the risk of a fatal crash in the month after a conviction was approximately 35% lower than that in a comparable month with no conviction for the same driver, and that this protective effect declined rapidly a few months after conviction. This protective benefit was consistent across ages, incidence of previous convictions, and other personal characteristics, and it was greater for speeding violations with penalty points than for speeding violations without points. The authors concluded that enforcement “effectively reduces the frequency of fatal motor vehicle crashes in countries with high rates of motor vehicle use. Inconsistent enforcement, therefore, may contribute to thousands of deaths each year worldwide” (p. 2177). As technologies have developed, the opportunities for enforcement other than by traditional policing have also

5

increased with automated detection of speeding offenses, red light violations, etc. In addition to the roadside reminders regarding potential violations, and the increase in implied and actual surveillance, the increased likelihood of punishment, and reduced opportunity for punishment avoidance, automated detection systems greatly enhance the potential for deterrence. There is substantial evidence that speed cameras not only reduce speeding but also reduce collisions and speed-related collisions (Pilkington & Kinra, 2005), with a meta-analysis suggesting that “injury crash reductions in the range of 20 to 25% appear to be a reasonable estimate of site-specific safety benefit from conspicuous, fixed-camera, automated speed enforcement programs” (Thomas, Srinivasan, Decina, & Staplin, 2008, p. 117). Retting, Ferguson, and Hakkert (2003) reported, on the basis of a meta-analysis of international studies of red light camera effectiveness, that injury crashes overall were substantially reduced at signalized intersections, particularly right-angle injury crashes, although the incidence of rear-end collisions increased. Other studies suggest that although violations are reduced, the overall safety benefit of red light cameras is at least questionable (Erke, 2009; Wahl et al., 2010). One of the difficulties for automated enforcement is that some who drive are not licensed to do so. A number of attempts have been made to quantify the number of unlicensed drivers in the United Kingdom during the past decade, and two very different approaches to tackling this difficult problem have yielded remarkably similar estimates. In a survey-based approach, samples of those holding provisional licenses (in the United Kingdom, such drivers must not drive without being accompanied by a qualified driver) were written to and asked, anonymously, whether or not they had driven while unlicensed and the extent of that driving (Knox, Turner, Silcock, Beuret, & Metha, 2003). The proportion of drivers admitting to driving illegally was then weighted by the number of drivers known to hold provisional licenses or who were disqualified by the courts. This approach yielded an estimate of approximately 476,300 unlicensed drivers. On March 31, 2006, the UK police randomly stopped 5793 vehicles and checked whether the drivers held current driving licenses, finding that 1.6% did not. Extrapolated to the UK driving population of approximately 31 million, this gives an estimate of approximately 480,000 unlicensed drivers. Various methodological weaknesses underlie these studies. The survey evidence is based on understandably low response rates (10e20%), despite the anonymity of responding, and gives no indication of the number of those who may be driving who have never held a license. The police random survey, by using the number of licensed drivers to estimate the total number of unlicensed drivers, obviously leads to underestimation. In addition to the problem that unlicensed drivers pose for automated

6

enforcement, there is evidence that unlicensed drivers are between three and nine times more likely to be involved in accidents that result in injury or death (Knox et al., 2003). These risk ratios for the general population are similar to those reported for a Californian sample (DeYoung, Peck, & Helander, 1997), although they are likely to be higher for particular groups of drivers (Blows et al., 2005). Just as studies of more traditional police-based enforcement demonstrate an enhancing effect of education, in the form of concurrent mass media campaigns, these studies of the effects of automated enforcement emphasize an increasing interaction between the effects of enforcement and engineering. This third element of Harvey’s three E’s safety mantra is considered next.

4. ENGINEERING Traditionally, safety benefits from engineering would have been anticipated from improvements to vehicle build quality, reliability, improved braking performance, and the protection offered to vehicle occupants. Others arise from improvements to road design, surface quality, reduced deterioration during adverse conditions (Elvik & Greibe, 2005), safer roadside furniture (Elvik, 1995), less confusing signage, etc. During the past decade, impressive additional safety benefits have accrued from the improvements to occupant protection. Studies of the safety benefits of child safety seats and booster seats for older/heavier children have shown that compared with restraint by seat belts alone, restraint by belts positioned more correctly by the concomitant use of boosters resulted in 59% fewer injuries to children aged 4e7 years who were involved in motor vehicle crashes (Durbin, Elliott, & Winston, 2003), whereas the U.S. National Highway Traffic Safety Administration (NHTSA) reported that child safety seats are 71% effective in reducing fatalities among infants and 54% effective among toddlers (NHTSA, 2009a, 2009b). Safety has also increased because of a reduction in the number of younger children traveling in the front seats of vehicles (Durbin, Elliot, Arbogast, Anderko, & Winston, 2005); children in rear seats are 50e66% less likely to suffer injury (Arbogast, Kallan, & Durbin, 2009). Air bags, although potentially problematic for children (Durbin, Kallan, et al., 2003), have been shown to reduce fatalities in frontal crashes by 14% when no seat belt is used and by 11% when used in conjunction with a seat belt (Braver, Ferguson, Greene, & Lund, 1997; NHTSA, 2009a, 2009b). Seat belt use is also associated with substantial reductions in mortality following head-on collisions (Crandall, Olson, & Sklar, 2001; Evans, 1986). However, although the reductions are robust, it should be acknowledged that both seat belt use and air bag deployment are associated with what are largely less serious specific injuries that would

PART | I

Theories, Concepts, and Methods

otherwise have been unlikely to occur (Hutt & Wallis, 2004; Smith & Hall, 2005). These enhancements to occupant protection have wellestablished safety benefits and have few, if any, direct implication for the way the driving task is carried out. In contrast, developments in in-vehicle and roadside telematics may have profound consequences for how the driving task is carried out and, in some cases, even what the driving task is. Even the most developed systems are still some way from having real market penetration, including systems that are commercially available, and the majority of the systems envisaged are barely at the stage at which live trials in real traffic are possible. Whereas the vehicle modifications presented previously have clear safety benefits, those that will arise from the advance of telematics rely largely on experts’ expectations (Kulmala, 2010). Nevertheless, the anticipated safety benefits, assuming systems are fitted in all European Union vehicles, are impressive. Those with the highest fatality reduction potential are electronic stability control (fatality reduction of approximately 17%), lane keeping support systems (~15% reduction in fatalities), and systems that warn the driver when the speed limit is exceeded and when locations with higher incidences of accidents are being approached (~13%). Other systems considered might induce speed adaptation depending on weather conditions, obstacles, or congestion; warn drivers of imminent collisions and apply brakes if necessary; advise drivers regarding accidents, obstructions, and poor visibility or road conditions in the locale; warn of upcoming red light obligations; or assist the driver at nighttime or during other poor visibility conditions by warning of obstructions beyond the range of headlights. Assessing the safety impacts of future developments is a profound methodological and theoretical challenge that relies on assumptions about how people will adapt to what might be very dramatic differences in what is required of them as “drivers,” the road environments in which they will drive, and the likelihood of other vehicles they may interact with having similar technological enhancements. Although I do not doubt that reliable relative assessments of potential safety benefits can be made, the treating of these estimates as likely to result in absolute casualty reductions is premature, at best. As I have attempted to show, Harvey’s three E’s have had, and continue to have, considerable relevance for how we conceptualize potential contributors to road safety. In my view, however, other E’s, as reviewed in the following sections, also merit consideration.

5. EXPOSURE Although it is inherent in the concept of risk that negative outcomes are weighted in some way by some index of the possible outcomes, road safety statistics typically consider

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injury and fatality as head counts, or head counts weighted by the size of the population, numbers of vehicles, or per unit of distance driven, in order to take into account at least to some extent what the relative opportunities are for collisions to occur. This more detailed consideration of such accident data allows us to identify, for example, that young, inexperienced drivers, relative to other motorists, have more of their accidents during weekends, at night or the early morning, or the accidents are more likely to take place when young drivers are accompanied by several similarly aged passengers rather than when traveling alone or when traveling with one other. Discovering particular patterns of “proneness” can be critical to understanding why such events take place, as well as for designing potential countermeasures. Although in principle this proposition might justify including exposure as an additional E in safety, two examples may make its case particularly compelling. I have been interested in the links between genetics, cognition, and sleep for many years, but it was only when re-reading some literature on young drivers that a particulardas yet untesteddhypothesis regarding the overinvolvement of young drivers in accidents occurred to me (Groeger, 2006). A variation on a particular gene (period 3) is associated with people’s self-declared preferences for being active in mornings or evenings. When sleep deprived, people with these particular genetic variations are especially poor at performing tasks in the early morning (Groeger et al., 2008). During the school/work week, teenagers report obtaining far less sleep than they would wish (Groeger, Zijlstra, & Dijk, 2004); thus, I hypothesized that teenagers with a particular configuration of the period 3 gene may actually be more likely to have accidents in the early morning when they had not slept earlier that night (Groeger, 2006). Epidemiological evidence considered in Groeger (2006) showed that when the numbers of reported trips made by young drivers at particular times of the day are taken into account, teenage drivers are far more likely to be involved in road traffic accidents in the early hours of the morning than are drivers in their early twenties. Without the perspicacity of Sweeney, Giesbrecht, and Bose (2004), who reported both accident and trip frequencies by age and time of day, this very detailed and specific prediction would not have occurred to me. If research currently under way in our laboratory provides support for this hypothesis, we may well have a new and quite distinct way of accounting for at least some of the higher crash involvement of inexperienced drivers. The notion that there are particular patterns of exposure associated with young, inexperienced drivers’ accidents is a major part of the rationale underlying graduated driver licensing (GDL) interventions. Although there are a wide variety of GDLs in operation throughout the world, the underlying principles are the same: Higher risk activities,

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such as driving at night, with alcohol, and with teenage passengers, are delayed until the driver is older. Evidence that GDL is associated with a substantial casualty reduction has been accumulating during approximately the past decade. Although some have suggested that the effects may arise partly through reduced exposure as a result of decreased or delayed licensing, Masten and Foss’s (2010) survival analysis demonstrated that 16-year-old drivers experienced lower first-crash incidence during the first 5 years of unsupervised driving than did those licensed under the previous system, with greater benefits for young male drivers. A meta-analysis of the effects of GDL systems goes further and helps to identify which components of GDL are associated with accident reductions (Vanlaar et al., 2009). In the learner stage, these components include the length of night restriction and driver education. In the intermediate stage, the influential components include driver education, whether night restrictions are lifted (for work purposes), passenger restrictions and whether these are lifted if passengers are family members, and, importantly, whether there is an exit test. Two of these effective components are particularly worthy of comment. First, it is noteworthy that the inclusion of a test prior to reducing restrictions is associated with greater reductions in accident risk. Because youthfulness and inexperience both contribute to overinvolvement in accidents, an intervention that reduces risk exposure solely on the basis of age, as I have speculated elsewhere, will be less effective in reducing accidents (Groeger & Banks, 2007). Second, the meta-analysis by Vanlaar and colleagues presents far more convincing evidence for the safety benefits of driver education than has hitherto been found, and it is striking that these emerge when some of the huge variability in exposure of young, inexperienced drivers is controlled. Although not featured in the studies considered previously, the role of parentally imposed restrictions on driving, through parenteteen agreements, etc., is also likely to contribute substantially to increased safety arising from graduated licensing (SimonsMorton, Ouimet, & Catalano, 2008).

6. EXAMINATION OF COMPETENCE AND FITNESS Perhaps because of the political and societal issues that testing raises, the potential road safety contribution of driver assessment has been largely ignored. Most discussions of driver education and testing readily concede that the competency standards drivers must demonstrate to gain a driver’s license are, at best, rudimentary (Baughan, Sexton, Maycock, Chin, & Quimby, 2005; Lonero, 2008; Mayhew, 2007). Increasing the requirement to demonstrate knowledge of the more theoretical aspects of driving seems unlikely to increase driving ability. In the United Kingdom,

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for example, the pass rate for the on-road driving test changed little after the introduction of extensive theory testing (Wells, Tong, Sexton, Grayson, & Jones, 2008). Wells et al. performed a thorough analysis of the relationship between demonstrating theoretical knowledge of driving and practical driving competence, which controlled for respondent age as well as hours of tuition and practice. The researchers found that no effect of passing a hazard perception theory test was observed among male or female respondents, even when the analysis was conducted separately for those taking their first, second, third, fourth, etc. driving test. Because it does not appear to improve driving ability, theory testing seems unlikely to have a safety benefit, other than by slightly delaying licensing. Another study, also conducted in the United Kingdom, raises serious questions about the reliability of driving test outcomes. Baughan and Simpson (1999) asked candidates taking the practical on-road driving test to voluntarily undergo a second test within days of their first test. The passefail designation was the same for 64% of those re-tested, suggesting that substantial numbers of drivers or examiners were unable to perform their respective roles consistently. These findings suggest that there is considerable scope for improving the reliability and validity of the practical driving test. Repeated, rather than one-off, testing and the introduction of more objective, electronic measurement of driver performance would not only identify those drivers capable of performing more consistently but also probably serve to delay licensingdwith a consequent increase in safety. Furthermore, as previously mentioned, incorporating testing within a GDL regime has been shown to increase safety; thus, requiring that drivers are competent to cope with the demands of the circumstances in which they will drive when restrictions are reduced has much to recommend it. In my view, improving our ability to examine drivers’ competence is perhaps the least wellexplored opportunity for enhancing road safety. Arguably, our rather unsophisticated approach to assessing driver competence has made it particularly difficult to develop rigorous, reliable, and especially valid assessments of fitness to continue driving among those who, through age or infirmity, are believed to pose a greater safety risk. Epidemiological studies, based on different data sets, indicate that older drivers with heart disease or stroke are more likely to be involved in at-fault traffic accidents (McGwin, Sims, Pulley, & Roseman, 2000). Sagberg (2006), using a different methodology, showed that similar risk factors pertain in Europe, adding that nonmedicated diabetes and depression are also associated with greater crash involvement. Although many jurisdictions require that drivers with such conditions notify the licensing authority and withdraw from driving, evidence suggests that compliance with this requirement is low. McCarron, Loftus, and McCarron (2008) reported that among

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consecutive hospital referrals, approximately 40% of drivers who suffered a heart attack or minor stroke were driving 1 month after the event. There is thus good evidence to support the suggestion that certain medical conditions are associated with increased involvement in traffic accidents, and that voluntary compliance with advice to cease driving is not adhered to. Given the many factors that are associated with lower crash risk, such as not being young, having driving experience, reduced exposure in terms of distance driven, and the timing and circumstances of trips, simply denying those who suffer from heart disease, untreated diabetes, stroke, etc., does not seem justified or fair. Regrettably, our ability to assess the driving skills and competences associated with greater safety is, at best, limited. A variety of approaches to this admittedly difficult issue have been adopted, and the relative successes of these approaches have been reported (Hunter et al, 2009; Schultheis, DeLuca, & Chute, 2009). Identifying those who pose greater threats to safety than is acceptable, however, is only one part of this complex, and often tragic, problem. Denying people the right to independent mobility, which the motor car has conferred so lavishly on so many of us, cannot be the end of our involvement with this issue as traffic professionals. Involvement might take several forms: counseling or supporting the former driver and family members in order to enable all concerned to cope with the decision, increasing access to other forms of transport, or developing remedial programs that might offer a reasonable possibility for former drivers to return to driving. The scientific challenge of the latter is substantial due to the limited theoretical understanding and practical progress with regard to improving driver education and training and driver assessment.

7. EMERGENCY RESPONSE For decades, the widely accepted view among medical professionals was that the prognosis for a seriously injured individual was in part determined by whether the patient reached the hospital within 1 h of the trauma onset. A search for historical sources and empirical support for this notion of a “golden hour” proved fruitless (Brooke Lerner & Moscati, 2001; Newgard et al., 2010). Despite this, there is ample evidence that in the case of life-threatening injuries, delaying treatment until the patient reaches a trauma center increases the likelihood of death (Hoffman, 1976). Studies show that compared to patients treated in the field or first hospital destination, patients whose first treatment is delayed until the trauma center is reached are 3.3 times more likely to die (Gomes et al., 2010). Sampalis, Lavoie, Williams, Mulder, and Kalina (1993) reported that a total prehospital time of more than 60 min was associated with a statistically significant adjusted relative odds of

Chapter | 1

How Many E’s in Road Safety?

dying (OR ¼ 3.0). Based on an analysis of a sample of more than 1400 traffic accidents on motorways and other roads, Sa´nchez-Mangas, Garcı´a-Ferrrer, De Juan, and Arroyo (2010) reported that a 10-min reduction in the typical medical response time of approximately 1 h was associated with an approximately one-third decrease in mortality probability. The circumstances in which certain types of accidents occur, such as in isolated rural areas or at times of the day or night when the crash site is less likely to be encountered by others who might contact emergency services, can leave those involved at greater risk of death. This is particularly the case for accidents involving younger drivers. Work carried out as part of the development of telematic systems estimated that an automated accident warning system linked to emergency services would reduce motor vehicle fatalities in Finland by 5e10%. In 95% of these cases, the consequence would be injuries requiring further hospital and other treatment, and in the remaining 5% the consequence would be injuries requiring no further treatment at all (Sihvola, Luoma, Schirokoff, Karkola, & Salo, 2009). Until such systems are widely available, and even when they are, in the case of severely injured road users, the rubric of (1) getting to the patient quickly, (2) treating what can be treated on site, and (3) getting the patient to an appropriate trauma treatment center as quickly as possible will remain key to ensuring that severe injuries do not unnecessarily result in death.

8. EVALUATION The seventh, and perhaps most important, “E” in road safety is evaluation. Throughout the previous discussion of the contributions to safety by education, engineering, enforcement, exposure, emergency response, and examining competence and fitness, I illustrated a range of methods and techniques that are critical to making informed, unbiased assessments of the effectiveness of safety interventions. In doing so, I glossed over very substantial challenges of the methods that road safety researchers have used to evaluate the effectiveness of countermeasures during the past century. Because safety is the desired outcome of interventions in this area, the “gold standard” for those seeking to evaluate effectiveness is casualty reduction, particularly fatalities. Counting crashes and relating these to some “explanatory” variables is the basis of much evaluation in road safety. These explanatory variables may reflect some purposeful “treatment” (e.g., increased penalties, mandatory driver education, and seat belt use) or some change consequent on other developments in society (e.g., economic activity, migration, and fuel shortages). The statistical challenges arising from crash- or casualtyfrequency data are very lucidly described by Lord and

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Mannering (2010). Because crashes are intrinsically rare events, Poisson distributions are generally assumed. As Lord and Mannering note, the Poisson approach requires that the mean crash frequency and variance in crash frequency will be equal, but these dispersion requirements are frequently violated, resulting in biased efficacy estimates. Such count-data methods require that counts are made with respect to some temporal or spatial context. However, information on variations in the explanatory variables of interest is rarely available at the level of detail required to adequately assess their explanatory power. Lord and Mannering use the example of attempting to model the effects of precipitation on monthly crash data, when in reality it is the distribution of precipitation in far smaller time units that is likely to influence whether or not crashes occur. Temporal and spatial contexts may also be intercorrelated, leading to inaccurate assessments of the explanatory power of other variables. Other problems addressed include the difficulties that arise from correlations between injury severity and crash type, underreporting of certain types of crashes, low mean and sample sizes, omission of other potential explanatory variables, and what Lord and Mannering refer to as “endogenous” variables, where the explanatory variable changes as a function of the dependent variable (e.g., evaluating the effectiveness of ice warning signs in preventing ice-related crashes, where ice warning signs are more likely to be placed at locations at which such accidents have occurred). These problems are intrinsic to the dependent variable in which we have most interest and on which we seek to exert a causal influence. Detecting and inferring causality is, to say the least, problematic. An insightful paper by Ezra Hauer (2010) considers “causality” in two other approaches to evaluation in road safety: cross-sectional and beforeeafter studies. He demonstrates the difficulty, perhaps even the impossibility, of inferring causality in both cases. Hauer’s case is that cross-sectional regression studies, in which crash frequencies are contrasted across sites with different types of intervention, rarely capture, and then poorly account for, what Lord and Mannering (2010) might refer to as the spatial and temporal context and also other circumstantial aspects of individual interventions. By doing so, crosssectional studies can neither negate nor corroborate each other’s findings. Beforeeafter studies, although more closely having the experimental control Hauer advocates as essential for determining causation, yield outcomes in which the efficacy of treatments depends on the particular circumstances of the treatment sites. Thus, the safety effects of replacing a stop sign with a traffic signal will depend on the amount and nature of traffic using the intersection, the number of lanes over which control is sought, the proximity of treated intersections to untreated ones, their relative conspicuity, and a host of other factors.

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Without a very large number of treated sites, for which the characteristics of each are carefully recorded, it is impossible to adequately estimate the extent to which any effect observed depends on particular circumstances in which the treatment is realized. Hauer implies that without being able to do so, predicting the effect an intervention will have in a given circumstance is impossible (see also Hauer, 1997). Hauer (2010) makes a further point that is sometimes neglected when road safety researchers engage in evaluation: Theory in road safety is weak but indispensable. In addition to citing a discussion between the eminent statisticians William G. Cochran and Sir Ronald Fisher in which the latter argued that elaborate theories, in which as many consequences of causal hypotheses are envisioned as possible, were among “the most potent weapons in observational studies” (Cochran, 1965, p. 252), Hauer asserts that “to do applied research without providing a theory is like attempting to build the roof of a house with no foundation” (p. 1130). Hauer’s comment testifies to what is in my view the essential step we must make to move from mere description to predictiondthe building and testing of theory. Fisher’s comment regarding the required elaborateness of theory, and the need to envision as many consequences of a theory as possible, is particularly apposite for road safety. As discussed previously, there are many difficulties in using crash or casualty counts, but alternatives are available to our research generation as to no other. The increasing sophistication of onboard vehicle and roadside monitoring systems, largely developed for nonsafety purposes, affords a broader range of dependent variables for assessing performance than ever before. Such nonintrusively collected data are almost routinely available, potentially from truly representative samples of drivers, and genuinely reflective of what these drivers actually dodnot just those whose misfortune it is to crash. Shankar, Jovanis, Aguerde, and Gross (2008) offer a very helpful primer on the use of such naturalistic data in road safety settings. Theories that elaborate the link between these variables downstream to safety and upstream to models of intended and unintended driver behavior can meet the requirements of Fisher’s dictum and offer the possibility of more cost-effective, reliable, and extensible evaluation. Although I have been more critical of this final “E” than of any other, it is probably the most important contributor to future safety. Without evaluation, there would be no measurable contribution to safety of any intervention, no opportunity for researchers to test their predictions concerning how and why certain interventions may be effective, no opportunity for policymakers to implement those interventions most likely to prove effective, and no opportunity for road safety experts to optimize their implementation.

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9. CONCLUSION In this chapter, I considered Harvey’s three E’s and their contribution to road safety, and I discussed how they remain a very useful way of considering safety interventions, despite the fact that almost a century has passed since they were first proposed. I also identified other potential contributors to safety: exposure, emergency response, examining for competence, and evaluation. I doubt that these additional E’s will endure as long as those Harvey identified, nor would I encourage others along this acrostic path. The purpose of exploring this highly influential mnemonic was to acknowledge both the contribution its elements have made and their continuing relevance. Each element, and indeed the offspring of each element, is more complex than the original formulation acknowledges. This methodological and theoretical complexity arises largely from the increasing inter- and intradisciplinarity on which our field, and ultimately “safety,” relies.

ACKNOWLEDGMENTS This work was supported by the Science Foundation Ireland (09/RFP/ NES2520) and Ireland’s Road Safety Authority. A version of this chapter was presented at the Sultanate of Oman’s Traffic Safety Summit.

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Sa´nchez-Mangas, R., Garcı´a-Ferrrer, A., De Juan, A., & Arroyo, A. M. (2010). The probability of death in road traffic accidents. How important is a quick medical response? Accident Analysis and Prevention, 42(4), 1048e1056. Schultheis, M. T., DeLuca, J., & Chute, D. L. (Eds.). (2009). Handbook for the assessment of driving capacity. San Diego: Academic Press. Shankar, V., Jovanis, P., Aguerde, J., & Gross, F. (2008). Analysis of naturalistic driving data: Prospective view on methodological paradigms. Transportation Research Record, 2061, 1e9. Sihvola, N., Luoma, J., Schirokoff, A., Karkola, K., & Salo, J. (2009). Indepth evaluation of the effects of an automatic emergency call system on road fatalities. European Transport Research Review, 1, 99e105. Simons-Morton, B. G., Ouimet, M. C., & Catalano, R. F. (2008). Parenting and the young driver problem. American Journal of Preventive Medicine, 35(3), 294e303. Smith, J. E., & Hall, M. J. (2005). Injuries caused by seatbelts. Trauma, 7(4), 211e215. Stafford, M. C., & Warr, M. (1993). A reconceptualization of general and specific deterrence. Journal of Research in Crime and Delinquency, 30, 123e135. Sweeney, M., Giesbrecht, L., & Bose, J. (2004). Using data from the 2001 National Household Travel Survey to evaluate accident risk. Paper presented at the 30th annual International Traffic Records Forum, Nashville, TN, July 25e27, 2004. Taxman, F. S., & Piquero, A. R. (1998). On preventing drunk driving recidivism: An examination of rehabilitation and punishment approaches. Journal of Criminal Justice, 26, 129e143. Tay, R. (2005a). General and specific deterrent effects of traffic enforcement. Journal of Transport Economics and Policy, 39(2), 209e223. Tay, R. (2005b). The effectiveness of enforcement and publicity campaigns on serious crashes involving young male drivers: Are drink driving and speeding similar? Accident Analysis and Prevention, 37, 922e929. Thomas, L. J., Srinivasan, R., Decina, L. E., & Staplin, L. (2008). Safety effects of automated speed enforcement programs: Critical review of international literature. Transportation Research Record, 2078, 117e126. Vanlaar, W., Mayhew, D., Marcoux, K., Wets, G., Brijs, T., & Shope, J. (2009). An evaluation of graduated driver licensing programs in North America using a meta-analytic approach. Accident Analysis and Prevention, 41(5), 1104e1111. Wahl, G. M., Islam, T., Gardner, B., Marr, A. B., Hunt, J. P., McSwain, N. E., Baker, C. C., & Duchesne, J. (2010). Red light cameras: Do they change driver behavior and reduce accidents? Journal of Trauma: Injury, Infection and Critical Care, 68(3), 515e518. Watling, C. N., Palk, G. R., Freeman, J. E., & Davey, J. D. (2010). Applying Stafford and Warr’s reconceptualization of deterrence theory to drug driving: Can it predict those likely to offend? Accident Analysis and Prevention, 42, 452e458. Wells, P., Tong, S., Sexton, B., Grayson, G., & Jones, E. (2008). Cohort II: A study of learner and new drivers: Volume 1dMain Report. (Road Safety Research Report No. 81). London: Department for Transport.

Chapter 2

Driver Control Theory From Task Difficulty Homeostasis to Risk Allostasis Ray Fuller Trinity College Dublin, Dublin, Ireland

1. INTRODUCTION Driving may be described as a control task in an unstable environment created by the driver’s motion with respect to a defined track and stationary and moving objects. The task includes requirements for route choice and following, coordination of maneuvers in support of navigational objectives, and ongoing adjustments of steering and speed (Allen, Lumenfeld, & Alexander, 1971). Figure 2.1 shows speed adjustments by a driver on a winding country lane, sampled at 5-s intervals. A fundamental issue in understanding driver behavior is the nature of the control process that produces such variations in speed. Control theory is predicated on the assumption that driver control actions are dependent on perceptual processes that select information that is compared to some standard or standards. Drivers act to keep resulting

40 Speed

Speed in mph

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discrepancies within acceptable limits in a negative feedback loop as the means of control in their goal-directed behavior (Figure 2.2). Ranney (1994), in his review of the evolution of models of driving behavior, makes a distinction between motivational and cognitive models and by implication includes control theory (e.g., Wilde’s risk homeostasis theory; Wilde,1982) within his motivational rubric. However, as can be inferred from the previous description, control theory encompasses both motivational (setting of standard) and cognitive (perceptual process) dimensions and is a characteristic not just of risk homeostasis theory (RHT) but also of zero-risk theory (Summala, 1986), Vaa’s (2007) “monitor model,” Summala’s (2007) “comfort zone model,” and the taskecapability interface (TCI) model (Fuller, 2000). It is with such models that we have seen the most evolution in recent years. All these models differ, however, in terms of their claims regarding what is the reference standard(s) in the control system. The principal aim of this chapter is to describe developments in how the TCI model conceptualizes these standards. It concludes by exploring whether, in the interests of theoretical parsimony, the different reference standards that have been proposed by Vaa and Summala can be assimilated into the developed TCI model.

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2. THE TASKeCAPABILITY INTERFACE MODEL

25

20

15 1

3

5

7 9 5-sec sample

11

13

FIGURE 2.1 Variation in speed on a country lane.

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10002-5 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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The TCI model is an attempt to understand what motivates driver decision making, with a particular emphasis on implications for performance safety. It starts from a recognition that driver perceptual processes and control actions both have rate limitations. Thus, the driver needs to continuously create and maintain conditions for driving within these limitations. That is, he or she must ensure that the demands of the driving task are within his or her capability (Figure 2.3). Loss of control occurs when, for 13

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Theories, Concepts, and Methods

FIGURE 2.2 Schematic description of a feedback loop. Source: Adapted from Carver and Scheier (1990).

Reference value Comparator

Input function (perception)

Output function (behavior)

Impact on environment

2.1. Driving Task Difficulty Lucky escape

Compensatory action by others

Capability (C)

Collision !

Loss of control

CD

Task demand (D)

Control

FIGURE 2.3 Starting point for the taskecapability interface model (2000): Limited capacity concept at the interface between driving task demand and driving task capability.

a multitude of possible reasons, drivers allow task demand to exceed their capability. It is the identification of these reasons that promises to shed light on how safety might be more reliably achieved as a concomitant outcome of our seemingly insatiable desire for greater mobility. From the perspective of the driver, the statistical probability of loss of control and collision (or road run-off) is not some potentially variable phenomenon and influence, as implied, for example, in RHT (Wilde, 1982). Once a driver begins to move his or her vehicle, the statistical probability of collision is essentially one. It is a certain outcome unless, of course, the driver continuously makes adjustments to avoid collision (or road run-off). For this reason, my original theoretical explorations of driver decision making focused on the concept of threat avoidance (Fuller, 1984a). However, that concept provides only a partial account, as has been discussed elsewhere (Fuller, 2005a; Michon, 1989).

The difficulty of the driving task is inversely related to the degree of separation between the demands of the task and the driver’s available capability. In principle, the greater that capability is, relative to task demand, the lower the difficulty of the task and vice versa. In general, the separation between demand and capability is equivalent to concepts such as spare capacity and safety margin (Figure 2.4). Where capability is more-or-less stable, changes in task demand will directly influence task difficulty. In this typical situation, task difficulty will be equivalent to workload and may in part be operationalized in terms of time-to-collision and time-to-line crossing, assuming resource demand (in terms of speed of information processing and response) to be inversely related to the time available (Wickens & Hollands, 2000). As task demand or workload increases, the margin of available capability to deal with additional demands decreases, and the driver becomes more vulnerable to the consequences of a performance error and to acute high demands such as in an emergency situation. Young, Higher Objective driver capability Task demand/ driver capability

Actual safety margin

Objective task demand

Lower

FIGURE 2.4 Driving task difficulty is inversely related to the degree of separation between driver capability and task demand.

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Driver Control Theory

Mahfoud, Walker, Jenkins, and Stanton (2008) demonstrated this phenomenon in a simulator study of the effects of eating and drinking on driving. They found that although these activities increased subjective ratings of physical workload, initially there was no effect on driving performance measures. However, when a pedestrian unexpectedly walked in front of them, there was a reduced ability to avoid collision. The authors concluded that although drivers may be able to cope with eating and drinking during normal driving, it is the response to a sudden peak in demand that is affected by the additional activity.

2.2. Driving Task Demand Driving task demand has both information input and response output characteristics, corresponding to the requirement to determine the situation ahead and the requirement to maneuver the vehicle appropriately. It arises out of a number of factors, including vehicle performance and information display characteristics, route choice, physical characteristics of the environment (e.g., visibility and road surface), and the presence and behavior of other road users. From a safety perspective, one can think of task demand in terms of the difficulty of information acquisition along dimensions of discriminability and flow rate and the number of potential conflicts for space in the driver’s trajectory. One can also think of it in terms of controllability associated with vehicle handling (but see Section 2.3), road surface quality, and the time available for decision making and response (which for any given situation decreases with increases in speed).

2.3. Driver Capability Driver capability arises from the driver’s basic physiological characteristics, education, training, and experience. These provide conditional rules for action as well as a realtime mental representation or simulation of the situation that enables top-down or feed-forward control decisions (see Section 2.4.3). This capability arms the driver with strategies for information acquisition and the capability of preadaptation to anticipated changes in task demand. It is ultimately expressed in speed and directional control of the vehicle. One could also include vehicle control functions that enhance the driver’s capability, such as antilock brake systems, electronic stability control, and global positioning system support for route and lane choice. Nevertheless, it makes little difference to the basic formulation: Such vehicle control supports can also be construed as reducing task demand (as in earlier formulations of the model) rather than as increasing capability. Fastenmeier and Gstalter (2007) have used task analysis to develop a typology of both road/traffic situations and driver behavioral requirements that they call SAFE

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(Situative Anforderungsanalyse von Fahraufgabendsituational analysis of behavioral requirements of driving tasks). A road/traffic situation is defined as “a bounded section from traffic reality that the driver experiences as a unit in time and space.” Behavioral requirements are a specification of relevant cognitive and psychomotor performances linked to successful negotiation of each situation. Fastenmeier and Gstalter (2007) support a distinction between a conscious information processing system, which is a sequential processor of limited capacity and speed and underpins reasoning and decision making, and a subconscious processor, which operates as a parallel, distributed system to perform a continuous, dynamic simulation of the environment and the individual’s position within it. This simulation provides the basis for a feed-forward control of the driver’s actions as well as a reference for detecting deviations from intended outcomes. The work of Fastenmeier and Gstalter (2007) provides important first steps in identifying at a micro and measurable level both the nature of driving task demands and the capabilities required of the driver to meet those demands. It is important to note, however, that capability is vulnerable in real time to a range of human factor variables, such as emotion and fatigue.

2.4. Task Difficulty Homeostasis The control theory concept at the center of the TCI model is the hypothesis of task difficulty homeostasis (Fuller, 2005a), the proposition that drivers continuously make real-time decisions to maintain the perceived difficulty of the driving task within certain boundaries, mainly (but not necessarily exclusively) by adjusting their speed (Figure 2.5). Thus, for example, increased task difficulty arising from snow and sleet and darkness additively reduces speed (Kilpela¨inen & Summala, 2007). High proportions of drivers state that they drive more slowly than usual when task difficulty increases, such as in fog (98%), heavy rain (96%), and on unfamiliar roads (88%) (Campbell & Stradling, 2003). Drivers also typically reduce speeds while negotiating intersections, but more so while simultaneously completing car phone tasks (Liu & Lee, 2005). They also choose to drive more slowly on a narrower version of the same road (Lewis-Evans & Charlton, 2006; Uzzell & Muckle, 2005). In Lewis-Evans and Charlton’s simulator study, ratings of difficulty and of subjective risk were higher for the narrower road, but drivers were not aware of the road feature that mediated these differences, suggesting that decision making was occurring at a preconscious level. Occasionally, speed is not the only variable that drivers can adjust in order to control the level of perceived task difficulty. For example, in a following situation, time headway may similarly be used. In a study of the effects of

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Theories, Concepts, and Methods

FIGURE 2.5 Task difficulty homeostasis.

Range of acceptable task difficulty

Comparator

Input function (perceived task difficulty)

Output function (speed and trajectory)

Impact on environment (task demand)

prolonged driving on truck drivers, it was found that as drivers’ ratings of drowsiness increased, so did their time headway (Fuller, 1984b). The corollary to this is that when task difficulty decreases, such as when roads are empty at nighttime, speeds increase (Broughton, 2005; Lam, 2003). Compensatory increases in speed have also been found by Larsen (1995), who measured the free speeds of drivers on different road segments in a 50 km/h zone. He observed an 11 km/h range from 49.2 to 60.2 km/h, with the highest mean speeds associated with what Larsen rated as the easiest driving conditions.

2.4.1. Calibration We need to modify the representation of the control process illustrated in Figure 2.5 to show that perceived task difficulty arises out of the interface between perceived task demand and perceived capability (Figure 2.6). Accuracy of driver perceptions is referred to as the driver’s calibration accuracy. Clearly, if drivers either underestimate task

FIGURE 2.6 Perceived task difficulty arises from perceived capability and perceived demand.

Range of acceptable task difficulty

Comparator

Output function (speed and trajectory)

Input function (perceived task difficulty)

Perceived capability

demand or overestimate their capability, the perceived level of task difficulty will be less than is objectively the case. Unfortunately, both of these conditions pertain to novice drivers in general (de Craen, 2010; Fuller, Bates, et al., 2008), and their poor calibration may explain in part the overrepresentation of this group in collision statistics. Harre´ and Sibley (2007) demonstrated that the disposition of young male drivers, in particular, to believe they are more capable than others occurs with both a traditional explicit measure of attitude and with a new implicit measure. In this latter, participants associated words indicating themselves with words indicating driving ability or driving caution more quickly than they associated words referring to other people with these same positive driving characteristics. The advantage of this implicit measure is that it avoids possible social desirability influences. Younger drivers also appear to be less well calibrated with regard to estimating the effects of distracting events on their performance or taking account of behavioral variables that may undermine capability. Horrey, Lesch, and Garabet (2008) asked younger and older drivers to complete

Perceived demand

Impact on environment (task demand)

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Driver Control Theory

a handheld or hands-free cell phone task while navigating a closed test track in an instrumented vehicle. Although drivers generally (correctly) rated their performance as poorer when distracted, across all driving measures subjective estimates were not related to the magnitude of the distraction effect. Some drivers who estimated the smallest effects actually exhibited the largest, and these were typically younger males. A review of young driver crashes (Organisation for Economic Co-operation and Development (OECD), 2006) concluded that the rate of inattention-related crashes and near crashes is four times higher for 18- to 20-year-old drivers than for those older than 34 years. Poorly calibrated drivers, who overestimate capability or underestimate task demand, will typically operate with less spare capacity and visit the boundary where task demand meets capability more frequently. Evidence for this in less experienced drivers derives from a study by Patten, ¨ stlund, Nilsson, and Svenson (2006) in which Kircher, O a secondary peripheral detection task in real driving was performed. Less experienced drivers had significantly longer reaction times to the peripheral stimuli and higher miss rates (although these results may have been confounded in part by familiarity with the route and sex of participant).

2.4.2. Evidence for Task Difficulty Homeostasis The concept of task difficulty homeostasis is not exclusive to the TCI model, and Summala (2007, p. 194) has argued a similar case. With reference to time-to-line crossing, he cites evidence that on a wider road, more time is available and hence drivers allow longer glances and more time for subsidiary tasks (Wikman, Nieminen, & Summala, 2008; Wikman & Summala, 2005). Similarly, on a road with a series of bends, available spare capacity diminishes and subsidiary tasks typically drop out (Summala, 2007). Evidence for the proposition that drivers try to keep task difficulty more or less constant over the short term derives from the work of Godthelp (1988), who instructed drivers in open road conditions to correct their path only at the moment when it could still be corrected comfortably (to prevent lane boundary crossing). Godthelp found that over a wide range of speeds, time-to-line crossing at the point of decision was essentially constant. In a field study, van der Horst (2007) asked drivers to brake hard at the last moment at which they thought they could stop in front of the simulated rear end of a stationary passenger car. He similarly found that time to collision appeared to be independent of approach speed. In addition, in a simulator study of car following, Van der Hulst, Meijman, and Rothengatter (1999) found that when drivers expected decelerations in the lead vehicle, they maintained the same minimum headway irrespective of whether the lead vehicle decelerated or not,

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implying that they were maintaining a consistent safety margin (time to collision).

2.4.3. Hysteresis and Top-Down and FeedForward Control There is evidence that under certain conditions there can be a hysteresis effect (i.e., response delay) in this homeostatic process, in which drivers’ adjustments lag behind changes in task demand. Thus, for example, Andrey, Mills, Leahy, and Suggett (2003) found that the first snowfall days of the year were especially prone to increased accidents. On the other hand, a key component of driver capability is a valid mental representation of what may happen next. It is this that enables top-down or feed-forward control decisions, where drivers’ adjustments to changes in task demand can anticipate those changes. We can represent this process in the model by including not only actual perceived demand but also perceived demand as immediately anticipated (Figure 2.7). Evidence that less experienced drivers have poorer anticipatory adjustment to changes in task demand derives from the work of Saad, Delhomme, and Van Eslande (1990), who found that younger drivers adjusted their speeds less (than older drivers) when approaching an intersection, and in that of de Craen (2010), who demonstrated that recently qualified drivers performed worse than experienced drivers in a test measuring adaptation of speed to increases in task demand. Real challenges for research and development are how to accelerate novice driver progression to this kind of anticipatory task difficulty management and how to sustain it in the face of frequent feedback that it was not actually necessary. In addition, the driver’s action creates, by and large, the future with which he or she has to deal. This is why the driver needs to know the effects of those actions in the context of the unfolding road and traffic situation ahead. Evidence of poorer knowledge of this type in less experienced drivers is clearly exemplified in their higher involvement in single-vehicle crashes.

2.4.4. Boundaries of Preferred Task Demand The lower boundary of a driver’s preferred task demand will be determined by a minimum consistent with making satisfactory progress and providing sufficient stimulus to avoid boredom and perhaps prevent a progressive decline into drowsiness and sleep. The upper level will be determined by such variables as the driver’s perceived capability, motivation to put effort into the task, and goals of the journey in question. Journey goals may, of course, have a direct influence on choice of speed; however, if the choice of speed is higher than would normally be preferred, perhaps because the driver is running late and needs to make up lost time, this will raise the level of task demand

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PART | I

FIGURE 2.7 Representation of actual perceived demand and anticipated demand.

Range of acceptable task difficulty

Comparator

Output function (speed and trajectory)

Input function (perceived task difficulty)

Perceived capability

Theories, Concepts, and Methods

Perceived demand (actual) Perceived demand (anticipated)

Impact on environment (task demand)

and require the driver to operate with a higher level of task difficulty.

2.4.5. Task Difficulty as Risk Feeling One further point in the elaboration of the TCI model is that drivers appear to experience task difficulty in the same way as they experience feelings of risk (Fuller, McHugh, & Pender, 2008). In the study by Fuller et al., participants were asked to rate video sequences of the same segments of a roadway traveled at a wide range of speeds that were systematically varied. Ratings were recorded of task difficulty but also of feelings of risk. Estimates of the statistical risk of loss of control and collision were also obtained. In several replications, it was found that ratings of task difficulty and feelings of risk covaried very closely: The typical correlation between the two variables was on the order of r ¼ 0.97. However, such ratings were independent of estimates of statistical risk at lower levels of rated difficulty and risk feeling. Thus, risk feeling and statistical risk estimates are not the same thing, but risk feeling can behave as a surrogate for task difficulty. This finding has since been replicated by Kinnear, Stradling, and McVey (2008) and Lewis-Evans and Rothengatter (2009). The pivotal role of risk feeling in driver decision making is discussed further in Section 5. That feelings of risk should be so closely associated with perceived task difficulty should come as no surprise given that the outcome of loss of control of the task may be potentially so punishing. If we consider how task difficulty may be represented in the “comparator” element of the task difficulty homeostasis model, one possibility is that it involves a meta-cognitive process that is sensitive to the degree of deviation from subgoals of the driving task. Relevant subgoals that relate to speed choice, because they

are time critical, are the maintenance of directional control (adhesion to road), sampling and processing of required information, and enabling of required response. Thus, deviations from these subgoals, such as loss of directional control, loss of time to sample needed information, and loss of time to enable response execution, may trigger a fear or anxiety response because of the potentially punishing consequences. It is a question for future research to determine whether or not the degree of fear felt is systematically related to such measurable variables as time-to-line crossing, or time to collision, or is triggered in an all-ornothing manner (i.e., driven by possibility rather than probability; see Loewenstein, Weber, Hsee, & Welch, 2001).

2.4.6. Individual Differences in Preferred Task Demand and Difficulty Accumulating evidence reveals that drivers vary in their individual dispositions to adopt a particular level of task demand. In a study with Steve Stradling’s group at Napier University (project HUSSARdhigh unsafe speed accident reduction), we interviewed a national sample of British drivers, and in part of this we presented respondents with a picture of a single carriageway rural road and asked them about two speeds: What speed would they normally drive and what speed would put them right at the edge of their safety margin? There was wide variation in preferred speed: 81% of the sample were distributed over a range of nearly 30 mph (36e64 mph). Furthermore, 7% indicated a speed lower than 36 mph and 11% a speed higher than 64 mph. There was similarly wide variation in what speed they thought would put them right at the edge of their safety margin. A majority (61%) said that a speed less than 65 mph would do so. Twenty-two percent said a speed of

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Driver Control Theory

65e74 mph, 11% said a speed between 75 and 84 mph, and 6% said a speed of 85 mph or faster would do so (Stradling et al., 2008). Despite this wide individual variation in preferred and in edge-of-safety margin speeds, there was a consistent relationship between the two speeds: Edge-of-safety margin speed represented a 14% increase over preferred speed. Furthermore, feelings of risk and stress did not vary with speed chosen: The feeling of risk was similar, whether one was a slow or a fast driver on the same segment of roadway. This suggests that despite variations in speed choice, perceived task difficulty may have been much more equivalent among drivers. Project HUSSAR also confirmed, on the basis of a 12year literature review, the national survey, and four focus groups, earlier findings by Musselwhite (2006) that there are four distinguishable groups of drivers. We have labeled them low risk threshold, high risk threshold, opportunistic, and reactive (Fuller, Bates, et al., 2008). Risk threshold in this context refers to the upper limit of task difficulty a driver will accept (i.e., the smallest separation between perceived task demand and capability). Low risk threshold drivers comply with speed limits, reduce their speed if they realize they are traveling faster than the speed limit, and are unlikely to change their driving behavior in a 30 mph (50 km/h) zone as a result of momentary influences, including if they are in a hurry. They are typically older, more experienced, and represent approximately 40% of male and female drivers. In marked contrast, high risk threshold drivers have positive attitudes to high-risk behavior and a thrill-seeking and expressive use of their car (Machin & Sankey, 2008), often as part of a youth subculture that exploits driving as a recreational activity that is functionally related to their life situation (Møller & Gregersen, 2008). They drive at higher speeds; commit more, and more extreme, speed limit violations and other forms of dangerous driving behavior; and have more convictions. Not surprisingly, they are more involved in collisions. Members of this group are typically young, inexperienced, and male, and they are poorly calibrated. They represent approximately 14% of drivers. The origins of the driving style of at least some members of this group may date back to early childhood. In a seminal paper by Vassallo et al. (2007), which was concerned in part with identifying longitudinal precursors of high-risk driving behavior, three clusters of drivers were identifiable at ages 19 and 20 years who differed reliably in their engagement with risk-related driving behaviors, such as excessive speeding, drink driving, drug driving, driving when fatigued, and not using seat belts. Members of the high-risk group, which comprised 7% of their sample of 1135 young adults, were mainly male (77%) and were found to have been involved in more speeding offenses and

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collisions. Compared with others, they were more antisocial in behavior and choice of friends, more aggressive, more irresponsible, showed less empathy, and were more likely to engage in maladaptive coping (e.g., multisubstance use). However, particularly intriguing in their findings was that the characteristics of antisocial behavior and aggressiveness differentiated between the groups as early as ages 5e8 years and persisted throughout later childhood and adolescence. Does this finding imply that we can identify certain types of high-risk driver as soon as they are old enough to go to school? If so, what implications might this have for early intervention? Opportunistic drivers do not pursue high speed for its own sake, unlike the high risk threshold drivers. They tend to adjust their speed to the conditions rather than to the speed limit, and they will exceed the limit if they believe it is safe to do so. They exploit opportunities to get ahead. Approximately 23% of drivers can be labeled as primarily opportunistic, and they are more likely to be male than female. The latter, on the other hand, are more likely to be reactive drivers. This group is not persistently concerned with making good progress and tends to avoid unsafe high speed and dangerous overtaking. However, such drivers can be strongly influenced by their emotional state, driving faster if annoyed or angry or under time pressure. Consistent with this is the finding in a questionnaire study by Bjo¨rklund (2008) that women drivers report more irritation than men when impeded or exposed to reckless driving, and evidence presented by Lustman and Wiesenthal (2008) that female drivers report more aggression than men when feeling low levels of anger in similar scenarios. Dispositional influences on driver risk threshold, and therefore speed choice (potentially), are partly captured by the social and cognitive variables that form the core elements of the theory of planned behavior (TPB), notably intentions, attitudes, and perceived social norms. However, correlations between measures of these variables and measures of actual behavior are not particularly strong, perhaps explaining approximately 25% of the variance in ˚ berg & Walle´n Warner, 2008), the behavioral variable (A and it is perhaps self-evident that such a conceptual approach cannot provide a comprehensive model of dispositional influence and most certainly not an account of real-time speed decisions by drivers. Thus, Paris and Van den Broucke (2008) conclude in an evaluation of TPB that actual speeding behavior can only partially be predicted from TPB concepts and that the cognitive determinants of safe driving as identified by the TPB need to be complemented by other factors, including less “conscious” cognitive factors such as personal identity and habit formation, as well as external factors, such as cues to action, reinforcers, or the design of roads. (p. 179)

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3. TASK DIFFICULTY ALLOSTASIS: TEMPORARY INFLUENCES ON RISK THRESHOLD In addition to dispositional differences, a wealth of research has demonstrated that several variables may temporarily raise a driver’s risk threshold. Such variables include feelings of anger and aggression, competitiveness, thrill-seeking to get an “adrenalin rush,” feelings of power, social influences, the pressure of being late, and to find out how fast a vehicle can go (Fuller, Bates, et al., 2008). For example, Ellwanger (2007) has shown that young drivers’ “delinquent” driving responses, such as speeding, aggressive driving, and risk taking, are strongly correlated with individuals’ ascribing their frustration to the voluntary and intentional actions of others on the road. Jamson (2008) reported that drivers drive closer to the car in front when their emotions are aroused. Similarly, King and Parker (2008) showed that relatively high levels of anger are associated with increased commission of both aggressive and highway code violations and that accident-involved drivers are more angry and hostile than accident-free drivers. This evidence of factors that may have an immediate influence on the level of task difficulty that drivers are prepared to accept implies that the hypothesis of task difficulty homeostasis is not completely satisfactory and that a more appropriate concept is that of allostasis. Whereas homeostasis is the process by which a target condition is maintained in the face of external variation in a negative feedback loop system, allostasis refers to adaptation to a more dynamic target condition and is defined as maintaining certain levels of biological conditions that vary according to an individual’s needs and circumstances (Kalat, 2008). So what we should really be discussing here is task difficulty allostasis. As an example of this allostatic variation in needs and circumstances, consider results from a study examining the conditions under which drivers of emergency service vehicles are more likely to crash (Gormley, Walsh, & Fuller, 2008; Walsh, Hannigan, & Fuller, 2008). For both ambulances and fire trucks, significantly more collisions are reported under blue light (BL) conditions (responding to an emergency situation with blue lights on and usually with accompanying siren) than under non-blue light (nBL) conditions. For every one nBL collision there were three BL collisions. This contrast is useful in the sense that fire trucks provide their own controls for a comparison of driving under time pressure in one direction and without that pressure in the other (albeit confounded by condition order). Drivers were quite open about their acceptance of an increased task demand level on the way to a serious case: You can justify driving at a certain speed when its three kids in a house, if you’re standing in front of a judge. You can’t justify that kind of driving if it’s a bin on fire. dParticipant 3

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Theories, Concepts, and Methods

In a questionnaire survey of these ambulance and fire truck drivers, they were significantly more likely to say they would overtake and take risks whenever possible when driving under blue lights. Thus, under certain conditions, the reference criterion for acceptable task difficulty can change: allostasisdnot homeostasis.

4. COMPLIANCE One further component now needs to be added to the model to make it more complete. The decision output from the process of task difficulty allostasis may be an achievable speed but which is in excess of the legal limit for the road segment in question. Hence, we need to include the driver’s disposition to transfer from choosing a speed based solely on task difficulty to a speed consistent with the legal limit (Figure 2.8). Evidence indicates that there is considerable variation in drivers’ dispositions to comply with limitsdvariation that represents both more or less stable individual differences (as discussed in Section 2.4.6; see Fuller, Bates, et al., 2008) and momentary influences on compliance (Stradling et al., 2008). With regard to individual differences, cognitive style may relate to degree of noncompliance with limits (and other forms of deviant behavior), particularly whether or not the individual tends to focus on potentially positive outcomes of choices and discount potentially harmful consequences or vice versa. Lev, Hershkovitz, and Yechiam (2008) showed that in a gambling task, traffic offenders give more weight to gains compared with losses, relative to control drivers, with the implication that when speeding their minds are more focused on the gains involved rather than the possible costs in terms of detection and penalty or loss of control. Interestingly, they were also found to be more extroverted, which would also dispose them to be more sensation-seeking in their profiles. Despite individual differences in disposition to comply, whatever its basis, violation of speed limits is a pervasive phenomenon: The OECD estimates that at any one time, approximately 50% of drivers are exceeding the speed limit (OECD/European Conference of Ministers of Transport, 2006). It is important to note that for a large proportion of drivers, this behavior does not necessarily represent some kind of willful contempt for rules and regulations but is rather an expression of their maintaining a preferred level of task difficultydadjusting task demand to the prevailing conditions as they perceive themdhence their anger at getting caught and fined and the general lack of social censure from others for minor violations. In the HUSSAR study discussed previously, all four of the focus groups supported the view that noncompliance is not necessarily unsafe and that an immediate influence

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Disposition to comply with speed limit Range of acceptable task difficulty Comparator

Input function (perceived task difficulty)

Perceived capability

Immediate influences on compliance

Output function (speed and trajectory) Perceived demand (actual) Perceived demand (anticipated)

Impact on environment (task demand)

FIGURE 2.8 Influences on compliance.

supporting noncompliance is that the speed limit is perceived to be too low.

5. RISK ALLOSTASIS THEORY Prevalent emotions concerning speed choice are likely to be fear and frustration, with fear associated with the upper level of difficulty tolerated (the driver’s risk threshold) and frustration the lower level, arising from deviations from driving goals that would otherwise have been positively or negatively rewarding. In relation to fear, in 1964, Taylor concluded from on-road observations of drivers’ autonomic activity that drivers adopt a level of anxiety that they wish to experience when driving and then drive so as to maintain it. Mesken, Hagenzieker, Rothengatter, and de Waard (2007) studied participants who drove an instrumented car in real road environments and gave self-reports at critical points. They found that anxiety associated with safetyrelated events was the most frequent on-road emotion (from a choice restricted to anger, nervousness, and happiness), and this was in turn associated with increased perceived risk (and heart rate). In the HUSSAR study (Stradling et al., 2008), in response to the open road scenario, feelings of risk were positively correlated with ratings of task difficulty (r ¼ 0.64) and significantly inversely related to perceived safety margin: The larger the margin, the less the feeling of risk. Most respondents (76%) agreed that if they drove any faster than normal, they would feel less in control, the task of driving would be more difficult (67%), and it would feel too risky (75%).

The upshot of this link between perceived task difficulty and risk feeling, discovered in our digital video studies mentioned previously (Fuller, McHugh, et al., 2008), is that we can now refer to the model as risk allostasis theory (RAT) (which is somewhat more pithy than the hypothesis of task difficulty allostasis subsumed within the TCI model). With this change in nomenclature, it is important to stress that we are not simply substituting allostasis for homeostasis in the theory known as risk homeostasis (Wilde, 1982). In Wilde’s model, the risk concept is operationalized in terms of feelings of risk in conjunction with statistical risk estimates (Simonet & Wilde, 1997). Estimates of statistical risk have no part to play in RAT, and this rejection of the role of statistical risk in driver decision making has also been emphasized by Vaa in his critical analysis of RHT (Vaa, 2007, pp. 214 and 266). Furthermore, in RHT, the determination of preferred risk levels (“target risk” in Wilde’s terminology) arises out of an inferred costebenefit analysis of safe and risky behavioral choices rather than the variables of perceived capability, journey goals, effort motivation, and dispositional and immediate factors identified in RAT.

5.1. The Role of Feelings in Decision Making The role of feelings in decision making has a long history, being explored, for example, in the early work on emotion of William James and Carl Lange in the nineteenth century and significantly developed as a concept in the work of Zajonc in the twentieth century (Zajonc, 1980). Nevertheless, one gets a sense that the so-called cognitive revolution has until relatively recently largely neglected this role.

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However, a new emphasis on the importance of emotion and feeling in decision making has emerged in particular through the work of Damasio (1994, 2003), with his concept of the “somatic marker hypothesis.” Before discussing this hypothesis, it is useful to briefly consider the fundamental role of feelings in motivation. Feelings are the experiences concomitant with reward and punishment, with incentives and deterrents, with things we seek and things we avoid. They are the engines of the values we have and the goals we seek. Thus, although our decisions about how to realize our goals may principally involve cognitive operations, it is feelings that select our goals, enabling us to choose between them, and that energize or motivate our approach to them. Driving goals are no different. They are similarly feelings motivated and must involve at the same time both positive, approach-motivating feelings associated with the achievement of the mobility goal (destination, journey, or both), and negative, avoidance-motivating feelings associated with collision or road run-off. Once goals are identified, to a certain extent we can leave it up to cognitive operations to guide decisions that enable us to attain these goals. From a feeling perspective, however, there is one important difference between goals of approach and goals of avoidance. In the former, feelings (positive) intensify as the goal becomes nearer and are presumably fully experienced when the goal is reached. In the latter, feelings (negative) decrease to the extent that the avoidance goal is achieved. Thus, negative feelings are presumably rarely fully experienced when avoidance is successful (as is the case nearly all of the time when driving), thus giving the impression that feelings of risk, for example, are generally not important in decision making (Lewis-Evans & Rothengatter, 2009). As Carver and Scheier note (1981, p. 199), “self-regulation is relatively affect free as long as normal discrepancy reduction processes are uninterrupted and are proceeding without difficulty [italics added].” However, “when discrepancies cannot be easily reduced, then affective processes become important” (pp. 360e361). Despite this assertion, it is most important to stress that the stimuli that trigger avoidance responses must retain their emotive characteristic; otherwise, they will become neutral stimulidmere shadows that are powerless to elicit an avoidance response. The driver still has risk feelings associated with objects to be avoided: These are what determine avoidance goals. However, when operating with a safe margin from objects to be avoided, those feelings are not intensified and may not enter conscious awareness. It is this condition that is captured so well by Summala’s zero-risk hypothesis, with risk feelings only kicking in when the safety margin has shrunk to some critical level. Summala’s (2007) model argues that action is continuously monitored by a subjective risk/fear monitor, but this only

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plays a role in decisions when some threshold has been reached. However, Summala’s view is surely untenable: Risk feelings must continuously play a part to enable the driver to maintain safety margins, even if they have the characteristic of “whispers of affect,” as Slovic, Finucane, Peters, and MacGregor (2002) described such responses. Without continuously taking account of the emotions triggered by elements in the road and traffic environment and discrepancies between current and goal states, the driver would have no basis for decision making for his or her choices. (To test this phenomenon directly when driving, keep your eyes closed and note the rapid onset of risk feeling as you proceed. Note that this test is not recommended from a safety perspective.) Interestingly, in a study for the Irish Road Safety Authority, we obtained evidence that suggests that younger drivers are less disposed to think (and presumably therefore feel) immediately of the severest consequences of extremely dangerous behavior (Gormley & Fuller, 2008). In an interview survey of 1039 male drivers attending the World Rally Championships in Ireland in 2007, we presented the following crash scenario and asked respondents to list as many consequences as occurred to them. Participants were distributed approximately equally across the four age groups of 17e19, 20e22, 23e25, and 26e28 years: I am now going to describe to you a crash and when I finish I would like you to tell me what you think the consequences might be: “John, a young man of 20, loved driving fast and showing his mates how he could push his car to the limit. One rainy day, with two of his mates with him in the car, he took a corner too fast, lost control, and slammed into a tree at 120 km/h (approximately 75 mph).” What do you think might be the consequences of this crash?

In the subsequent analysis, attention was paid to the order in which particular responses were given. The three main categories of consequence identified in order of frequency were death, serious injury, and damage to car/ property. No differences between age groups were found in the frequency of reporting any particular category. However, death was significantly less likely to be mentioned early as a consequence by the youngest group of drivers. Consistent with this, in a survey of young drivers in compulsory service in the Israeli Defense Forces, TaubmaneBen-Ari (2008) found that the cost of risk to life was not a predictor of any reckless driving measure. In a comprehensive review of brain imaging studies and decision making, Glendon (2008) noted that less welldeveloped executive functions of the brain in late adolescence may mean that implications of hazards are not so readily accessed. Linked to this is the observation that the

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integration of emotion with cognition, which appears to be mediated by the amygdala and hippocampus, is still maturing during this period.

5.2. The Somatic Marker Hypothesis In the somatic marker hypothesis, Damasio argues that elements of experience, such as objects, persons, and scenarios, automatically trigger an emotional response, albeit often only a weak one, whenever their representation is activated in the brain by either external or internal stimuli. Damasio proposes that in any situation requiring a decision, emotional signals “mark options and outcomes with a positive or negative signal that narrows the decisionspace and increases the probability that the action will conform to past experience” (Damasio, 2003, p. 148). This emotional signal has an auxiliary role that increases the efficiency of the reasoning process and is not usually a substitute for it. However, when we immediately reject an option that would lead to certain disaster, reasoning may be “almost superfluous”: The action may be taken without some intervening conscious cognitive processing. Because emotional signals are body related, Damasio labeled this set of ideas the somatic marker hypothesis. Through learning, somatic markers can become linked to stimuli and patterns of stimuli. When a negative somatic marker is linked to an image of a future outcome, it sounds an alarm. Slovic et al. (2002) refer to a similar concept as the “affect heuristic.” The key conclusions, however, are that not only is affect essential to rational action but also affective responses have a direct effect on cognitive operations (see Fuller (2007) for a complete exposition of Damasio’s conceptualization). Note that, as discussed previously, emotional responses in the form of somatic markers arise not only from stimuli external to the driver but also from perceived discrepancies between goal states and current states. Included in these discrepancies are where task demand exceeds the upper limit preferred by the driver (yielding a conscious feeling of anxiety, risk, or fear) and where progress goals are thwarted (yielding a conscious feeling of frustration, anger, or rage). The relevance of the somatic marker hypothesis for driver decision making has been discussed by Summala (2007), who suggests that “in dynamic time-limited situations like driving, fast affective heuristics must have a big role” (p. 198), and its potential implications for driver safety have been discussed by Fuller (2005b, 2007). Increases in risk may not be felt because of suppressed emotional reactivity (e.g., through alcohol, depression, denial, desensitization, and perhaps in conditions in which the outcome of the decision is uncertain; van Dijk & Zeelenberg, 2006) or because of the swamping effect of other emotions (e.g., anger and exhilaration). If felt, risk feelings may be misattributed to events other than those related to accomplishing the driving task (e.g., anxiety

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from interaction with a passenger). Furthermore, experience may not have been sufficient to provide learning opportunities to link particular scenarios to feelings of risk (as with a novice driver) (Kinnear et al., 2008; Wickens, Toplak, & Wiesenthal, 2008). These and related issues for further research are discussed by Fuller (2005c), and a preliminary study of the contrasting roles of emotion and cognitive decision making as dispositional characteristics of drivers has been reported by Wickens et al. (2008).

6. ALTERNATIVE CONCEPTUALIZATIONS OF DRIVER GOALS Turning to recent proposals for what constitutes the driver’s control goals when driving, Vaa (2007) develops the implications of the somatic marker hypothesis for driver behavior in his “monitor model.” This argues that drivers may make adjustments to the prevailing road and traffic conditions with varying degrees of conscious awareness, on a continuum from unconscious adjustment to fully conscious decision making. He proposes that although risk feeling may describe one homeostatic target for drivers (referred to as tension/anxiety), other feelings may also be targeted. Candidates he suggests as “other feelings” are avoidance of threat or difficulty, compliance and noncompliance, arousal, sensation, joy, and relaxation. Feelings of avoidance of threat or difficulty are clearly related to task difficulty and feelings of risk, as discussed as targets in RAT, which also now incorporates dispositional and immediate influences on compliance. However, the wider range of target states motivating driver decision making proposed by Vaa’s monitor model describe rather the dispositional motives and immediate influences on risk threshold as described in RAT. Rather than being target conditions in themselves, I argue that dispositional motives and immediate influences operate to “set” the target level of risk feeling in the negative feedback control loop. Thus, the driver looking for more arousal raises his or her risk threshold to achieve that state, and the driver wanting to relax does the opposite. Summala’s theoretical development also appears to be moving in a more inclusive direction. Whereas the 1976 conceptualization developed with Risto Na¨a¨ta¨nen (Na¨a¨ta¨nen & Summala, 1976) postulated a subjective risk monitor that kicked in when risk experience exceeded a risk threshold, to both alert the driver and influence decision making, Summala now suggests that drivers operate not with just one target variable but with a whole range of them (Summala, 2007). He invokes the umbrella concept of a “comfort zone” to represent the range of values relating to each variable that drivers are assumed to be motivated to target: “It is hypothesized that drivers normally keep each of them within a certain range (or above a certain threshold) in a comfort

24

zone” (p. 201). Comfort is defined as a general mood or emotion that is “pleasant but not especially aroused, tense, or activated” (p. 201). Included in Summala’s (2007) target variables are space and time margins and mental load specifically relating to control. He also includes motivation for compliance. These variables may be translated in terms of the concept of a target range of task difficulty (operationalized in terms of time to collision and time to line crossing) and influences on compliance as represented in RAT. However, Summala adds various other target variables, including comfort in relation to thermal state, seating, vibration, glare, and rate of speed change and progress. Clearly, Summala’s model is shifting from one specifically concerned with control and collisions to one concerned with more general motives that inform driver decision making. From the perspective of RAT, “glare” and “rate of speed change and progress” may be subsumed under task demand (and therefore task difficulty) elements. However, Summala’s other comfort motives must be secondary to those relating to safety motivation. A driver will hardly survive for very long without crashing if he or she prioritizes temperature, seating comfort, or vibration as the target states that direct decision making. As pointed out by Carver (1994), “certain kinds of discrepancies are more demandingdmore importantdthan others.. For example, the experience of threat to one’s physical safety can override an attempt to engage in activities that are otherwise quite important” (p. 389). Nevertheless, these suggestions by Summala have enriched our conceptualization of potential aspects of driver motivation (we await empirical validation), even though their relevance to our understanding of why collisions occur is unclear. RAT is concerned with representing the process of driver decision making and in particular how motivations influence the outcome for system safety. However, in principle, it can be expanded to include the kinds of motives proposed by Summala. Their influence may be included in RAT as a top-down controlled hierarchy of secondary reference targets in decision making. Because a safe outcome normally has to be prioritized, they must enter the decision-making process after risk allostasis decisions have been made, perhaps at the point in the process where influences on compliance also have their effect. Summala’s extended target variables nevertheless raise a further question: When a control system has multiple reference standards, as he suggests, how do they operate in relation to each other? For example, are they implemented in serial order, as suggested in RAT, where compliance standards emerge as secondary to task difficulty targets, or can they operate in parallel? If the latter, the further question remains as to how their separate outputs are eventually integrated into the behavioral decision. Thus, if

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Theories, Concepts, and Methods

task difficulty is calling for an increase in speed and simultaneously compliance is calling for a reduction, how is the conflict resolved by his system? Perhaps the main conclusion to be drawn here is that despite the discrepancies that have emerged in conceptualizations of what drivers are aiming for in their decision making, these apparent tensions may in fact reflect a hidden consensus. At least, from the perspective of RAT, that is what I have tried to demonstrate. RAT proposes that driver control decisions are motivated by a desire to maintain feelings of risk (and its corollary task difficulty) within an acceptable range, even though for much of the time these feelings may be below the level of conscious awareness. The acceptable level of risk feeling and task difficulty may vary as a function of factors such as journey goals and emotional state, and there appear to be individual differences in preferred levels related to age, experience, gender, and personality. Constraints on the driver’s freedom to manage this process unavoidably arise from performance limitations of the vehicle as well as through obstructions caused by congested traffic flow that force driving at a lower level of task demand than that preferred. Freedom may also be restricted by compliance with regulated speed limits. With the development of RAT and related concepts advanced by Vaa and Summala, there is a current convergence in recognizing the primacy of the role of feeling in driver decision making. As Laertes says in Shakespeare’s Hamlet, “best safety lies in fear,” and this recognition opens up a whole new set of exciting and promising research questions.

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Chapter 3

CaseeControl Studies in Traffic Psychology Martha Hı´jar,* Ricardo Pe´rez-Nu´n˜ez* and Cristina Incla´n-Valadezy *

National Institute of Public Health, Cuernavaca, Morelos, Mexico, y London School of Economics and Political Science, London, UK

1. INTRODUCTION Although the effects of urbanization and industrialization in most countries suggest a degree of inevitability, substantial reductions in rates of road crash fatalities have been achieved in high-income countries despite increasing motorization. Evidence suggests that for the majority of the world’s population, the burden of road traffic injuries is increasing dramatically (Ameratunga, Hijar, & Norton, 2006). Risk in road traffic derives from a need to travel for different reasons and from a range of factors that determine who uses different parts of the transport system, how it is used and why, and at what times (Tingvall, 1997). The concept of risk in road safety includes factors related to exposure, considered as the amount of movement or travel within the transport system by different users or a given population density; the crash probability given a particular exposure; the probability of being injured after a crash; and the outcome of injury. It has been documented that, although it might not be possible to eliminate all risks, it is possible to reduce the exposure to risk of severe injury or minimize its intensity and fatal consequences. The global and concerted approach required must consider the wider societal burden of road traffic crashes (both fatal and nonfatal outcomes) and particularly focus on efforts to protect vulnerable road users (e.g., motorcyclists, human-powered vehicles, and pedestrians). Addressing the disparities in both the impact of and response to this problem must be high on the global public health policy and research agenda (Ameratunga et al., 2006). A substantial body of literature points to the propensity of some road user groups, particularly pedestrians and those using motorized and nonmotorized two-wheelers, to be vastly overrepresented among crash victims at the global level (Peden et al., 2004; Razzak & Luby, 1998) and be at higher risk of crash-related disability (Mayou & Bryant, 2003). Passengers in formal and informal modes of public

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10003-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

and mass transport constitute another important road user group that is a common feature among road crash data, especially from less resourced environments. Error by a road user may indeed trigger a crash but may not necessarily be its underlying cause. In addition, human behavior is governed not only by individual knowledge and skills but also by the environment in which the behavior takes place (Khayesi, 2003). Indirect influences, such as the design and layout of the road, the nature of the vehicle, and traffic laws and their enforcement or lack of enforcement, affect behavior in important ways. For this reason, the use of information and publicity on their own is generally unsuccessful in reducing road traffic collisions (Allsop, 2002; European Road Safety Action Programme, 2003; Impacts Monitoring Group in the Congestion Charging Division of Transport for London, 2003). Error is part of the human condition. Aspects of human behavior in the context of road traffic safety can certainly be altered. Nonetheless, errors can also be effectively reduced by changing the immediate environment rather than focusing solely on changing the human condition (Wang et al., 2003). This chapter provides general guidelines on how to use the epidemiological approach to study the problem of road traffic injuries using the caseecontrol study design. The chapter begins with a general description of epidemiological study designs. It proceeds to give a more comprehensive description of caseecontrol studies, specifically how these studies are defined and when they are most commonly used, followed by “case” definition and alternatives to select cases. Subsequently, it presents the definition of a “control” and explains how controls can be identified and selected. Reasons to match are then presented, followed by a discussion of the forms of matching and stratification and of the disadvantages of matching strategies. An argumentation on how many controls should be included in caseecontrol studies is 27

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PART | I

then presented, followed by a discussion on how to analyze caseecontrol studies and what measures of associationecausality are used in this type of study. Several variants of caseecontrol studies are presented. Next, the chapter addresses the problem of representativeness and discusses some of the principal biases that are relevant and characteristic of caseecontrol studies, especially in road safety research and analysis. Finally, the advantages and disadvantages of caseecontrol studies are discussed.

2. EPIDEMIOLOGICAL STUDY DESIGNS The purpose of epidemiology is to describe and explain the population health dynamicsdto identify the elements that compose it and to understand the forces that governs it in order to develop actions aimed to preserve and promote ´ vila & Lo´pezhealth between populations (Herna´ndez-A Moreno, 2007). It is thus not only concerned with the occurrence of disease or other health-related events but also with the identification of factors that cause those conditions, which has become the main focus of modern epidemiology (dos Santos-Silva, 1999b). In general, researchers studying road safety attempt to answer the following questions: 1. Does alcohol consumption increase the risk of pedestrian injuries (Haddon, Valien, McCarroll, & Umberger, 1961)? Alcohol consumption (exposure)

Pedestrian injuries (outcome)

Theories, Concepts, and Methods

2. Does seat belt use decrease the risk of severe road traffic injuries in a car collision (Hı´jar-Medina, Flores-Aldana, & Lopez-Lopez, 1996)? Seat belt use (exposure)

Severe road traffic injuries (outcome)

3. What environmental factors could increase the risk of pedestrian and cyclist road traffic injuries (Kraus et al., 1996)? Environmental factors (exposure)

Pedestrian and cyclist road traffic injuries (outcome)

´ vila and Lo´pez-Moreno (2007) classified Herna´ndez-A epidemiological studies using a multidimensional approach (Table 3.1), including the following: 1. Assignation of exposure: observational, experimental, and Quasi-experimental. 2. Number of measurements performed in each study subject to verify changes in the occurrence of both exposition and its effect (longitudinal versus crossover study design). 3. Criteria employed to select population under study (none, exposition, and effect). 4. Temporal relationship between the start of the study and the measurement of the occurrence of the effect (retrospective, prospective, mixed, or ambispective). 5. Unit of analysis for which all variables of interest are measured (individual, group, and population). It is important to note that the concept of an individual in

TABLE 3.1 Multidimensional Classification of Epidemiological Studies

Type of study

Assignation of Exposition

No. of Observations (Measurements) by Individual

Selection Criteria of Population Temporality of under Study Analysis

Unit of Analysis

Experimental

Controlled (random)

Two or more

None

Prospective

Individual or group

Pseudo-experimental

For/by convenience

Two or more

None

Prospective

Individual or group

Cohort

Out of the control of researcher

Two or more

Exposition

Prospective or retrospective

Individual

Cases and controls

Out of the control of researcher

One or more

Effect

Prospective or retrospective

Individual

Crossover

Out of the control of researcher

One

None

Retrospective

Individual

Ecological

Out of the control of researcher

Two or more

None

Retrospective

Group or population

Source: Herna´ndez-A´vila and Lo´pez-Moreno (2007).

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road safety research has not been reduced to a single person. Some studies have also had as a unit of analysis streets (Kraus et al., 1996), street crossing locations (Koepsell et al., 2002), crash sites (Wintemute, Kraus, Teret, & Wright, 1990), etc. The previous classification categorizes epidemiological studies according to the strength of evidence that each study design provides to the causal relationship between exposure variables and a health outcome of interest ´ vila & Lo´pez-Moreno, 2007). In this sense, (Herna´ndez-A the best study design to establish cause-and-effect relationships is the experimental randomized design. For strictly medical interventions, the “gold standard” is the double-blind, randomized controlled trial. This study design involves the random allocation of different interventions (treatments or conditions) of studied subjects to compare treatment groups with control groups not receiving the treatment. Participants, caregivers, or outcome assessors are not allowed to know which intervention are they receiving. Although these studies may be ideal for testing the efficacy of interventions, there are many instances in which trials would be impossible, impractical, and/or unethical. For example, it would generally be considered unethical to randomly assign research subjects to be exposed to alcohol in order to evaluate the substance’s effects on their driving ability. In this sense, the choice of study design is affected by numerous factors and considerations. According to Robertson (1992), this decision depends on

29

what is the unit of analysis (people, vehicles, environment)? In what population should the study be conducted? To what population of people, vehicles, or environments will the results be generalized? What kind of measurements of the factors are available or could be obtained? How reliable and valid are the measurements? Can the data be collected without violating ethical guidelines? How can the study isolate the effects of given factors independent of, or in combination with, other relevant factors? How much time will be needed to complete the study? How much will the study cost? (pp. 84e85)

The next section provides a general definition of casee control studies and discusses examples of caseecontrol studies in the road safety field.

3. CASEeCONTROL STUDIES 3.1. Definition and Characteristics Caseecontrol studies represent a sampling strategy in which the population under study is selected based on the presence (case) or absence (control) of an event of interest (i.e., health condition, disease, and death) (Lazcano-Ponce, Salazar-Martinez, & Hernandez-Avila, 2001). The underlying purpose of these studies is to identify causal factors of the events of interest by comparing characteristics of both groups (cases and controls). As shown in Figure 3.1, caseecontrol studies start by identifying the study population, from which cases are identified and their exposure status is determined retrospectively. Then, a control group FIGURE 3.1 Design of caseecontrol studies. Source: Herna´ndez-A´vila and Lo´pez-Moreno (2007).

Non-eligible Population

Nonparticipants Eligible Participants

Identification of cases

Selection of controls

Cases

Controls

Study population

Reconstruction of exposition

Beginning of study

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of study subjects is sampled from the entire source population that gives rise to the cases (Rothman & Greenland, 1998). Once the exposure status is also identified in the control group, comparison between both groups may evidence whether exposure to a specific factor is higher in the case group (risk factor) or lower (protective factor) or the same as in the control group (no evidence of associa´ vila & Lo´peztion) (dos Santos-Silva, 1999a; Herna´ndez-A Moreno, 2007). It is important to remember that assignation of exposure in caseecontrol studies is out of the control of researchers. A good example of a caseecontrol study in road safety is the study of Jones, Harvey, and Brewin (2005). This study explored the symptom profiles of acute stress disorder and post-traumatic stress disorder (PTSD) in participants who did or did not sustain traumatic brain injury following a road traffic accident. This study selected as “case” all survivors of a road traffic collision during a period of time who had been diagnosed with traumatic brain injury (TBI). The control group was composed of survivors of a road traffic accident during a period of time with no TBI (Jones et al., 2005). Once the exposure status was identified, comparison between both groups could evidence whether exposition to a specific factor is higher in the case group (risk factor) or lower (protective factor) or the same as in the control group (no evidence of associa´ vila & Lo´peztion) (dos Santos-Silva, 1999a; Herna´ndez-A Moreno, 2007). This study found that at 3 months posttrauma, there was no difference in PTSD symptom profile between non-TBI (controls) and TBI groups (cases). Caseecontrol studies can be conceptualized within the framework of a hypothetical cohort study (Rothman & Greenland, 1998). Although in practice it can be difficult to characterize the cohort or study base (Wacholder, McLaughlin, Silverman, & Mandel, 1992), caseecontrol studies can be based on special cohorts of interest rather than on the general population (Rothman & Greenland, 1998).

3.1.1. When Are CaseeControl Studies Used? Caseecontrol studies are frequently one of the first approaches used in the etiological study of a disease or health condition. This is in part due to the possibility of incorporating in the analysis many exposition factors simultaneously and relatively quickly and inexpensively (dos Santos-Silva, 1999a). Therefore, caseecontrol studies represent a cost-effective way of identifying risk and protective factors and generating hypotheses for subsequent, methodologically stronger studies (Lazcano-Ponce et al., 2001). Caseecontrol design is simply an efficient sampling technique for measuring exposureedisease associations in a cohort or study base (Wacholder, et al., 1992). In addition, caseecontrol studies are commonly used to study conditions

PART | I

Theories, Concepts, and Methods

that are relatively rare or that have a prolonged induction period (dos Santos-Silva, 1999a). A study carried out in Shanghai, China, is an example of a caseecontrol study measuring the effect of exposition to different risk factors (Yu, Wang, & Chen, 2005). This study explored the risk factors influencing the occurrence of road traffic injuries on drivers with a history of accidents and on controls. The study included physiological, psychological, and behavioral risk factors and found that factors such as tiredness and waking up early were related to the occurrence of road traffic injuries. It is important to highlight that the human host or vector (pedestrian and driver) in road traffic injuries has been the unit of analysis in most caseecontrol studies, to the neglect of factors that may be more subject to change for injury control. However, caseecontrol designs can also provide strong evidence regarding environmental factors (Robertson, 1992).

3.2. Case Definition As can be seen in the previous examples, the definition of a case can be virtually anything that the investigator wishes: an injured person from a specific gender or age group or a specific road userdpedestrian, cyclist, motorcyclist, or car occupant. Whoever the case is, the case definition will implicitly define the source population for cases, from which also the controls should be drawn (Rothman & Greenland, 1998). In this sense, having precise criteria to define a case is highly relevant. Objective documentation that cases actually have the disease or health condition under study is highly recommended. When this is not possible, an alternative is to classify cases as “confirmed,” “probable,” or “likely.” If analysis shows a gradual decrease in relative risk from the confirmed category to the likely category, problems of erroneous classification are suspected. For this reason, this classification gives researchers the opportunity to evaluate the probability that results are affected by an incorrect classification of the disease analyzed (dos Santos-Silva, 1999a). In general, there are two types of cases: incident and prevalent. Incident cases are those new cases that appear in the population under study in a specific period of time (or during a pre-established period of time). Memory of past events and exposures tends to be more accurate in cases recently diagnosed. For this reason, incident cases are preferred over prevalent cases. In addition, it is less probable that incident cases changed their habits (exposures) as a result of disease. Prevalent cases are all cases existent (new and previous) in a population in a specific time (or a short period of time) (dos Santos-Silva, 1999a). Prevalent cases are especially useful when it is not possible to establish a specific date for disease onset.

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CaseeControl Studies in Traffic Psychology

However, patients with a condition of prolonged duration tend to be overrepresented because those with a condition of short duration drop out of the study due to recovery or death. Unless exposure under study is not related to recovery or survival, incident cases should be privileged when designing a caseecontrol study (dos Santos-Silva, 1999a).

3.2.1. Identification and Selection of Cases Selection of cases should privilege internal validity rather than external validity (dos Santos-Silva, 1999a). Internal validity refers to the absence of errors made during the selection process of the population under study or during the measurement of individual variables of interest. To achieve internal validity, comparability of groups under study should be met (Hernandez-Avila, Garrido, & SalazarMartinez, 2000). On the other hand, external validity refers to the capacity of a study to generalize observed results to the base population. A prerequisite of external validity is the achievement of internal validity. This is the reason why internal validity should be privileged over external validity (Hernandez-Avila et al., 2000). In addition, selection of cases should include only those for whom the reasonable possibility exists that the disease or health condition existed prior to the study (dos SantosSilva, 1999a). Ensuring that cases comprise a relatively homogeneous group will increase the possibility of detecting important etiological relations. It is less important to be able to generalize results to the entire population than to establish an etiologicalecausal relation even when this

31

relation only applies for a small group of the population (dos Santos-Silva, 1999a). Ideally, selection of cases should follow the paradigm of longitudinal studies. It is recommended to select recently diagnosed cases (incident cases). Less recommended is the use of prevalent cases unless other requisites are met or when justified (Table 3.2;Lazcano-Ponce et al., 2001). Commonly used sources of cases in the published literature on road traffic injuries are hospital cases (Celis, Gomez, Martinez-Sotomayor, Arcila, & Villasenor, 2003; Tester, Rutherford, Wald, & Rutherford, 2004), administrative registries such as city police reports (Lightstone, Peek-Asa, & Kraus, 1997; von Kries, Kohne, Bohm, & von Voss, 1998), and coroners’ registries (Wintemute et al., 1990). Also common is a combination of sources (Stevenson, Jamrozik, & Burton, 1996). Even when cases are identified exclusively at hospital points, they can be reasonably assumed to represent all cases in a region or determined study population when, for example, the severity of the condition or disease requires hospitalization. This is the case for severe road traffic injuries, for which health care utilization patterns are different from those of most other diseases due to the severity of injuries and because the urgent demand for treatment eliminates some of the most common access barriers (Hı´jar-Medina & Va´zquez-Vela, 2003). However, slight injuries that are treated in a hospital or emergency room cannot be considered as representative of the total study base. When reporting results of a caseecontrol study, it is important to specify which cases were not included in the

TABLE 3.2 Options for Selection of Cases Option

Characteristics

Utilization of incident cases with long exposure periods or prolonged latency periods

OR tends to be similar to RR when cases under study are incident and preceded by a long-term exposure.

Use of prevalent cases with prolonged exposure period

OR is similar to RR if disease does not affect the status of exposure and there is a long-standing exposure period. Prevalent cases could be included, especially when new cases are not available (low prevalent conditions), lethality of disease is low, and exposure does not modify the clinical outcome of the disease (survival).

Utilization of incident cases and very short exposure periods

OR is similar to RR when the risk period is short and incident cases are used.

Utilization of prevalent cases

OR comes closer to RR when prevalence of cases is low only if outcome is not related with survival before selection, condition, or exposure and if disease does not affect the exposure status.

Utilization of death cases

Inclusion of death cases is only justified in exposures that could be quantified through the use of high-quality secondary sources of data, such as medical records and occupational information sources.

OR, odds ratio; RR, relative risk. Source: Lazcano-Ponce et al. (2001).

32

PART | I

study even though they satisfied all inclusion criteria. Reasons for exclusion and the number of cases by reason should be specified. This information allows the assessment of the level to which study results may be affected by a selection bias (dos Santos-Silva, 1999a). l

3.3. Definition of a Control A control is an individual without the condition of interest who serves as reference for a case. The purpose of the control group is to determine the relative (as opposed to absolute) size of the exposed and unexposed denominators within the source population. From the relative size of the denominators, the relative size of the incidence rates (or incidence proportions, depending on the nature of the data) can be estimated (Rothman & Greenland, 1998). Thus, caseecontrol studies yield estimates of relative effect measures (Rothman & Greenland, 1998). It is important to note that controls should meet all eligibility criteria defined for cases other than those related to the diagnostic of disease, outcome, or health condition under analysis (dos Santos-Silva, 1999a).

l

l

l

3.3.1. Identification and Selection of Controls Conceptually, it can be assumed that all caseecontrol studies are nested inside a particular population (dos Santos-Silva, 1999a). This is called the “study base,” which can also be thought of as the members of the underlying cohort or source population for the cases during the time periods when they are eligible to become cases (Wacholder, et al., 1992). However, the identification of the appropriate study base from which to select controls is the primary challenge in the design of caseecontrol studies (Wacholder, et al., 1992). For this reason, selecting a control group could be the most difficult part of a caseecontrol study (dos Santos-Silva, 1999a). Controls are selected from the same population base as cases but through a mechanism independent from that used for case ´ vila & Lo´pez-Moreno, 2007). selection (Herna´ndez-A Some authors have set basic principles for control selection that are required to minimize bias, including the following: l

l

Cases and controls should be “representative of the same population base experience” (Wacholder, et al., 1992, p. 1020). Operationally, this implies that if a control develops the condition or disease (the event under study), he or she must be included as a “case” in the study (Lazcano-Ponce et al., 2001). Confounding should not be allowed to distort the estimation of effect. This is referred to as the deconfounding principle. Confounders that are measured can be controlled in the analysis. Unknown or unmeasured

l

Theories, Concepts, and Methods

confounders should have as little variability as possible. Because this variability is measured conditionally on the levels of other variables being studied, the use of stratification or matching can, in effect, reduce or eliminate the variability of the confounder (Wacholder, et al., 1992). Controls must be sampled independently of their exposure status to ensure that they represent the population base (Rothman & Greenland, 1998). The degree of accuracy in measuring the exposure of interest for the cases should be equivalent to the degree of accuracy for the controls, unless the effect of the inaccuracy can be controlled in the analysis (Wacholder, et al., 1992). The probability of selection of a control should be proportional to the time a subject has remained eligible to develop the event or condition under study. This implies that not only are all controls at risk of developing the condition but also subjects selected as controls in an early stage could become cases in latter stages (Lazcano-Ponce et al., 2001). The study should be implemented so as to learn as much as possible about the questions being investigated for a fixed expenditure of time and resources (Wacholder, et al., 1992). This has been called the efficiency principle, and it calls for consideration of costs as well as validity in selection of controls. Statistical efficiency refers to the amount of information obtained per subject; more broadly, efficiency encompasses the time and energy needed to complete the study (LazcanoPonce et al., 2001; Wacholder, et al., 1992). An exclusion rule that applies equally to cases and controls is valid because it simply refines the scope of the study base (Wacholder, et al., 1992).

Results of a caseecontrol study become more credible to the extent that these principles are met. The objective of the principles is to reduce or eliminate selection bias, confounding bias, and information bias (Wacholder, et al., 1992). Perfect adherence to a principle can be as difficult to achieve as perfect experimental conditions in a laboratory. Sometimes, one principle can conflict with another. Indeed, tolerating a minor violation of a principle is often the only way to study a particular exposureedisease association. Such a study can still provide valuable information, particularly when the impact of the violation can be measured (Wacholder, et al., 1992). 3.3.1.1. Source of Controls Most of the sources of controls for epidemiological research are presented in Table 3.3 along with their advantages and disadvantages. However, studies of road traffic injuries commonly use controls obtained from hospitals and neighborhoods. For example, as discussed previously, one of the most popular and practical strategies

Chapter | 3

CaseeControl Studies in Traffic Psychology

to identify road traffic injury cases is through a hospital or emergency room. It could be difficult to consider that they represent all persons injured as a result of road traffic collisions, although this may be true when a hospital covers

33

the entire population under study and there are no access barriers for individuals. If not, they might be claimed to be representative only of road traffic injury users of a specific hospital or emergency room. Controls, however, may

TABLE 3.3 Advantages and Disadvantages of Different Types of Controls Type of Controls

Advantages

Disadvantages

Population controls

Same study base: Ensures that the controls are drawn from the same source population as the case series.

Inappropriate when there is incomplete case ascertainment or when even approximate random sampling of the study base is impossible because of nonresponse or inadequacies of the sampling frame.

Exclusions. Definition of the base can encompass the exclusions. Extrapolation to base population: Distribution of exposures in the controls can be readily extrapolated to the base for purposes such as calculations of absolute or attributable risk.

Inconvenience: Definition of the base can encompass the exclusions. Recall bias: Responses by a previously hospitalized case may reflect modifications in exposure due to the disease, such as drinking less coffee or alcohol after an ulcer, or due to changes in perception of past habits after becoming ill. Less motivation to cooperate.

Random digit dialing

In some circumstances, could come close to sampling randomly from the source population.

Probability of contacting each eligible control will not necessarily be the same because households vary in the number of people who reside in them and the amount of time someone is at home. Contact with a household may require many calls at various times of day and various days of the week. Challenging to distinguish business from residential telephone numbers.

Neighborhood controls

Convenient substitute for population-based sampling of controls. Control of environmental or socioeconomic confounding factors.

If a person is injured in a neighborhood, controls who have knowledge of the injury may give misleading information because of denial of personal vulnerability or other psychological factors. Overmatching. Could introduce selection bias because it cannot be assumed that controls represent the base population from which cases were extracted.

School rosters

Especially useful when population under study is of school age.

Selection bias in contexts of high rates of school desertion.

Hospital or disease registry controls

Comparable quality of information.

Different catchments: Catchments for different diseases within the same hospital may be different.

Convenience. Factors such as socioeconomic characteristics, race, and religion can be controlled. Normally, they tend to be willing to participate and to provide complete and exact information.

Other diseases obtained from a population registry

Comparable quality of information.

Controls from a medical practice

Useful strategy when it is otherwise difficult to find controls who are comparable to cases on access to medical care or referral to specialized clinics.

Willing to participate and to provide complete and exact information.

Berkson’s bias: Caused by selection of subjects into a study differentially on factors related to exposure. Disease of controls could be related to exposure (risk factors).

Berkson’s bias: Caused by selection of subjects into a study differentially on factors related to exposure. Disease of controls could be related to exposure (risk factors). The study base principle can be jeopardized with medical practice controls because the exposure distribution for controls may not be the same as that in the study base. (Continued)

34

PART | I

Theories, Concepts, and Methods

TABLE 3.3 Advantages and Disadvantages of Different Types of Controlsdcont’d Type of Controls

Advantages

Disadvantages

Friend controls

More convenient and inexpensive source of controls.

The credibility of representativeness of exposure is low for factors related to sociability, such as gregariousness or, possibly, smoking, diet, or alcohol consumption, because sociable people are more likely to be selected as controls than are loners.

Controls can be selected from a list of friends or associates obtained from the case at little extra effort while the case is being interviewed. Friends may be likely to use the medical system in similar ways. Moreover, biases due to social class are reduced because usually the case and friend control will be of a similar socioeconomic background. Despite serious shortcomings, friend controls may be useful in some exceptional circumstances, such as in a study of exposures unrelated to friendship characteristics, as is likely in a study of a genetically determined metabolic disorder. Relative controls Useful when genetic factors confound the effect of exposure, blood relatives of the case have been used as a source of controls in an attempt to match on genetic background.

“Friendly control” bias: Sociable people are more likely to be selected as controls than are loners. Loners, although not on anyone’s list, can become a case. A less serious problem is that the use of friend controls can lead to overmatching because friends tend to be similar with regard to lifestyle and occupational exposures of interest. Some cases may not be willing to provide names of friends, increasing nonresponse. Cases and controls may be overmatched on a variety of genetic and environmental factors that are not risk factors but are related to the exposure under study.

Spouses might be a suitable control group if matching on adult environmental risk factors is sought. The case series Only patients need to be studied, and recurrences can as the source of be handled easily. controls

For studies of chronic diseases in which the main focus is on more stable time-dependent covariates, the use of a study series of cases only, as might be found in a disease registry, requires a complete and accurate exposure history and the strong assumption that the exposure of interest is unrelated to overall mortality. This study design may also have lower power than more conventional studies.

Proxy Useful when subjects are deceased or too sick to respondents and answer questions or for persons with perceptual or deceased cognitive disorders. controls Provide accurate responses for broad categories of exposure information, and sometimes even better information than the index subjects.

Because proxy respondents will tend to be used more often for cases than for healthy controls, violation of the comparable accuracy principle is likely. More detailed information is usually less reliable. Could violate the comparable accuracy principle.

Source: dos Santos-Silva (1999a), Lazcano-Ponce et al. (2001), Roberts and Norton (1995), Rothman and Greenland (1998), Stevenson et al. (1996), Wacholder, McLaughlin, et al. (1992), and Wacholder, Silverman, et al. (1992a).

sometimes be difficult to identify in the context of this design. For example, what would be a good control for an injured motorcyclist or car occupant? One solution is to select users of the same medical unit who are more comparable to cases with respect to quality of information because they also have been ill and hospitalized (Wacholder, Silverman, McLaughlin, & Mandel, 1992a). They are also the most convenient choice when controls will be asked to provide bodily fluids or to undergo a physical examination (Wacholder, et al., 1992a). Another strategy to identify suitable controls is that used by Wells et al. (2004). They obtained a random sample of motorcycle riding by identifying motorcyclists from 150 roadside survey sites (also randomly selected from a list of all nonresidential roads in the region under study).

Motorcyclists were photographed as they approached the survey site, stopped, and invited to participate in the study. Where survey sites or conditions were too dangerous for motorcyclists to be stopped, vehicles were photographed and followed up through their registration plate details. Although the authors reported that only 42 (3%) drivers refused to participate, this participation rate could be much smaller in other contexts (Wells et al., 2004). However, would this be the best solution to identify a control for an injured pedestrian? Here, neighborhood controls are even more recommended, especially if we consider that pedestrian injuries in some contexts occur near the place of residence 70% of the time (Fontaine & Gourlet, 1997; Muhlrad, 1998). In these cases, neighborhood controls represents an excellent option to sample

Chapter | 3

CaseeControl Studies in Traffic Psychology

controls. In this sense, after a case is identified, one or more controls who reside in the same neighborhood as that case are identified and recruited into the study. Such controls are matched to the cases on neighborhood. This constitutes a convenient substitute for population-based sampling of controls (Rothman & Greenland, 1998). This is relevant if we consider that population-based control studies tend to be more expensive, require more time, and also require a roster of all eligible subjects and families. In both cases, it is possible and, in fact, common that healthy people do not participate, which could introduce selection bias due to nonparticipation (dos Santos-Silva, 1999a). A neighborhood approach was used by Celis et al. (2003) to identify suitable controls to study pedestrian injuries in a sample of children 1e14 years old, although cases were identified through the attorney general’s office and emergency room registries. Upon leaving the house of each case, the interviewer knocked on the door of the house located immediately to the left and asked whether a child 1e14 years old lived there; if the answer was positive, authorization was requested to conduct the interview. If more than one child lived in the house, one of them was chosen randomly as the control. If there were no children living in the house, or permission was denied to conduct the interview, the next house to the left was approached in the same manner. If the cases are a representative sample of all cases in a precisely defined and identified population and the controls are sampled directly from this population, the study is said to be population based. If possible, this is the most desirable option (Rothman & Greenland, 1998). As previously noted for cases, it is important to give reasons why controls do not participate and, when possible, provide additional information about their sociodemographic characteristics (age, sex, etc.) (dos Santos-Silva, 1999a). For example, we consider a population-based caseecrossover and caseecontrol study of alcohol and the risk of injury (Vinson, Maclure, Reidinger, & Smith, 2003). Cases were injured patients recruited from emergency departments. Each case’s alcohol consumption in the 6 h prior to injury was compared to his or her consumption the day before in a caseecrossover analysis. Cases were recruited by telephone and matched to other cases by age, gender, day of week, and hour. Caseecontrol analyses examined recent alcohol consumption (past 6 h), hazardous drinking in the past month, and alcohol use disorders in the past year. Alcohol’s effect on injury risk was related more strongly to acute exposure than to measures of long-term exposure. The risk was significant even at low levels of consumption.

3.3.2. Matching The fundamental question concerning the selection of cases and controls is the following: What should be allowed to

35

vary as the hypothesized cause, or causes, in stratified samples, and what should be held constant? If the variables to be held constant can be other than randomly distributed between case and controls, the purpose of the design is defeated (Robertson, 1992). Matching is a control selection method that can sometimes improve efficiency in the estimation of the effect of exposure by protecting against the situation in which the distributions of a confounder are substantially different in cases and controls (Table 3.4; Wacholder, Silverman, McLaughlin, & Mandel, 1992b). Matching consists of selecting controls based on one or more characteristics of cases, such as sex, age, and socioeconomic status. This strategy increases statistical efficiency and tends to decrease bias associated with well-known confusion factors (Lazcano-Ponce et al., 2001). However, some authors state that the improvement is typically small, except for strong confounders (Wacholder, et al., 1992b). One example from the road traffic injury literature is the study published by Haddon et al. in 1961. They demonstrated the important role that alcohol plays in pedestrian injuries. They measured alcohol levels in fatally injured pedestrians and in randomly selected persons at the same places, walking at the same time of day, on same day of the week, and moving in the same direction as the fatally injured. Consequently, environmental factors were the same for the cases and the controls and did not account for differences in alcohol levels found in the cases and controls (Robertson, 1992). 3.3.2.1. Forms of Matching and Stratification There are two forms of matching: individual matching and frequency matching (Lazcano-Ponce et al., 2001). Individual matching refers to the selection of one or more controls who have exactly or approximately the same value of the matching factor as the corresponding case. The matching factor should not be the exposure under study. Frequency matching or quota matching results in equal distributions of the matching factors in the cases and the selected controls (Wacholder, et al., 1992b). Because cases and controls have similar matching factors, differences in health outcomes may be attributed to other factors (dos Santos-Silva, 1999a). 3.3.2.2. Disadvantages of Matching Matching has some disadvantages as well. In some cases, matching adds more costs and complexity to a sampling scheme by requiring extra effort to recruit controls. In addition, this strategy may result in the exclusion of cases when no matched control can be found, particularly when matching on several variables (Wacholder, et al., 1992b). Matching may also delay a study when cases have to be identified and complex matching variables have be

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Theories, Concepts, and Methods

TABLE 3.4 Reasons to Match Reason

Justification

Control of unmeasured confounders

Identifiable but not quantifiable variables with many categories, such as neighborhood or telephone exchange, can serve as proxies for environmental or socioeconomic confounding factors that are difficult to measure. Matching on such a variable may balance cases and controls with respect to unknown confounders.

Increase statistical power

Matching can ensure that there are sufficient controls to estimate an effect in a particular subgroup or to identify an interaction.

Time comparability

In unmatched studies, it can be difficult to achieve time comparability between cases and controls for exposures that vary over time.

Feasibility

Matching may be the most feasible method of obtaining controls.

Completeness of control for confounding

Perfect matching, followed by a matched analysis, results in complete control for a continuous confounder under a multiplicative model of the joint effects. Alternative strategies, such as regression adjustment for the confounder, can result in bias if its effect is misspecified (e.g., if linearity is wrongly assumed). Categorization may leave some residual confounding, but this is of little importance unless there is a substantial gradient in risk within strata.

Efficiency

Matching reduces the possibility of severe loss of efficiency due to a major discrepancy in the empiric distributions of a strong risk factor between cases and controls. Matching should be considered only for risk factors whose confounding effects need to be controlled for but that are not of scientific interest as independent risk factors in the study. Matching on variables that are unrelated to risk of disease is pointless; it can only reduce a study’s efficiency. Age, sex, and race are often used as matching variables because they are usually strong confounders and because their effects are usually well-known from descriptive epidemiology.

Source: Wacholder, Silverman, et al. (1992b).

obtained for cases and potential controls before control selection can be performed (Wacholder, et al., 1992b). In addition, matching may also present methodological problems. When controls are selected based on one characteristic that tends to hide the association between disease and the exposure of interest, the overmatching problem arises (dos Santos-Silva, 1999a). Matching on a factor that is a surrogate for or a consequence of disease or matching on a correlate of an imperfectly measured exposure can also lead to overmatching and bias (Wacholder, et al., 1992b). In general terms, “overmatching” refers to a matching that is counterproductive by either causing bias or reducing efficiency. Matching on an intermediate variable in a causal pathway between exposure and disease can bias a point estimate downward because the exposure’s effect on disease, adjusting for (conditional on) the intermediate variable, is less than the unadjusted effect (Wacholder, et al., 1992). Overmatching can occur even when matching per se was not used in the selection of controls, such as when an overly homogeneous population base is used for a specific study (Wacholder, et al., 1992b). Therefore, if the role of a variable is doubtful, the best strategy is not matching but, rather, adjusting its effect in statistical analysis (dos Santos-Silva, 1999a). Stratified or matched analyses can be considered even when there is no matching or stratification in the design. However, matching at the design stage reduces the investigator’s flexibility

during the analysis (Wacholder, et al., 1992b) because the effect of matching factors can no longer be studied (dos Santos-Silva, 1999a). Selection of people engaged in the same activity at the same site, time of day, day of week, etc. may not be possible for activities that occur at the case sites infrequently, such as use of “all-terrain” vehicles or snowmobiles. A child injured in a pedestrian collision may have no siblings close enough in age to serve as controls within the household, although children in reasonable proximity in the same neighborhoods may serve as controls depending on the factors of interest (Robertson, 1992).

3.3.3. Number of Controls 3.3.3.1. Ratio of Controls to Cases Determination of the number of controls is another important decision when designing a caseecontrol study. It is useful to consider the ratio of controls to cases. Wacholder, et al. (1992b) argue that there is usually little marginal increase in precision when the ratio of controls to cases is increased beyond four, except when the effect of exposure is large. In general, the best way to increase precision in a caseecontrol study is to increase the number of cases by widening the base geographically or temporally rather than by increasing the number of controls because the marginal increase in precision from an additional case is

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CaseeControl Studies in Traffic Psychology

37

greater than that from an additional control (assuming there are already more controls than cases in the study). In matched and stratified studies, the most efficient allocation of a fixed number of controls into strata is usually one that sets the ratio of controls to cases to be approximately equal (Wacholder, et al., 1992b). 3.3.3.2. Number of Control Groups Some researchers have suggested choosing more than one control group when one of them has advantages that are missing from the other and vice versa (Rothman & Greenland, 1998). It certainly is reassuring when the results are concordant across control series. The problem is when results are discordant because investigators must decide which result is “correct” and essentially discard the other (Wacholder, et al., 1992b). Wacholder et al. suggest that usually the best strategy is to decide which control series is preferable at the design stage. However, multiple control groups might be helpful when each serves a different purpose, such as when each control group provides the ability to control for a particular confounder. In this situation, the second control group can act as a form of replication. 3.3.3.3. One Control Group for Several Diseases Use of a single control group for more than one case series can lead to savings of money and effort. Systematic errors in assembling the control series would presumably affect each individual series equally, but the availability of a larger number of controls would increase the precision of point estimates (Wacholder, et al., 1992b).

3.4. Analysis in CaseeControl Studies: Measures of AssociationeCausality Caseecontrol studies use the odds ratio (OR) as a measure to evaluate the strength of association between a factor (exposure) and the event (health condition or disease) under study. This measure indicates the relative frequency of exposure between cases and controls, as shown in Figure 3.2. The quotient of OR of exposure in cases and the OR of exposure in controls corresponds to the OR of exposure. In this type of study, the incidence of disease cannot be estimated both in exposed and in nonexposed individuals because they are selected based on the presence or absence of the condition under study and not by their exposure status (with the exception of some variants of caseecontrol studies, such as the nested caseecontrol, the caseecohort, and the caseecrossover designs). On the other hand, although relative risk is not directly calculated, when frequency of disease is low, OR is a nonbiased estimator of the incidence rate ratio or relative risk (Lazcano-Ponce et al., 2001).

FIGURE 3.2 Analysis of a nonmatched caseecontrol study. Source: Herna´ndez-A´vila and Lo´pez-Moreno (2007).

Odds ratio values oscillate between 0 and infinite. The OR obtained from a caseecontrol study indicates how many times higher (when OR is >1) or lower (when OR is <1) the probability is that cases have been exposed to a factor under study compared to controls (dos Santos-Silva, 1999a). The former are considered risk factors (i.e., increases in the likelihood of having the characteristic under study) and the latter protective factors (i.e., decreases in the likelihood of developing the phenomenon under study). Once the OR is estimated, it is useful to calculate a measure of variability to that point estimation. The confidence interval indicates a range within which the true value of association is estimated to lie if such value would have been obtained from the total population. The confidence interval (i.e., 95%) determines how likely the interval is to contain the point estimation. If the confidence interval includes 1, the association is not statistically significant. At a given level of confidence, and all other things being equal, a result with a smaller confidence interval is more reliable than a result with a larger one. In this sense, sample size is an important determinant of the width and precision of a confidence interval in the estimation procedure. Figure 3.2 shows how to calculate these measures. Odds ratios are commonly interpreted as a measure of casual relation between exposure and the event under

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PART | I

study. For this to be true, some conditions are needed. Controls should be part of and be a representative sample of the same base population as cases. Most caseecontrol studies are retrospective, and as a result, a perfect causal relation cannot always be verified because the disease/ health condition is evaluated before exposure (Herna´n´ vila & Lo´pez-Moreno, 2007). This situation also dez-A

Theories, Concepts, and Methods

introduces other errors or bias (Lazcano-Ponce et al., 2001).

3.5. Subtypes of CaseeControl Studies Table 3.5 describes some of the most important variants of caseecontrol studies in epidemiological research.

TABLE 3.5 Variants in CaseeControl Studies Subtype

Characteristics

Caseecohort studies

Studies in which the source population is a cohort and every person in the cohort has an equal chance of being included in the study as a control, regardless of how much time that individual has contributed to the person-time experience of the cohort. This is a logical way to conduct a caseecontrol study when the effect measure of interest is the ratio of incidence proportions rather than a rate ratio. Paralleling the earlier development, the average risk (or proportion) of falling ill during a specified risk period may be written. An advantage of the caseecohort design is that it allows one to conduct a set of caseecontrols studies from a single cohort, all of which use the same control group. Just as one can measure the incidence rate of a variety of diseases within a single cohort, one can conduct a set of simultaneous caseecohort studies using a single control group.

Nested caseecontrol studies

Studies that use a risk groups sampling approach to identify cases of a disease that occur in a defined cohort and, for each, a specified number of matched controls is selected from among those in the cohort who have not developed the disease by the time of disease occurrence in the case. Sampling could be assumed as nested inside a dynamic cohort, where study subjects remain in the cohort for variable time and where exposure could take different values over time.

Cumulative (“epidemic”) caseecontrol studies

Studies that are aimed at addressing a risk that ends before subject selection begins (i.e., some epidemic diseases). In such a situation, an investigator might select controls from that portion of the population that remains after eliminating the accumulated cases; that is, one selects controls from among noncases (those who remain free of disease at the end of the epidemic).

Case-only studies

Studies in which cases are the only subjects used to estimate or test hypotheses about effects. For example, it is sometimes possible to employ theoretical considerations to construct a prior distribution of exposure in the source population and to use this distribution in place of an observed control series. Such situations naturally arise in genetic studies, in which basic laws of inheritance may be combined with certain assumptions to derive a population- or parental-specific distribution of genotypes. It is also possible to study certain aspects of joint effects (interactions) of genetic and environmental factors without using control subjects.

Caseecrossover studies

Studies that use one or more (predisease) time periods as matched “control periods” for the case. The exposure status of the case at the time of the disease onset is compared with the distribution of exposure status for the same individual in the earlier periods. Such a comparison depends on the assumption that neither exposure nor confounders are changing over time in a systematic way. Only a limited set of research topics are amenable to the caseecrossover design. The exposure must vary over time within individuals rather than stay constant. Like the crossover study, the exposure must also have a short induction time and a transient effect; otherwise, exposures in the distant past could be the cause of a recent disease onset (the “carryover” effect).

Two-stage sampling

Studies in which the control series comprises a relatively large number of individuals (possible everyone in the source population), from whom exposure information or perhaps some limited amount of information on other relevant variables is obtained. Then, for a subsample of the controls (or cases), more detailed information is obtained on some variables. It is useful when it is relatively inexpensive to obtain the exposure information but the covariate information is more expensive to obtain; when exposure information has already been collected on the entire population, but covariate information is needed; and in cohort studies when more information is required than was gathered at baseline.

Proportional mortality studies

Studies in which cases are deaths occurring within the source population. Controls are not selected directly from the source population. This control series is acceptable if the exposure distribution within this group is similar to that of the source population. Consequently, the control series should be restricted to categories of death that are not related to the exposure.

Source: Lazcano-Ponce et al. (2001) and Rothman and Greenland (1998)

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CaseeControl Studies in Traffic Psychology

3.6. Problems with CaseeControl Studies 3.6.1. Bias Because caseecontrol studies are commonly retrospective, they are particularly vulnerable to the introduction of errors in the process of selection and information gathering. For this reason, these studies are not considered to be appropriate for finding causal effects (Table 3.6; ´ vila & Lo´pez-Moreno, 2007). However, in Herna´ndez-A some instances, caseecontrol studies can be prospective, increasing their potential to study causality (Herna´ndez´ vila & Lo´pez-Moreno, 2007). A Error in the measurement of variables is unavoidable in epidemiologic studies, particularly when information is obtained retrospectively (Wacholder, et al., 1992). With nondifferential errors, the bias is typically (but not always) in a predictable direction (toward lack of association) and, unless the measurement is so bad as to be negatively correlated with the truth, seldom reverses the direction of the association. On the other hand, the effect of differential measurement error on estimates of association is usually unpredictable (Wacholder, et al., 1992). A widespread concern about survey-based caseecontrol studies is that cases recall previous exposures differently than do controls. Cases may spend time thinking about possible reasons for their illness, may search their memories for past exposure or even exaggerate or fabricate exposure, or may try to deny any responsibility for the disease. Therefore, some suggest using control groups of diseased subjects to achieve equal accuracy. Although accuracy of information and how that accuracy differs between cases and controls are considerations in the choice of control group, one must also be concerned about choosing controls with conditions possibly related to exposure (Wacholder, et al., 1992b). The degree of

TABLE 3.6 Sources of the Most Commonly Observed Bias in CaseeControl Studies Selection bias Nonresponse Information bias Measurement error Bias by the interviewer (observer bias) Interviewee bias Memory bias (recall bias) Exposure bias Confusion bias Source: Hernandez-Avila et al. (2000).

39

accuracy in measuring the exposure of interest for the cases should be equivalent to the degree of accuracy for the controls, unless the effect of the inaccuracy can be controlled in the analysis (comparably accuracy principle) (Wacholder, et al., 1992). The efficiency principle can conflict with the deconfounding principle when controls are selected to have the same values of confounders as cases, thus restricting the variability of the confounding variables. This in turn reduces the precision of estimates of effect. When control of confounding is essential for bias reduction, the efficiency principle must be subordinated (Wacholder, et al., 1992). A study carried out to quantify the relationship between acute alcohol consumption and risk of injury (Watt, Purdie, Roche, & McClure, 2004), in the context of other potential confounding factors (i.e., usual alcohol intake, risk-taking behavior, and substance use, defined as use of prescription/ over-the-counter medication or illicit substances), used three separate measures of alcohol consumption. A hospital-based caseecontrol study was used in which 488 cases were matched to 488 population controls on gender, age group, neighborhood, and day and time of injury. It was concluded that acute alcohol consumption significantly increased the risk of injury, even when situational and other risk factors were considered. It is important to note that Watt et al. (2004) did not report the effect of the mix of illnesses seen in the hospital on alcohol use or the effect of alcohol use on seeking medical care for injury or illness. Thus, the extent of overor underestimation of alcohol’s effect on injury is uncertain. If alcohol contributes to the problem presented by the control patients or to the probability of seeking medical attention, the difference in alcohol measured between cases and controls could be less than if persons exposed to the circumstances of injury who had no reason to seek medical attention were chosen as controls. If the medical condition is such that the control patients would not have been engaged in activities similar to those of the injured people, the effect of aspects of those activities would be overestimated (Robertson, 1992). In addition, the relationship between alcohol and injury appears to be confounded in Watt et al.’s (2004) study by usual drinking patterns, risk-taking behavior, and substance use. One way of studying the factors would be to replicate these caseecontrol studies and measure hypothesized biological factors that might contribute to alcohol use, other behaviors, or both jointly. Two major problems would be encountered in such a study. First, although measurement of alcohol in breath samples of controls is seldom resisted by persons selected as controls, requests for blood or other biological specimens might not be acceptable. Second, reliable evidence that trauma does not change the hypothesized biological factor is necessary before assuming that a difference between cases and controls is indicative of

40

PART | I

causation (Robertson, 1992). For example, research was done to measure state anger and the risk of injury where cases were patients seeking care for an acute injury. They were compared with two controls: the patient himself or herself 24 h earlier and an individual recruited by telephone from the community and matched for age group, sex, and time. Self-reported anger was assessed with three Likert scale items. Anger just before the injury was compared in caseecrossover analyses with the respondent’s own level of anger 24 h earlier and, as in caseecontrol analyses, with community participants’ level of anger at the same hour of the same day of the week during a subsequent week. Anger was not associated with fall and traffic injuries, but anger was strongly associated with intentional injuries inflicted by another person in both men and women (Vinson & Arelli, 2006).

Theories, Concepts, and Methods

3.6.2. Is Representativeness a Problem in CaseeControl Studies? Although much emphasis has been given to the necessity that selected cases should be representative of all cases, as noted by Rothman and Greenland (1998), such advice can be misleading, given that sometimes cases (and thus controls) may be restricted to any type of case that may be of interest for research. Studies may focus only on women or only on the most severe cases or even specific groups of a population (elderly, schoolchildren, etc.). In this sense, case definition will implicitly define the source population for cases, from which the controls should be drawn. It is this source of population for the cases that the controls should represent, not the entire nondiseased population. Achieving representativeness in this context would be

TABLE 3.7 Advantages and Disadvantages of CaseeControl Studies Advantage

Disadvantage

For diseases that are sufficiently rare, caseecontrol studies are an efficient and useful alternative (Lazcano-Ponce et al., 2001; Rothman & Greenland, 1998). This is also the case for diseases with prolonged latency periods (dos Santos-Silva, 1999a; Herna´ndez´ vila & Lo´pez-Moreno, 2007; Lazcano-Ponce et al., 2001). A

For exposures that are extremely rare, caseecontrol studies are not efficient (Rothman & Greenland, 1998) unless exposition is responsible for a large proportion of cases (high population attributable fraction) (dos Santos-Silva, 1999a).

Relatively easy to perform (Rothman & Greenland, 1998).

Sometimes it is difficult to define the base population from which ´ vila & Lo´pez-Moreno, 2007). cases are drawn (Herna´ndez-A

Not extremely expensive nor time-consuming, especially compared ´ vila & to cohort studies (dos Santos-Silva, 1999a; Herna´ndez-A Lo´pez-Moreno, 2007; Rothman & Greenland, 1998).

Given that exposure is measured, quantified, or reconstructed retrospectively in most of the cases, information bias is common (Lazcano-Ponce et al., 2001). This is sometimes due to problems in precise measurement of exposition levels (exposure bias) (dos ´ vila & Lo´pez-Moreno, 2007). Santos-Silva, 1999a; Herna´ndez-A

Require fewer subjects under study than other epidemiological study designs. For example, prospective cohort studies would require the inclusion of a larger number of individuals and a longer follow-up period to ensure the inclusion of a sufficient number of cases (dos Santos-Silva, 1999a).

Selection bias is common (dos Santos-Silva, 1999a; Herna´ndez´ vila & Lo´pez-Moreno, 2007; Lazcano-Ponce et al., 2001). There A are a variety of reasons for this, including the following: l Difficulty of finding an adequate control group l If exposure of interest determines selection of cases and controls in a different manner (diagnostic bias)

Several expositions or risk factors of the disease or health condition under study can be analyzed at the same time (dos Santos-Silva, ´ vila & Lo´pez-Moreno, 2007; Lazcano-Ponce 1999a; Herna´ndez-A et al., 2001).

It is not possible to directly estimate incidence or prevalence for both ´ vila & Lo´pez-Moreno, exposed and nonexposed (Herna´ndez-A 2007).

Allows the estimation of true relative risk if and when representativeness, simultaneity, and homogeneity assumptions are met (Lazcano-Ponce et al., 2001).

Temporality between exposure and disease could be difficult to establish (reverse causality) (dos Santos-Silva, 1999a; Herna´ndez´ vila & Lo´pez-Moreno, 2007). A Lack of representativeness (except when the study is population based) (dos Santos-Silva, 1999a). Not useful when disease under study is measured continuously (Lazcano-Ponce et al., 2001). If condition of interest is highly prevalent (more than 5%), the odds ratio is not a confident estimation of risk ratio (Lazcano-Ponce et al., 2001).

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CaseeControl Studies in Traffic Psychology

neither easy nor desirable, and a perfectly valid caseecontrol study would be possible (Rothman & Greenland, 1998). The study base principle entails the requirement of representativeness of the base but not necessarily of the general population. Representativeness of the general population is crucial in estimating the prevalence of disease, the attributable risk, or the distribution of a variable in a population based on a sample. However, representativeness per se is not needed in analytical studies of the relation between an exposure and disease. An association found in any subpopulation may be of interest in itself; in a representative population, an association that is limited to one group may be obscured because the effect is weaker in other groups or because of differences in the distribution of the exposure. On the other hand, detection of variability of the strength of association (effect modification) can be missed if the study base is narrowly defined. If there is reason to believe that an effect is strongest in one particular subgroup, exclusion of other subgroups might be the best strategy for demonstrating that effect; thus, a study of the effect of a possible risk factor for myocardial infarction might restrict the base to subjects who had a previous one (Wacholder, et al., 1992).

3.7. Advantages and Disadvantages of CaseeControl Studies Table 3.7 highlights the most important advantages and disadvantages of caseecontrol designs that should be taken into account when determining which type of epidemiological approach to use.

4. CONCLUSIONS Throughout the world, no matter the level of motorization, there is a need to improve road safety for all road actors to reduce current inequalities and the risk of road traffic injuries. Road safety efforts must be evidence based, fully funded, properly resourced, and sustainable. The best way to achieve this is through research that requires an approach that includes various key elements such as policy makers, decision makers, professionals, and practitioners who recognize that the traffic injury problem is an urgent one but one for which solutions are already largely known. It will require that road safety strategies be integrated with other strategic, and sometimes competing, goals, such as those relating to the environment and to accessibility and mobility. Among the many research-related needs for road injury prevention, it is necessary to encourage the development of professional expertise across a range of disciplines at the national level along with regional cooperation and exchange of information to achieve maximum benefit.

41

Developing such expertise should be a priority where it does not exist. This chapter sufficiently clarifies the advantages and limitations of using caseecontrol designs. They are important for researching the causes of road traffic injuries and for determining the following: l l l

Causes and correlates of road crash injury; Factors that increase or decrease risk; Factors that might be modifiable through interventions.

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Khayesi, M. (2003). Liveable streets for pedestrians in Nairobi: The challenge of road traffic accidents. In J. Whitelegg, & G. Haq (Eds.), The Earthscan reader on world transport policy and practice (pp. 35e41). London: Earthscan. Koepsell, T., McCloskey, L., Wolf, M., Moudon, A. V., Buchner, D., Kraus, J., et al. (2002). Crosswalk markings and the risk of pedestrianemotor vehicle collisions in older pedestrians. Journal of the American Medical Association, 288(17), 2136e2143. Kraus, J. F., Hooten, E. G., Brown, K. A., Peek-Asa, C., Heye, C., & McArthur, D. L. (1996). Child pedestrian and bicyclist injuries: Results of community surveillance and a caseecontrol study. Injury Prevention, 2(3), 212e218. Lazcano-Ponce, E., Salazar-Martinez, E., & Hernandez-Avila, M. (2001). Caseecontrol epidemiological studies: Theoretical bases, variants and applications. Salud Publica de Mexico, 43(2), 135e150. Lightstone, A. S., Peek-Asa, C., & Kraus, J. F. (1997). Relationship between driver’s record and automobile versus child pedestrian collisions. Injury Prevention, 3(4), 262e266. Mayou, R., & Bryant, B. (2003). Consequences of road traffic accidents for different types of road user. Injury, 34(3), 197e202. Muhlrad, N. (1998). Vulnerable road users in urban traffic: Some conclusions of an OECD expert group. Paper presented at the fourth World Conference on Injury Prevention and Control, Amsterdam. Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A., & Jarawan, E.et al. (Eds.). (2004). World report on road traffic injury prevention. Geneva: World Health Organisation. Razzak, J. A., & Luby, S. P. (1998). Estimating deaths and injuries due to road traffic accidents in Karachi, Pakistan, through the capturee recapture method. International Journal of Epidemiology, 27(5), 866e870. Roberts, I., & Norton, R. (1995). Sensory deficit and the risk of pedestrian injury. Injury Prevention, 1(1), 12e14. Robertson, L. S. (1992). Injury epidemiology. New York: Oxford University Press. Rothman, K. J., & Greenland, S. (1998). Modern epidemiology (2nd ed.). Philadelphia: Lippincott-Raven. Stevenson, M., Jamrozik, K., & Burton, P. (1996). A caseecontrol study of childhood pedestrian injuries in Perth, Western Australia. Journal of Epidemiology and Community Health, 50(3), 280e287. Tester, J. M., Rutherford, G. W., Wald, Z., & Rutherford, M. W. (2004). A matched caseecontrol study evaluating the effectiveness of speed

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Theories, Concepts, and Methods

humps in reducing child pedestrian injuries. American Journal of Public Health, 94(4), 646e650. Tingvall, C. (1997). The zero vision. In H. van Holst, A. Nygren, & R. Thord (Eds.), Transportation, traffic safety and health: The new mobility (8th ed.). (pp. 35e57) Berlin: Springer-Verlag. Vinson, D. C., & Arelli, V. (2006). State anger and the risk of injury: A caseecontrol and caseecrossover study. Annals of Family Medicine, 4(1), 63e68. Vinson, D. C., Maclure, M., Reidinger, C., & Smith, G. S. (2003). A population-based caseecrossover and caseecontrol study of alcohol and the risk of injury. Journal of Studies on Alcohol, 64(3), 358e366. von Kries, R., Kohne, C., Bohm, O., & von Voss, H. (1998). Road injuries in school age children: Relation to environmental factors amenable to interventions. Injury Prevention, 4(2), 103e105. Wacholder, S., McLaughlin, J. K., Silverman, D. T., & Mandel, J. S. (1992). Selection of controls in caseecontrol studies: I. Principles. American Journal of Epidemiology, 135(9), 1019e1028. Wacholder, S., Silverman, D. T., McLaughlin, J. K., & Mandel, J. S. (1992a). Selection of controls in caseecontrol studies: II. Types of controls. American Journal of Epidemiology, 135(9), 1029e1041. Wacholder, S., Silverman, D. T., McLaughlin, J. K., & Mandel, J. S. (1992b). Selection of controls in case-control studies: III. Design options. American Journal of Epidemiology, 135(9), 1042e1050. Wang, S. Y., Chi, G. B., Jing, C. X., Dong, X. M., Wu, C. P., & Li, L. P. (2003). Trends in road traffic crashes and associated injury and fatality in the People’s Republic of China, 1951e1999. Injury Control and Safety Promotion, 10(1e2), 83e87. Watt, K., Purdie, D. M., Roche, A. M., & McClure, R. J. (2004). Risk of injury from acute alcohol consumption and the influence of confounders. Addiction, 99(10), 1262e1273. Wells, S., Mullin, B., Norton, R., Langley, J., Connor, J., Lay-Yee, R., et al. (2004). Motorcycle rider conspicuity and crash related injury: Caseecontrol study. British Medical Journal, 328(7444), 857. Wintemute, G. J., Kraus, J. F., Teret, S. P., & Wright, M. A. (1990). Death resulting from motor vehicle immersions: The nature of the injuries, personal and environmental contributing factors, and potential interventions. American Journal of Public Health, 80(9), 1068e1070. Yu, J. M., Wang, Y. C., & Chen, F. (2005). A caseecontrol study on roadrelated traffic injury in Shanghai. Zhonghua Liu Xing Bing Xue Za Zhi, 26(5), 344e347.

Chapter 4

Self-Report Instruments and Methods ¨ zkan Timo Lajunen and Tu¨rker O Middle East Technical University, Ankara, Turkey

1. INTRODUCTION In recent decades, the number of social psychological studies in traffic research has increased drastically. The popularity of self-reports has also increased; for example, a SCOPUS literature search (October 15, 2010) returned three times more studies for the time period 2000e2009 including the search word “Questionnaire” than for 1990e1999. Because social psychological studies are mostly based on self-reports, increased interest in social psychological factors has also resulted in the increased use of self-report methodology. Self-reports include a great variety of different methods, including questionnaires and inventories, interviews, focus groups (Basch, DeCicco, & Malfetti, 1989; Kua, Korner-Bitensky, & Desrosiers, 2007), and driving diaries (Gulian, Glendon, Matthews, Davies, & Debney, 1990; Joshi, Senior, & Smith, 2001; Kiernan, Cox, Kovatchev, Kiernan, & Giuliano, 1999). Common features in all these diverse self-report measures are that participants are aware that they are participating in a study; they are asked to actively reply to more or less structured questions; and their responses are taken as “face valid”dthat is, answers are scored and analyzed based on the responses and not, for example, according to response time or other behavioral or physiological measurement. In self-reports, the content of the responses in this way is assumed to reflect a respondent’s reality. Self-reports and especially questionnaires have many advantages. They are usually less expensive than studies using an instrumented vehicle or a simulator, they provide more detailed information than observations, and they can reach large numbers of people. Representativeness of the sample is easy to establish and can be measured with direct statistical comparisons to driver populations. Moreover, the reliability of items and measurements can be easily evaluated with standard statistics. Due to large samples, complicated and detailed statistical analyses can be conducted. The advantages of self-report-based survey studies are clear. However, self-reports also have some serious shortcomings that should be taken into account when Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10004-9 Copyright Ó 2011 Elsevier Inc. All rights reserved.

planning a study. This chapter discusses the possible problems in using survey methods in traffic research. It concentrates on questionnaire studies because the survey questionnaire is the most often applied research method and, thus, a good example for this overview. Driving diaries and logs, as well as interviews, share the same problems with questionnaires but also have serious problems of their own. Therefore, this chapter concentrates on questionnaires as self-reports. Most of the examples are from Driver Behaviour Questionnaire (DBQ) literature because the DBQ (Reason, Manstead, Stradling, Baxter, & Campbell, 1990) is one of the most widely used instruments for measuring driver behaviors and, thus, provides good demonstration material. Note, however, that most of the DBQ-related concerns are serious problems of any road behavior measure based on self-reports.

2. FOR WHAT KIND OF TRAFFIC RESEARCH CAN SELF-REPORT BE USED? A literature search shows that self-report methodology has been used for a wide variety of research, including attitudes, opinions, beliefs, emotions, cognitive processes, and behaviorsdbasically any aspect of driving. While acknowledging that self-reports can be the most appropriate tool or even the only tool for many research purposes (e.g., for measuring attitudes, beliefs, and opinions), selfreports are often misused. Especially self-reports of past accidents, near misses, mileage, and driver behavior can be misleading or biased.

2.1. Two Components of Driving: Driver Performance and Behavior Driving can be seen as being composed of two separate components, driving skills and driving style (Elander, West, & French, 1993), or, in other words, driver performance and driver behavior (Evans, 1991). Driving skills include those information-processing and motor skills that may be expected to improve with practice and training, i.e., with 43

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PART | I

driving experience (Elander et al., 1993). In addition to learning, driving-related skills can be considered to be affected by a driver’s general information-processing ability. The role of the driver’s general information-processing and motor ability is emphasized when some of those skills have declined either temporarily (e.g., driving while intoxicated) or permanently (e.g., Alzheimer’s disease). The driving task can be described as a skilled activity with several distinct levels that are organized hierarchically (Summala, 1996). This hierarchy, from bottom to top, includes the control (operational), maneuvering (guidance), and planning (navigational) levels (Michon, 1985; Van Der Molen & Botticher, 1988). In the beginning, all these functions need conscious control, but gradually with more practice and driving experience, these functions become increasingly automated (Summala, 1987). In this learning process, basic motor skills are acquired quickly, whereas the development of perceptual skills is slower. For example, beginner drivers learn to use the manual gear and clutch rather quickly but are slow to learn to use their peripheral vision for lane keeping (Mourant & Rockwell, 1972). In terms of measurement with self-reports, it is important to understand that drivers are unaware of most of the automated processes that they perform while driving. Driving style concerns individual driving habitsdthat is, the way a driver chooses to drive. Driving style becomes established over a period of years but does not necessarily get safer with driving experience (Elander et al., 1993). Practice and increased exposure to the diversity of traffic situations result in improved skills but also increased subjective control of driving, less concern for safety, and habitually driving with narrow safety margins (Na¨a¨ta¨nen & Summala, 1976; Spolander, 1983; Summala, 1985). In fact, it has been reported that some safety-related skills,

Theories, Concepts, and Methods

such as scanning patterns and the keeping of adequate safety margins, fail to improve or even deteriorate once explicit tuition is removed and the feedback is not consistent as it is with many other safety-related skills (Duncan, Williams, & Brown, 1991). Not even the specific defensive driving courses focused on anticipatory and safety-oriented driving habits have an effect on drivers’ accident involvement (Lund & Williams, 1985). Because driving is to some extent a “self-paced” task and drivers actually determine their own margin of error, driving style can be assumed to reflect drivers’ individual personality characteristics, attitudes, and motives. In addition, the effect of motivational factors and “extra-motives” (Na¨a¨ta¨nen & Summala, 1976) on driving safety is more remarkable in some driver groups than in others. For example, young male drivers tend to take unnecessary risks more frequently than do young female drivers (Evans, 1991). Drivers can, at least in part, influence their subjective crash risk by deliberately adopting a safe driving style that gives them large safety margins (Na¨a¨ta¨nen & Summala, 1976; Summala, 1980). Figure 4.1 shows how driving skills (driving performance) and driving style (driver behavior) are related to the likelihood of errors and size of the safety margin and, finally, to crashes. In this model, errors and safety margin are treated as outcomes of behavior and skills. The “driving skill” (or driver performance) pathway (dotted arrows) describes how driving experience as exposure to a variety of traffic situations and as practice is related to driving skills, which in turn determine the probability of a driver error. The “driving style” pathway in Figure 4.1 describes how driving experience, personality factors, attitudes and beliefs, and lifestyle are related to driving style (or driving behavior), which in turn influences the sizes of safety margins: The more risky the driving style, the narrower

General cognitive abilities Driving skills Driving experience

Errors

Driving style

Lifestyle

Crash Safety margins

Personality factors Attitudes and beliefs FIGURE 4.1 Two pathways to crash

Chapter | 4

Self-Report Instruments and Methods

margins the driver accepts. Finally, frequent driving errors due to lacking skills, and narrow safety margins, together lead to heightened crash risk. The literature on psychological factors associated with differential traffic crash involvement indicates that both driving skills and driving style are related to crash risk (Elander et al., 1993). Drivers’ maximum performance capabilities do not necessarily predict their accident involvement and, conversely, bad attitudes do not alone cause drivers to lose vehicle control in a curve. Because driving is a self-paced task and drivers can largely determine the task demands, a risky driver actually makes the driving task too difficult for himself or herself so that the demands exceed his or her capabilities. Effective countermeasures should therefore include both the driving skill and style components, and these components should be seen as related to each other.

2.2. Measuring Driver Behavior and Performance with Self-Reports: Driver Behavior Questionnaire and Driver Skill Inventory Although in the literature self-reports have been used for measuring both driving skills (performance) and driving style (driver behavior), we believe that self-report methodology fits better to studies of driver behavior than performance for several reasons. First, driver behavior refers to driving styledthat is, the everyday way of driving that the driver prefers. For example, preferred speed, following distance, and rule obedience are all voluntary behaviors about which drivers are mostly aware. Drivers’ awareness of their driving skills can be assumed to be much lower because basic motor and perceptual processes are automatic and do not need attention. Exposure as learning means that experienced drivers are probably even less aware of their skills than are novices because controlling the vehicle requires conscious attention only in especially demanding situations. For example, shifting gears becomes automatic in the very early stages of learning to drive; thus, the experienced driver is no longer aware of his or her skill level in changing gears. Second, driver behavior consists of behaviors that are often included in highway code or unwritten norms. For example, speeding and driving while intoxicated violate both the traffic code and social norms of driving. Rudeness or aggressive driving might not be illegal but still violates social norms. Because of this normative character of most of the driver behaviors, drivers actually know what the ideal normative behavior would be and can compare their behavior to those norms (e.g., try to drive just slightly above the speed limit). Unlike driver behavior, it is usually difficult to determine the “skill norms” of driving, and drivers are mostly unaware of their skill level. The only

45

time when drivers might realize shortcomings in their skills is when an error leads to some negative consequences, such as a crash or car engine stalling because the driver selected a wrong gear. Because of these two issues, high awareness level and existence of an absolute norm, self-reports fit better to studies of driver behavior than to those of driver performance. Of course, drivers’ views of their skills can be studied with self-reports, but direct measurement of real driving skills with a self-report is practically impossible. The problems of self-reports in the measurement of driving skills (performance) and driver behavior can be well demonstrated by comparing the Driver Behavior Inventory (DBQ) and the Driver Skill Inventory (DSI). The DBQ is based on a theoretical taxonomy of aberrant behaviors (Reason et al., 1990). The main distinction between errors and violations is based on the assumption that they have different psychological origins and demand different modes of remediation (Reason et al., 1990). Errors are the result of cognitive processing problems, whereas violations include a motivational component and contextual demands. Errors were defined by Reason et al. as “the failure of planned actions to achieve their intended consequences” (p. 1315) and differentiated into slips and lapses and mistakes. Violations referred to “deliberate deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system” (p. 1316). In their first study on DBQ, Reason et al. found that driver errors and violations are two empirically distinct classes of behavior comprising three factors (deliberate violations, dangerous errors, and “silly” errors). Later, Parker, Reason, Manstead, and Stradling (1995) confirmed the three-factor structure of the DBQ. Since the publication of the original article by Reason et al. (1990), the DBQ has become one of the most widely used methods for measuring self-reported driving behaviors. A metaanalytical review by de Winter and Dodou (2010) reported 174 studies in which the DBQ was used either in original or in modified form. Cross-cultural studies have demonstrated the universality of the distinction between errors and ¨ zkan, violations (Lajunen, Parker, & Summala, 2004; O Lajunen, Chliaoutakis, Parker, & Summala, 2006). Erroreviolation distinction seems to also be sample invariant because it has been found among different driver groups, such as professional drivers, motor riders, traffic offenders, probationary drivers, parentechild pairs, young women, and older drivers (de Winter and Dodou, 2010). The DBQ items (abbreviated 19-item form) from a sixcountry study are listed in Table 4.1. By definition, the error items include items in which the driver makes a mistakedthat is, these behaviors are not intentional, although the consequences can be serious. The error items mostly describe situations in which the driver makes an attention or perceptual error (“fail to see pedestrians crossing” and “miss ‘give way’ sign”) or vehicle handling error (“brake

46

PART | I

Theories, Concepts, and Methods

TABLE 4.1 The Means of DBQ Items after Controlling the Effects of Age, Mileage, and Sex and ANOVA Results (F) in Finland (FIN), Great Britain (GB), Greece (GR), Iran (IRN), The Netherlands (NL), and Turkey (TR) DBQ Items (Item No.)

FIN

GB

GR

IRN

NL

TR

F7,1452

Aggressive violations

0.78

0.86

1.66

1.33

0.67

1.20

40.69***

Sound horn to indicate your annoyance (03)

1.00

1.29

2.39

1.75

1.07

1.89

40.73***

Get angry, give chase (11)

0.71

0.32

0.56

1.17

0.18

0.61

31.61***

Aversion, indicate hostility (17)

0.64

0.96

2.06

1.09

0.76

1.12

38.16***

1.21

1.20

0.88

1.21

1.19

0.94

11.33***

Pull out, force your way out (06)

0.34

0.99

0.62

0.79

0.54

0.58

16.77***

Disregard the speed limit on a residential road (07)

2.51

1.69

1.18

2.12

1.88

1.44

29.52***

Push in at last minute (12)

0.49

0.60

0.47

1.15

0.73

0.64

15.81***

Overtake a slow driver on the inside (13)

0.32

0.86

0.89

1.45

1.03

1.42

35.20***

Race from lights (14)

1.35

1.31

1.04

0.84

1.66

0.83

17.03***

Close following (15)

1.40

0.92

0.85

1.21

0.82

0.68

18.12***

Shooting lights (16)

1.09

0.85

0.66

0.77

0.55

0.63

10.30***

Disregard the speed limit on a motorway (19)

2.16

2.41

1.31

1.35

2.31

1.29

33.53***

0.53

0.52

0.62

1.02

0.56

0.73

35.31***

Queuing, nearly hit car in front (01)

0.62

0.68

0.59

1.12

0.55

0.67

13.80***

Fail to see pedestrians crossing (02)

0.80

0.47

0.67

1.10

0.59

0.63

14.73***

Fail to check your rearview mirror (04)

0.80

0.77

0.54

1.15

0.94

1.50

15.03***

Brake too quickly on a slippery road (05)

0.59

0.69

0.67

0.83

0.66

0.83

3.39**

Turning right nearly hit cyclist (08)

0.22

0.30

0.51

0.95

0.39

0.45

28.32***

Miss “give way” signs (09)

0.26

0.25

0.60

0.86

0.32

0.47

22.89***

Attempt to overtake someone turning left (10)

0.23

0.24

0.51

0.74

0.34

0.48

16.65***

Underestimate the speed of an oncoming vehicle (18)

0.74

0.75

0.84

1.45

0.67

0.81

23.34***

Ordinary violations

Errors

*p < 0.05; **p < 0.01; ***p < 0.001. Instruction: “How often, if at all, has this kind of thing has happened to you?” Answer scale: 0, never; 1, hardly ever; 2, occasionally; 3, quite often; 4, frequently; 5, nearly all the time. ¨ zkan, Lajunen, Chliaoutakis, et al. (2006). Source: Adapted from O

too quickly on a slippery road”). The other versions of the DBQ also include lapses, which largely involve failures of memory and are embarrassing but not dangerous (“forgot where you parked your car”). Hence, each of these error items requires the driver to (1) notice that he or she made an error, and (2) remember the error later when asked. The paradox in self-reports of attention or memory mistakes is that most of the errors go unnoticed. The DBQ lapses and error scales require a forgetful driver to recall his or her errors that he or she might not even have noticed. As Bjørnskau and Sagberg (2005) accurately noted, “Unconscious errors may be hard to remember precisely because they are unconscious” (p. 137). We can assume that drivers prone to cognitive failures and forgetting while

driving (e.g., elderly drivers and drivers with slight dementia) also have difficulties in remembering their mistakes when answering self-reports such as the DBQ. The DBQ is only one example; there are several even more demanding scales that measure the cognitive performance of the driver. The DSI (Lajunen & Summala, 1995) provides another example of measurement of driver skills (or errors) with self-reports. However, the DSI does not actually measure skills but, rather, measures a driver’s skill and safety orientation. Drivers are asked to evaluate themselves as drivers by indicating their strengths and weaknesses with regard to driving. Hence, the external criterion (compared to “other drivers”) or absolute criterion (frequency of

Chapter | 4

Self-Report Instruments and Methods

47

TABLE 4.2 Driver Skill Inventory (DSI) Items and Means for Finland (FIN), Sweden (SWE), Turkey (TR), and Greece (GR) DSI Items (Item No.)

FIN

SWE

TR

GR

F3,789e795

Perceptual motor skills

3.73

4.07

3.84

3.90

5.46***

Fluent driving (1)

3.50

3.76

3.86

3.87

8.75***

Perceiving hazards in traffic (2)

2.77

3.39

3.45

3.58

29.86***

Managing the car through a skid (4)

3.50

3.70

3.65

3.39

6.37***

Predicting traffic situations ahead (5)

3.38

3.75

3.87

3.62

14.11***

Knowing how to act in particular traffic situations (6)

3.02

3.65

3.46

3.56

16.42***

Fluent lane changing in heavy traffic (7)

3.51

4.09

4.07

3.81

23.08***

Controlling the vehicle (10)

3.29

4.23

4.05

3.56

34.56***

Make a hill start on a steep incline (13)

3.19

3.93

3.93

3.68

31.30***

Overtaking (14)

2.30

2.80

3.42

3.09

28.01***

Reverse parking into a narrow gap (20)

3.73

4.07

3.84

3.90

5.46***

Driving behind a slow car without getting impatient (3)

3.11

2.39

3.05

2.79

14.94***

Staying calm in irritating situations (9)

3.29

3.13

3.32

3.34

1.36

Keeping a sufficient following distance (11)

3.87

3.48

3.81

3.59

7.24***

Conforming to the speed limits (16)

3.60

2.75

3.59

3.30

25.38***

Avoiding unnecessary risks (17)

3.89

3.73

3.87

3.69

Tolerating other drivers’ errors calmly (18)

3.15

2.96

2.90

3.58

20.02***

Obeying the traffic lights carefully (19)

4.04

4.30

4.18

4.15

2.77*

Safety skills

2.53

*p < 0.05; **p < 0.01; ***p < 0.001. Instruction: “Please indicate your strong and weak components.” Scale: 0, definitely weak; 1, weak; 2, neither weak nor strong; 3, strong; 4, definitely strong. Source: Data from Walle´n Warner et al. (2010).

errors) was replaced by internal comparison. Table 4.2 describes the DSI perceptual motor skill and safety skill items as well as item means for Finland, Greece, Sweden, and Turkey. These items do not measure a driver’s absolute level of skills or proneness to safety behaviors but, rather, his or her orientationdthat is, whether the respondent sees himself or herself as a skillful (in terms of perceptual motor skills) or a safe (rule obedient and risk avoidant) driver. In earlier studies, safety orientation (high scores on safety skills) was a strong predictor of accidents, whereas emphasis on perceptual motor skills was related to heightened accident risk. In Lajunen, Parker and Stradling’s study (1998), safety skills even buffered the effects of driver anger on violations. These comparisons of the DBQ and the DSI show that direct measurement of driver skills or errors is very difficult and possibly unreliable using self-report instruments. However, self-reports can be used reliably to measure a driver’s view or opinion about his or her skills. Although remembering individual

mistakes is difficult, drivers usually have a general idea of themselves as drivers, which can be measured by selfreport instruments. It was previously mentioned that the DBQ also measures violations (i.e., deliberate deviations from safe driving). Violations are conducted to achieve some benefits in traffic, such as saving travel time (e.g., “overtake a slow driver inside”), psychological satisfaction from feeling competent and capable (e.g., “race from lights” and “discard a speed limit”), or to vent one’s anger (e.g., “sound horn to indicate hostility”). Whereas errors are unintended deviations from safe practices, violations are deliberate violations of a social norm, highway code, and/or safe practices. Asking “violating drivers” to honestly report their violations is the second paradox in the DBQ and selfreports in general: We assume that people who like to violate social norms in traffic would respect the honesty required in self-reports. It is more likely that drivers who do not respect general rules and norms in traffic would also not

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be exactly honest when answering self-report surveys. The DSI again uses a different strategy for assessing a driver’s proneness to violations. Several perceptual motor skill items are actually a reverse measure for proneness to violations (e.g., “staying calm in irritating situations,” “conforming to the speed limits,” and “avoiding unnecessary risks”). The positive phrasing of these items can be assumed to reduce the reluctance to answer the items honestly. In the DSI, the driver compares his or her perceptual motor skills and safety skills. The structure of the DSI encourages a driver to admit some “weaknesses” while he or she can excel in other aspects of driving. Whereas using self-reports as a direct measurement of behavior in the DBQ is problematic and possibly biased, the self-report is used adequately in the DSI.

3. SELF-REPORTS OF ACCIDENTS, NEAR ACCIDENTS, AND MILEAGE So far, it has been demonstrated that self-reports can be used to measure only certain aspects of driving. Although self-reports are not adequate measures of unconscious processes or norm violations that drivers would not like to report honestly, self-reports work well when recording driver attitudes, self-evaluated skills, and beliefs. Interestingly, most of the self-reported drivers’ behaviors are evaluated in terms of how risky they aredthat is, the potential to cause a serious accident. Hence, accident risk seems to be the ultimate criterion for driver behaviors; therefore, either objective or self-reported number of past accidents is used as criterion for safe driving.

3.1. Self-Reports of Accident Involvement In most studies concerning behavioral correlates of individual differences in road traffic accident risk, a driver’s accident history (the number and/or severity of accidents) has been used as a criterion for safety. These accident data are collected either from drivers’ self-reports or from official statistics. However, both are subject to systematic and random error and are therefore somewhat biased (Elander et al., 1993). The advantage of asking drivers to report accidents is that minor crashes can also be recorded. In addition, drivers’ self-reports are usually more detailed than official reports because drivers can be asked very specific questions. However, comparisons between selfreported and state-recorded numbers of accidents have shown some underreporting of accidents in self-reports. McGwin, Owsley, and Ball (1998) studied the agreement between self-reports and state records for identifying crash-involved older drivers. Results indicated that there was a moderate level of agreement between self-reported and state-recorded crash involvement. However, there were significant differences between crash-involved drivers

PART | I

Theories, Concepts, and Methods

identified via state records and those who completed selfreports with respect to demographic (age and race), driving (annual mileage and days per week driven), and vision impairment. The authors concluded that research designed to identify risk factors for crash involvement among older drivers should carefully consider the issue of case definition, particularly if self-report is used to identify crashinvolved older drivers. Whereas McGwin et al.’s (1998) study investigated the agreement between self-reports of accidents and official records among older drivers, Boufous et al. (2010) studied the accuracy of self-report of on-road crashes and traffic offenses among 2991 young drivers in New South Wales, Australia. Participants completed the follow-up questionnaire in which they were asked if they had been involved in an on-road crash or had been convicted of a traffic offense while driving during the year prior to the survey. This information was linked to police crash data. Results showed a high level of accuracy in young drivers’ selfreport of police-recorded crashes and of police-recorded traffic offenses. The authors concluded that surveys may be useful tools for estimating the incidence of on-road crashes and traffic offenses for young drivers. The difference between the results of the study by McGwin et al. and those by Boufous et al. may indicate that self-reports are less reliable among (even healthy) elderly drivers than among young drivers. Hence, the choice of accident recording method should also take into account the sample characteristics. Self-reports of accidents are easily biased by intentional or unintentional misrepresentation (Elander et al., 1993). The latter source of bias can be caused either by a different definition of reportable accidents between drivers or by simple forgetting. In a study by Loftus (1993), 14% of people involved in road accidents leading to an injury did not remember the event a year later. In a series of studies, Maycock and colleagues asked drivers to report accidents during the past 3 years and found that the forgetting rate was rather high at approximately 30% per year (Maycock, 1997). The forgetting rate was lower (14%) for serious accidents leading to injuries than for those accidents that resulted in only material damages. When the accidents were recalled over a 3-year period, more accidents were reported for the more recent times during the period than for longer ago (Maycock, 1997). Based on these studies, Chapman and Underwood (2000) concluded that “there is reason to suspect that even severe accidents may be routinely forgotten by normal drivers over periods as short as a year” (p. 33). They also suggested that minor accidents are forgotten at much higher rates. Although self-reports of accidents seem to be more or less problematic and the degree of accuracy seems to depend on various factors (e.g., time period, age of the participants, and the way in which the survey is conducted), official

Chapter | 4

Self-Report Instruments and Methods

accident records from the police, hospitals, or insurance companies also seem to have many shortcomings. Forgetting, various definitions of accidents, or deliberate underreporting typical of self-report do not distort the official accident records, but official records have some other limitations. First, the police or insurance companies’ accident records do not include minor damages. Second, some driver groups, such as older drivers, can be overrepresented in these records for reasons not related to their risk of accident (Elander et al., 1993). For example, elderly drivers are overrepresented in the official accident statistics because they have a greater risk of being injured or dying compared to young drivers (Evans, 1991). The same statistical phenomenon caused by differential injury risk may be observed when males and females are compared (Evans, 1991). Arthur et al. (2005) assessed the convergence of selfreport and archival crash involvement and moving violations data in a 2-year longitudinal follow-up study. Results suggested a lack of convergence between self-report and archival data at both time 1 and time 2. Moreover, the self-report data included a broader range of incidents (more crashes and tickets) than did state records. Similar conclusions were drawn by Anstey, Wood, Caldwell, Kerr, and Lord (2009), who evaluated associations between selfreported crashes and state crash records among 488 community-dwelling participants aged 69 to 95 years. Crash history data were obtained from state records (5-year retrospective and 12-month prospective), retrospective selfreport (5 years), and prospective monthly injury diaries (12 months). As in the study by Arthur et al., respondents reported more accidents than were recorded in official records: During the past 5 years, 22.3% of respondents reported a crash, and 10.0% reported a crash in the 12-month follow-up period, whereas 3.2% of the sample had state crash records during the previous 5 years and 0.6% had state-recorded crashes during the 12-month follow-up period. The authors concluded that caution should be applied when using state crash records as an outcome measure in driving research and suggested that retrospective self-reported accidents over 5 years are preferable. In addition to the age and gender bias as well as the omission of minor accidents, official accident records are not always available because of data protection acts and because the period for recorded accidents might be limited. Also, the type and role of the parties (e.g., culpability) in the accident are rarely available. Studies on self-reports of accidents and state records show that both recording methods have considerable shortcomings and strengths. In conclusion, it seems that the best self-report method for recording accidents is a retrospective self-report of a of maximum of 5 years. The shorter the reporting period, the smaller the underreporting bias due to forgetting should be. If possible, self-reports should be complemented with state accident records.

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3.2. Self-Reported Near Accidents as a Criterion for Safe Driving Because the forgetting rate increases as the time period for recall becomes longer and when minor accidents are asked for, we could suppose that self-reports of serious accidents, such as in the previous year, would be the best estimate. However, accidents and especially serious accidents are infrequent. This means that samples in accident studies must be enormous, which is impossible for most of the studies. One approach to this problem is to record near accidents, which are extremely frequent events (Chapman & Underwood, 2000) and may lead to a crash in less optimal conditions. By definition, near accidents can be assumed be a good candidate for a general measure of safety. Chapman and Underwood (2000) compared reports and recalls of more than 7000 car journeys from 80 subjects during the course of a year. These records included more than 400 reports of near accidents. The results showed that near accidents are generally forgotten extremely rapidly, with an estimated 80% of incidents being no longer reported after a delay of up to 2 weeks. If the near accident was a serious one or the respondent was the guilty party, the forgetting rate was smaller. Chapman and Underwood’s study shows that although near accidents are very frequent and, thus, easier to use in statistical analyses than actual accidents, the high forgetting rate makes them an unreliable measure of risk. Near accidents can best be recorded by using a driving diary technique, in which drivers keep a log about their near accidents for a number of weeks. Because near accidents are recorded as soon as possible after they happen, the forgetting rate remains low, whereas the number of near accidents is much higher.

3.3. Self-Reports of Mileage and Driving Experience Mileage is one of the main factors measured in studies on driver behavior. Mileage as total lifetime mileage or annual mileage and type of exposure are extremely important variables because mileage (and exposure) largely determines a driver’s likelihood of being involved in an accident. Moreover, particular driver characteristics may lead only to particular forms of driving behavior, which may thereby affect liability only for accidents of certain types (Elander et al., 1993).

3.3.1. Mileage, Exposure, and Accident Involvement Overall mileage has been consistently reported to be associated with accident involvement (French, West, Elander, & Wilding, 1993; Quimby & Watts, 1981). This effect may be largely caused simply by greater exposure,

50

indicating that people who spend much time on the road are also exposed to the danger of having accidents more than people with low annual mileage (Summala, 1996). In addition to this simple effect on accident involvement based on exposure, the overall mileage has much more complicated effects on driving style and safety. It has been found that the relationship between mileage and accidents is not linear but, rather, a negatively accelerating curve, with smaller increases in accident rate at a higher level of mileage (Maycock, 1997). In the beginning of a driver’s career, high mileage means increased exposure and increased accident involvement, but for experienced drivers the higher levels of mileage do not increase the number of accidents in the same ratio (Evans, 1991; Maycock, 1997). The most likely explanation for this finding is that drivers with very high mileage per year drive mostly on relatively safe roads (Elander et al., 1993). People who drive low mileages tend to accumulate much of their mileage on congested city streets with two-way traffic and no restriction of access, whereas high-mileage drivers typically accumulate most of their mileage on freeways or other divided multilane highways with limited access. Because the driving task is simpler, the accident rate per mile is much lower on freeways, and beyond a certain point, a person driving half as many miles as another would be expected to have considerably more than half as many accidents (Janke, 1991). In addition, drivers with exceptionally high overall mileage may have developed expertise based on both extensive practice and interest in driving. It is even possible that drivers with very high annual mileage adopt a safer driving style than moderately experienced drivers, although mileage has been reported to correlate with faster driving (Wilson & Greensmith, 1983). The effects of mileage on driving style and accidents depend largely on the type of exposure. The type of roads usually driven (motorways vs. country roads, and traffic density), time of year (winter vs. summer), time of the day spent on the road (daylight vs. night), and usual purpose of driving (work related vs. free time) all affect the likelihood of accident involvement and driving style. Special effort should therefore be directed to measure these factors related to exposure in studies on driver style and accident involvement because exposure may be related to psychological variables under investigation. Ignoring the role of driving experience and exposure can increase error variance and reduce the true associations between psychological variables and accident frequency (Elander et al., 1993).

3.3.2. Measuring Mileage with Self-Reports In driver behavior studies, mileage is traditionally measured with either driving diaries or questionnaires, which are both based on self-reports. Self-reports of mileage share to some degree the same problems and bias sources as self-reports of

PART | I

Theories, Concepts, and Methods

accidents. The accuracy of self-reports has been questioned in several studies, and in-vehicle recording devices have been suggested as an alterative to self-reports (Blanchard, Myers, & Porter, 2009). Although in-car recordings certainly provide a more unbiased and accurate measure of exposure than selfreports, their usefulness is still very limited to certain types of studies. For example, large-scale population studies do not allow direct recordings of driving frequency and amount. Moreover, many studies require anonymous participation; thus, direct measurements cannot be considered. Although mileage self-reports share some of the problems with self-reports of accidents (e.g., drivers forget to report some trips), self-reports of mileage are less biased than self-reports of accidents for several reasons. An accident is a single event, but the estimation of mileage is continuous, which makes it naturally easier to evaluate. Whereas accidents can be embarrassing or even traumatic events that one would like to forget, mileage is a rather neutral issue. Moreover, there are more techniques for reporting exposure than accident involvement: Drivers can be asked to report their lifetime mileage, mileage during a certain period of time (year, month, week, or day), frequency of driving in days or number of trips, in addition to the type of exposure (e.g., in-city traffic, highways, and night driving). Drivers can also relate their lifetime mileage to other more objective measures, such as ownership and frequency of car use. Smith and Wood (1977) performed a study in which employees were asked to recall the individual business journeys that they had made during the previous 6 or 8 months, and then these recalls were compared with the expense claims submitted during the period. During 6 months, 27.3% of car journeys appeared to be forgotten, and this percentage increased to 34.8% for the 8-month period. Note that although frequency of trips is an estimate for mileage, it might be more vulnerable to forgetting than self-reports of annual or monthly mileage because drivers are more likely to forget an individual trip than how much they drive in general. Staplin, Gish, and Joyce (2008) and Langford, Koppel, McCarthy, and Srinivasan (2008) studied the association between the extent of driving and crash involvement: The lower the annual mileage driven, the higher the per-distance crash rate. According to these studies, there is a clear pattern of misestimating for those who self-report an extremely low or extremely high number of miles driven, which casts serious doubt on self-reports, especially when using extreme estimates of mileage. Both studies demonstrated overestimation by the highest mileage drivers and underestimation by the lowest mileage drivers, with underestimation also linked specifically to a travel pattern composed of frequent but short trips. In both studies, the need for objective exposures instead of self-reports of exposure was highlighted.

Chapter | 4

Self-Report Instruments and Methods

Studies seem to indicate that self-report bias in mileage estimates can be best solved by not using self-reports but, rather, relying on, for example, GPS-based in-car recordings. Because this is not possible in many survey studies, the best remedy for reducing self-report bias is to use as many estimates of mileage as possible. For example, total annual or monthly mileage, frequency of driving (number of trips per time unit), or the proportion of long trips can be asked, and the final estimates can be based on several self-report indicators.

4. VALIDITY OF SELF-REPORTS OF DRIVING Validity simply means the extent to which a test (or a selfreport) measures what it is intended to measure. Validation strategies are traditionally divided into techniques for evaluating the validity of measurement and techniques for evaluating the validity of decisions based on the test scores. The first approach to validation includes content and construct validation strategies, which are aimed at investigating if the contents of the test are adequate and if the correlations with other similar tests and measurements indicate that the structure of the test is supported by empirical evidence. The second group of strategies provides tools for investigating criterion-related validitydthat is, the correlations between the test score and the criterion. Self-report measurements in traffic research do not differ from the usual psychological and educational measurements; thus, the same criteria can be used for assessing the measurement validity. Before validity can be evaluated, the test scores have to show a certain level of reliability. Reliability refers to consistency of measurement and can be evaluated using different strategies, including split-half and alpha reliability for evaluating internal consistency and testeretest (including parallel form reliability) for evaluating the stability of the scores over time. Because reliability is a perquisite for validity, reliability analysis has to be completed successfully before any validity coefficients can be calculated. Because test reliability depends on the length of the test (number of items), quality of items, and the sample and is thus a very technical characteristic of the test, tests published in international peer-reviewed journals usually show adequate reliability.

4.1. Assessing the Reliability and Validity of a Self-Report Instrument of Driving: The Driver Behavior Questionnaire The DBQ (Reason et al., 1990) provides a good example for demonstrating how a test has been validated in traffic ¨ zkan and colleagues research. Two studies by O

51

¨ zkan et al., 2006; O ¨ zkan, Lajunen, & Summala, 2006) (O are used to demonstrate how reliability and validity of a self-report instrument can be assessed.

4.1.1. Reliability Virtually all DBQ studies report internal reliability coefficients for the DBQ scales. The general finding is that both violations and error scales show adequate reliabilities (internal reliability coefficients are usually ~0.80). When the error scale is split into dangerous errors and nondangerous “silly” mistakes, and the violations scale is split into ordinary violations and aggressive violations, the reliability coefficients tend to be somewhat lower. For ¨ zkan, Lajunen, and Summala’s (2006) study, example, in O the alpha reliabilities for errors and violations were 0.84 and 0.83, respectively. When errors were divided into mistakes and lapses and violations were divided into ordinary violations and aggressive violations, the alpha reliabilities were 0.81 for mistakes, 0.67 for lapses, 0.79 for ordinary violations, and 0.74 for aggressive violations ¨ zkan, Lajunen, & Summala, 2006). This slight decrease (O in alpha coefficients does not indicate unreliability but, rather, reflects the fact that the number of the items in the scale is directly related to the strength of the reliability coefficient. In addition to internal consistency (alpha coefficient or split-half reliability), the temporal stability of the scores indicates reliability of the instrument. Stability of the scores over time can be evaluated by calculating a testeretest reliability coefficient in which the correlation between time 1 and time 2 scores is calculated. High correlation between scores indicates high temporal stability. Because testeretest reliability analysis requires testing the same drivers twice, studies reporting retest reliabilities are rare, especially when the time gap between measurements is long. Parker et al. (1995) conducted a survey in which 1600 drivers’ DBQ responses were analyzed. Seven months after their original responses, 80 respondents completed the DBQ again. Testeretest reliabilities were 0.69 for errors, 0.81 for violations, and 0.75 for lapses, which indicate relatively high reliability over time (Parker et al., 1995). The retest sample in the study by Parker et al. (1995) ¨ zkan, Lajunen, and Summala (2006) was small. Later, O assessed testeretest reliability of the DBQ in a sample of 622 drivers. The time gap between the measurements was 3 years, which reduces the probability of respondents remembering their initial answers to the DBQ items. The testeretest reliability was 0.50 for errors, 0.76 for viola¨ zkan, Lajunen, tions, and 0.61 for the whole scale (O & Summala, 2006). The following conclusions based on ¨ zkan, Lajunen, Chliaoutakis, Parker et al. (1995) and O et al. (2006) can be drawn. First, DBQ scales and especially

52

violations show sufficient temporal stability. Second, the error scale shows much lower testeretest stability than the violations scale. This finding may not indicate problems in the temporal stability of the error scale because it can be assumed that among novice drivers the error scores should decrease as a function of mileage: The more young drivers drive and gain experience, the less likely they are to make errors. On the other hand, the opposite might be true for elderly drivers: The older the drivers become, the more frequent cognition-related errors occur. Moreover, the difference in stability score supports the distinction based on driving style (the way drivers tend to drive) and driving skills: Driver performance (lack of errors) can improve, but driving style (violations) as a habitual way of driving stays the same. In addition to the number and quality of the items, reliability coefficients also depend on the sample characteristics. The importance of sample characteristics can be especially seen when the reliability of a self-report instrument is studied among different driver groups (e.g., novices, elderly drivers, and professionals) in one culture or when similar samples from different countries ¨ zkan, Lajunen, Chliaoutakis, and cultures are compared. O et al. (2006) investigated the applicability of the DBQ in six different countries (Finland, Great Britain, Greece, Iran, The Netherlands, and Turkey). A total of 242 drivers were chosen from each of the six countries and were matched for age and sex. Reliabilities were compared to the original British data that were used for developing the DBQ. According to the results, reliabilities of the scales were at the same level as in the original British data. This finding demonstrates the cross-cultural applicability of DBQ.

4.1.2. Content Validity After determining that the instrument has reasonable reliability coefficients in the study population, the next step is to evaluate the content and construct validity of the instrument. Content validity is demonstrated when we can say that the test provides an adequate sample of a particular domain (Guion, 1977). “Adequate sample” means that test items in a self-report test cover all the relevant topics in the domain without including items that belong to another domain. For example, DBQ ordinary (nonaggressive) violations should cover all the most common and thus representative behaviors of “deliberate deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system” but not violations with aggressive motivation because those behaviors belong to the DBQ aggressive violations scale. This distinction is not always clear because some behaviors, such as “close following” or “forcing one’s way,” might contain an aggressive motivation but can also be violations without aggressive content.

PART | I

Theories, Concepts, and Methods

The procedure for assessing content validity usually consists of the following three steps: (1) Describe the content domain (e.g., aberrant driving behavior), (2) determine the area of the domain (e.g., type of aberrant behavior) measured by each item (e.g., “disregard the speed limit on a residential road” measures highway code violations without aggressive aims, and “miss a ‘give way’ sign” measures potentially dangerous “errors”), and (3) compare the structure of the self-report instrument with the structure of the content domain. Hence, content validity cannot be evaluated numerically by using an index, but the process is based on qualitative comparisons between the theoretical model and contents of the instrument in which self-report test items (questions) as measurements are compared to theoretical constructs. This complete system of relationships among the constructs and behaviors is called as nomological network (Cronbach & Meehl, 1955). Such detailed content analysis has not been used for evaluating the DBQ, so the question about the content validity of DBQ is still open. We can only state that the DBQ typology of dangerous but unintentional errors and deliberate violations seems to match well with the driver behavior performance (or style skills) model presented in Figure 4.1. This can be considered as support for content validity.

4.1.3. Construct Validity: Internal and External Validity Whereas content validity indicates the degree to which a self-report instrument “looks as it should,” construct validity shows to what degree the instrument “performs as it should.” Content validity analysis is based on qualitative comparisons, whereas construct validity is established when the correlation pattern between the instrument and other relevant measures is as theoretically expected. Hence, the test shows high construct validity when its items provide a good measure of the specific construct. Construct validity includes both convergent (test correlates with other tests measuring the same construct) and discriminant validity (test does not correlate with theoretically unrelated measures). Construct validity includes both the validity of the factor structure and relationships to other tests. The validity of the structure of the scale is usually assessed by using either exploratory or confirmatory factor analysis. Another possibility to determine if the test measures the construct concerned is to perform an experimental study in which the same aspect as supposedly measured by the test is experimentally manipulated. For example, driver skill training should increase drivers’ skill scores in the DSI (Lajunen & Summala, 1995) but not safety skills. Construct validity is most often evaluated by investigating a test’s correlation to earlier similar instruments. High correlations in the expected direction indicate construct validity. The most

Chapter | 4

Self-Report Instruments and Methods

advanced way of evaluating construct validity is the multitraitemultimethod approach (Campbell & Fiske, 1959). In this method, all constructs (both those that should be related and those that should not) are measured with different measurement methods. In driver behavior, this could mean self-reports, peer opinions (e.g., spouse), in-car measurements in real traffic, and simulator recordings. If the measurements of the same construct by using different methods correlate, this indicates construct validity. On the other hand, measurements of different unrelated constructs with the same methodology should not correlate. Correlations between unrelated constructs indicate method bias. Development of the Driver Social Desirability Scale (DSDS) (Lajunen, Corry, Summala, & Hartley, 1997) demonstrates how construct validity can be evaluated by using exploratory factor analysis for testing the structural validity of the scale and correlations with existing standard instruments for testing convergent validity. According to Lajunen et al., following the theory of Paulhus (1984), social desirability consists of two components: selfdeception and impression management. Lajunen and colleagues developed a traffic-specific social desirability scale (DSDS) to control self-deception and impression management in self-reports of driving. Data were collected

53

both in Australia and in Finland to minimize cultural bias. The results of exploratory factor analysis showed that items grouped into two independent factors as hypothesized, which can be taken as an indication of construct validity of the structure (Table 4.3). Second, construct validity of the newly developed DSDS was evaluated by calculating correlations with the Balanced Inventory of Desirable Responding (BIDR) (Paulhus & Reid, 1991), which was used as a model for developing DSDS. Table 4.4 shows that the Driver Impression Management (DIM) scale had stronger correlations with the BIDR Impression Management (IM) scale than with the BIDR Self-Deception (SD) scale. Moreover, Driver Self-Deception (DSD) had stronger correlations with BIDR-SD than with BIDR-IM. This was especially true for the Finnish data. These correlations indicate an adequate level of convergent and discriminant validity for the DSDS scale. The factor structure of the DBQ has been well studied ¨ zkan, Lajunen, Chliaoutakis, et al., 2006; O ¨ zkan, (O Lajunen, & Summala, 2006). Based on comparisons between matched samples (by age and sex) from six countries (Finland, Greece, Iran, The Netherlands, United Kingdom, and Turkey) using confirmatory factor analysis,

TABLE 4.3 Factor Loadings for the Driver Social Desirability Scale Items in Australian (A) and Finnish (F) Samples F2

F1

Item A

F

A

F

I have never exceeded the speed limit.

0.49

0.65

I have never wanted to drive very fast.

0.55

0.56

I have never driven through a traffic light when it has just been turning red.

0.45

0.63

I always obey traffic rules, even if I’m unlikely to be caught.

0.71

0.67

I always keep sufficient distance from the car in front of my car.

0.34

0.47

If there were no police control, I would still obey speed limits.

0.72

0.77

I have never exceeded the speed limit or crossed a solid white line in the center of the road when overtaking.

0.42

0.55

I always know what to do in traffic situations.

0.60

0.65

I never regret my decisions in traffic.

0.60

0.60

I don’t care what other drivers think of me.

0.33

0.31

I always am sure how to act in traffic situations.

0.91

0.77

0.61

0.59

Driver Impression Management (DIM)

Driver Self-Deception (DSD)

I always remain calm and rational in traffic.

0.35

Instruction: “The following items concern your driving in different situations. Please express your agreement or disagreement with each statement, selecting a number from the scale.” Answer scale: 7-point scale ranging from “not true” (1) to “quite true” (4) and “very true” (7). Source: Adapted from Lajunen et al. (1997).

54

PART | I

Theories, Concepts, and Methods

TABLE 4.4 Correlation Coefficients between Driver Social Desirability Scales (DSDS) and Paulhus’ Balanced Inventory of Desirable Responding (BIDR) in the Finnish (FIN) and Australian (AUS) Dataa Scale

DSDS-DIM

DSDS-DSD

DSDS

BIDR-IM

BIDR-SD

DSDS-DSD AUS

0.21**

FIN

0.17*

DSDS

0.86***

0.61***

0.85***

0.67***

AUS

0.54***

0.51***

0.63***

FIN

0.48***

0.05

0.38***

AUS

0.16*

0.47***

0.34***

0.44***

FIN

0.16*

0.42***

0.34***

0.31***

AUS

0.30***

0.43***

0.39***

0.66***

0.65***

FIN

0.42***

0.25***

0.45***

0.88***

0.73***

AUS FIN BIDR-IM

BIDR-SD

BIDR

a

Correlation coefficients indicating convergent validity are shown in boldface type, and correlations indicating discriminant validity are underlined. *p < 0.05; **p < 0.01; ***p < 0.001.

¨ zkan and colleagues concluded that the fit of the threeO factor model (aggressive violations, ordinary violations, and errors) of the DBQ was partially satisfactory in each country. Exploratory factor analyses together with target (Procrustes) rotation and factorial agreement indexes showed that the “ordinary violations” factor was fully congruent and the “errors” factor was fairly congruent ¨ zkan, Lajunen, Chliaoutakis, et al., across countries (O 2006). The two-factor structure based on violations and errors seems to be universally valid and has been found in different driver groups, including professional drivers, motor cycle drivers, offenders, probationary drivers, parentechild pairs, young women, and older drivers (de Winter & Dodou, 2010). Although the DBQ yields slightly different factor structures in different countries, the core structure of the instrument seems to be stable, showing high construct validity. In addition to the factorial validity, the construct validity of the DBQ in terms of other tests has been investigated. de Winter and Dodou (2010) concluded that “the DBQ errors and violations factors are strongly situated in a network of correlations with other questionnaires and tests” (p. 463) and listed a great variety of factors to which the DBQ scale scores have been related. Although the

correlations between DBQ and the variety of indicators are impressive, they still do not prove that the DBQ is a valid instrument. For example, correlations to other driving questionnaires might be based on method bias: The items in different driving inventories are often very similar even though the names of the scales suggest different contents. The most robust validation evidence for the DBQ is the correlation between driver behavior (i.e., errors and violations) measured during real driving and driver behavior measured in a simulator. Despite the large number of DBQ studies, there are no studies in which the DBQ has been systematically compared with errors and violations in real driving, which naturally casts doubts on the construct validity of the DBQ (Af Wa˚hlberg, 2010). The lack of robust (i.e., from other types of measurements besides self-reports) validation evidence is not only a problem specific to the DBQ but also applies to many self-reports of driving. One reason for the lack of validation studies might be the practical difficulties: Driver samples in the in-car observation studies have usually been quite small, whereas questionnaire studies usually require large samples. Another problem is that a short drive with an instrumented car is unlikely to capture differences between drivers that, for example, the DBQ is designed to measure.

Chapter | 4

Self-Report Instruments and Methods

For example, it is unlikely to observe aggressive or even ordinary violations in study conditions. Moreover, the DBQ measures the frequency of aberrant behaviors in a year’s time, whereas a test drive with an instrumented car lasts only a few hours. Therefore, it is not surprising that selfreports of driving lack evidence of convergent validity. West, French, Kemp, and Elander (1993) compared self-reports of speed, calmness, and deviant driver behavior to similar observed behaviors among 48 drivers. According to results, observed speed on the motorway correlated well with drivers’ self-reports of normal driving speed, observed calmness correlated with self-reported calmness, and observed carefulness correlated with self-reported deviant driving behavior. Hence, in this study, self-reports seem ˚ berg (2002) to reflect driver behavior well. Haglund and A compared self-reported speed measures and observed speed among 533 drivers. The observed speed had a significant correlation of 0.36 with self-reported speed, 0.37 with normal speed, and -0.42 with intention to keep speed limit. Although these correlations are mediocre, they are still statistically significant and indicate that self-reports show some construct validity compared to objective observations.

4.1.4. Criterion-Related Validity: Concurrent and Predictive Validity Concurrent and predictive validity refer to validation strategies in which the predictive value of the test score is evaluated by validating it against certain criterion. In the case of driver behavior, the most used criterion is a driver’s accident involvement. Hence, a self-report of driving shows validity if it is related todpreferably predictsdaccident involvement. In concurrent validation, the test scores and criterion variable are measured simultaneously. In predictive validation, the test scores are obtained in time 1 and the criterion scores in time 2, which allows one to evaluate the true prediction power of the self-report instrument. One of the strengths of the DBQdespecially for violationsdis that it has strong correlations with drivers’ ¨ zkan, Lajunen, Chliaoutakis, et al., accident involvement (O ¨ 2006; Ozkan, Lajunen, & Summala, 2006). The results of de Winter and Dodou’s (2010) meta-analytical study showed that both DBQ violations and errors correlated with self-reported accidents. However, the correlations of errors and violations with recorded accidents were not statistically significant, although this might be due to the small number of samples included in the meta-analysis. The metaanalysis also showed that errors and violations correlated negatively with age and positively with exposure, and that males reported fewer errors and more violations than females, which are all common findings in the DBQ literature. In addition to retrospective design, de Winter and Dodou investigated the DBQ and self-reported accidents

55

prospectively in a sample of 10,000 beginner drivers, who answered the DBQ after 6, 12, 24, and 36 months of licensure. The results of this study showed that the error and violation factor predicted accidents prospectively and retrospectively. Because de Winter and Dodou’s metaanalysis included a sample of more than 45,000 respondents and the prospective sample was also large, it can be concluded that the DBQ shows relatively high predictive validity in terms of self-reported accidents. Due to the small number of studies that have used official accident records as criterion, the predictive validity of DBQ in terms of officially recorded accidents is still unclear.

4.2. Socially Desirable Responding in SelfReports of Driving We previously mentioned forgetting as one source of bias influencing accident and near accident self-reports. Forgetting can also naturally influence self-reports of driving behavior such as violations, but because driving behavior refers to how a driver chooses to drivedthat is, to driving style in everyday situationsdforgetting should have only a minor role in self-reports of driving behavior. Whereas errors are failures of cognitive processes (e.g., forgetting to check the rearview mirror when overtaking) and thus usually go unnoticed, driving behaviors are something that drivers choose to do and that they do repeatedly (e.g., speed choice). We can assume that selfreports of driving behavior and especially self-reports of aberrant driver behaviors are influenced by socially desirable responding rather than forgetting.

4.2.1. Socially Desirable Responding as Impression Management and Self-Deception Social psychological studies have shown that self-reports of personality, attitudes, and behavior are inaccurate or even biased to some degree because at least some subjects tend to engage in socially desirable respondingdthat is, a tendency to give answers that make the respondent look good (Paulhus, 1984; Paulhus & Reid, 1991). Consistent with these findings, it can be assumed that self-reports of driving behavior and driver attitudes are somewhat biased by socially desirable responding. A large number of studies using different measures of socially desirable responding have shown that it consists of two distinct factors called “impression management” (or “other-deception”) and “self-deception” (Paulhus, 1984; Paulhus & Reid, 1991). In this distinction, impression management refers to the deliberate tendency to give favorable self-descriptions to others and therefore is close to lying and falsification (Paulhus & Reid, 1991). The deliberate nature of impression management is also manifested in the finding that impression management increases in public settings and

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seems to be a situation-dependent phenomenon (Paulhus, 1984). It has been recommended that impression management should be controlled when it is conceptually independent of the trait being assessed but still contributes to the self-reported scores of that trait (Paulhus, 1984; Paulhus & Reid, 1991). The self-deception factor can be characterized as a positively biased but subjectively honest self-description. In contrast to impression management, self-deception is an unintentional tendency and not influenced by the anonymity versus public context manipulation (Paulhus, 1984). Unlike impression management, self-deception has been reported to be intrinsically linked to positive personality constructs such as psychological adjustment (Sackeim & Gur, 1979; Taylor & Brown, 1988), high self-esteem (Paulhus & Reid, 1991), and lack of neuroticism (Borkenau & Ostendorf, 1989; Paulhus & Reid, 1991). These findings suggest that self-deception could be used for the purposes of gaining pleasure (ego enhancement) as well as avoiding pain (denial) and therefore provides an aid for coping with negative life events and threatening information (Paulhus, 1984; Paulhus & Reid, 1991). Self-deception appears to be more of a personality construct than simply a distorting factor. Paulhus (Paulhus, 1984; Paulhus & Reid, 1991) suggests that self-deception should not be controlled if it is an intrinsic aspect of the personality construct concerned. Note, however, that the bias caused by self-deception may not be constant over situations. Some situations can be more threatening than others and, therefore, elicit stronger need for self-deception.

4.2.2. Socially Desirable Responding in SelfReports of Driving Behavior In self-reports of traffic behavior, impression management may cause serious bias. The majority of studies concerning personality and motivational factors related to accident proneness use retrospective designs in which accident history and punishments are elicited by self-report and then correlated with personality and background variables. It can be hypothesized that this kind of design is extremely liable to deliberate impression management. In fact, earlier findings show that drivers tend to report speeding tickets honestly but “forget” their involvement in other types of traffic violations (Summala & Hietamaki, 1984). In addition to impression management, the construct of selfdeception can also be hypothesized as an important factor in driving behavior. Drivers’ sense of control in traffic and trust in their own capabilities as drivers also increase with driving experience and improvement in skills. An exaggerated sense of control and confidence in one’s judgment and skills constitutes a real risk factor in traffic, where proper alertness and anticipation of possible risks are essential for safety (Summala, 1988).

PART | I

Theories, Concepts, and Methods

Lajunen and colleagues (1997) investigated the relationship between their DSDS and self-reported accidents as victim and as responsible party, number of tickets, speeding (100 km/h (62 mph) roads and 60 km/h (37 mph) roads, in general), overtaking, rule compliance, and the Driver Behavior Inventory scales (Glendon et al., 1993) dislike of driving, driver aggression, and driver alertness. The samples consisted of 203 Finnish and 201 Australian drivers. Correlation analyses also indicated that driver impression management (lying) was negatively related to the self-reported number of accidents and punishments, overtaking frequency, speeding, and driving aggression, and it was positively related to traffic rule compliance. Driver self-deception correlated positively with variables measuring sense of control in traffic (Lajunen et al., 1997). Lajunen and Summala (2003) investigated the effects of socially desirable responding on self-reports of driving by recording self-reports of driving in both public and private settings. In public settings, 47 applicants for a driving instructor training course completed the DBQ and the BIDR as part of the entrance examination. In a private setting, 54 students in the training course completed the same questionnaires anonymously in the classroom. Comparisons showed a difference between the two settings in six DBQ item scores such that aberrant behaviors were reported less frequently in the public setting than in the private setting. The authors concluded that bias caused by socially desirable responding is relatively small in DBQ responses. Note, however, that the study was based on a between-subjects design (i.e., the same respondents were not followed) and that an entrance examination for a driving instructor training course hardly reflects ordinary drivers’ responses. Sullman and Taylor (2010) replicated Lajunen and Summala’s (2003) study by using a repeated measures design. A sample of 228 undergraduate students completed the DBQ and a measure of socially desirable responding in class, which constituted a public place, and then did so again 2 months later in the privacy of their homes. As expected, participants demonstrated higher levels of general social desirability in the public setting than in the private setting. None of the DBQ items were significantly different across the two locations, and the authors concluded that the DBQ is not particularly vulnerable to socially desirable responding. Note, however, that the study was not counterbalanced, and that the difference in privacy in “private” and “public” settings was actually not maximized because in both conditions subjects’ names were asked. Af Wa˚hlberg (2010) composed a questionnaire that included scales from several well-known driver inventories and distributed it three times to a group of young drivers in a driver education program and twice to a random group. The DIM scale from the DSDS (Lajunen et al., 1997) was

Chapter | 4

Self-Report Instruments and Methods

used to control for socially desirable responding. Whereas in earlier studies only the correlations (Lajunen et al., 1997) or group differences in quasi-experimental settings were studied (Lajunen & Summala, 2003), Af Wa˚hlberg controlled the effects of impression management when calculating the predictive power of driver behavior inventories. All self-report instruments, including the DBQ, included in the study correlated negatively with impression management, indicating bias: The correlations between the DBQ violation scale and impression management were -0.51 and -0.45. Moreover, the predictive power was more than halved when social desirability was controlled for. Impression management also correlated with self-reported accidents and penalty points in both samples. Similar influence of impression management on self-reported accident involvement (but not official records) was also found in an earlier study (Af Wa˚hlberg, Dorn, & Kline, 2009). The authors concluded that whenever self-reported accidents are used as an outcome variable and predicted by other self-report measures, a lie scale should be included and used for correcting the associations. The conclusion about self-report instruments was even more serious. According to Af Wa˚hlberg, even the most well-known psychometric scales used in driver research are susceptible to social desirability bias.

4.2.3. How to Cope with Socially Desirable Responding in Self-Reports of Driving The literature on socially desirable responding and selfreports of driving seems to be mixed. Whereas driver behavior scales seem to have significant correlations with socially desirable responding, quasi-experimental studies do not seem to indicate any serious bias in self-reports of driving. One possibility is to stop using self-reports of driving and accidents and to rely only on observed behavioral data and official accident records, as some researchers seem to suggest (Af Wa˚hlberg, 2010; Af Wa˚hlberg et al., 2009). The other possibility is to let the use of self-reports of driving go unchallenged and accept the small social desirability bias as an innate characteristic of self-reports. The first alternative would limit behavioral traffic research immensely because many fields, especially social psychology, require use of self-reports. For example, driver attitudes, opinions, and attributions cannot be measured “objectively” but only with self-reports. Moreover, the objective measures also have serious methodological limitations, as studies using an instrumented vehicle, simulator, or laboratory tests show. Official accident records suffer their own sources of bias. Studies conducted by Af Wa˚hlberg (2010) and Af Wa˚hlberg et al. (2009) show that the second alternative is not an option: Traffic researchers cannot continue ignoring bias in the self-reports of driving and outcomes.

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Self-report research methodology offers various ways of coping with socially desirable responding. First, an emphasis on anonymity and confidentiality in instructions and procedures (e.g., sealed envelopes and large group data collection) reduce the effect of socially desirable responding. Second, scales for socially desirable responding, such as the DSDS, can be included in studies and their effect statistically controlled. Scales for controlling impression management, self-deception, careless answering style, and so forth can be easily designed and embedded in such instruments as the DBQ. It is surprising that traffic psychologists have ignored these biases while the use of control scales is common in mainstream psychological tests (e.g., validity scales of the MMPI-2). Third, objective measures of accidents and behavior should be used whenever possible.

5. CONCLUSION This chapter provided a general overview of the use of selfreports in traffic research. Although self-reports can offer a rich source of information, they also have some serious shortcomings and limitation that have to be taken into account. Review of studies using self-report methodology shows that traffic researchers pay far too little attention to the psychometric characteristics and validity of the tests. As the review of the DBQ studies showed, only a few studies have addressed the validity issues and cross-cultural applicability of self-reports of driving. More research and especially large sample validation studies with objective records of accidents and behavior are needed to further assess the level of bias in self-reports and especially to develop effective strategies for reducing different sources of bias. When evaluating the role of self-reports in traffic research, we fully agree with Reason et al. (1990) that the DBQ is a powerful means to measure behaviors that are “too private to be detected by direct observation” but that at the same time “DBQ responses are several stages removed from the actuality of what goes on behind the wheel” (pp. 1329e1330). The same applies to self-reports in general: They are able to reveal information that is not available with any other measurement methods. The gap mentioned by Reason et al. between the reality and the picture given by self-reports may not be possible to erase, but at least it can be considerably reduced with adequate use of self-report methodology.

REFERENCES Af Wa˚hlberg, A. E. (2010). Social desirability effects in driver behavior inventories. Journal of Safety Research, 41(2), 99e106. Af Wa˚hlberg, A. E., Dorn, L., & Kline, T. (2009). The effect of social desirability on self reported and recorded road traffic accidents.

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Theories, Concepts, and Methods

˚ berg, L. (2002). Stability in drivers’ speed choice. Haglund, M., & A Transportation Research Part F: Traffic Psychology and Behaviour, 5(3), 177e188. Janke, M. K. (1991). Accidents, mileage, and the exaggeration of risk. Accident Analysis and Prevention, 23(2e3), 183e188. Joshi, M. S., Senior, V., & Smith, G. P. (2001). A diary study of the risk perceptions of road users. Health, Risk and Society, 3(3), 261e279. Kiernan, B. D., Cox, D. J., Kovatchev, B. P., Kiernan, B. S., & Giuliano, A. J. (1999). Improving driving performance of senior drivers through self-monitoring with a driving diary. Physical and Occupational Therapy in Geriatrics, 16(1e2), 55e64. Kua, A., Korner-Bitensky, N., & Desrosiers, J. (2007). Older individuals’ perceptions regarding driving: Focus group findings. Physical and Occupational Therapy in Geriatrics, 25(4), 21e40. Lajunen, T., Corry, A., Summala, H., & Hartley, L. (1997). Impression management and self-deception in traffic behaviour inventories. Personality and Individual Differences, 22(3), 341e353. Lajunen, T., Parker, D., & Stradling, S. G. (1998). Dimensions of driver anger, aggressive and highway code violations and their mediation by safety orientation in UK drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 1(2), 107e121. Lajunen, T., Parker, D., & Summala, H. (2004). The Manchester Driver Behaviour Questionnaire: A cross-cultural study. Accident Analysis and Prevention, 36(2), 231e238. Lajunen, T., & Summala, H. (1995). Driving experience, personality, and skill and safety-motive dimensions in drivers’ self-assessments. Personality and Individual Differences, 19(3), 307e318. Lajunen, T., & Summala, H. (2003). Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses. Transportation Research Part F: Traffic Psychology and Behaviour, 6(2), 97e107. Langford, J., Koppel, S., McCarthy, D., & Srinivasan, S. (2008). In defence of the ‘low-mileage bias.’ Accident Analysis and Prevention, 40(6), 1996e1999. Loftus, E. F. (1993). The reality of repressed memories. American Psychologist, 48, 518e537. Lund, A. K., & Williams, A. F. (1985). A review of the literature evaluating the defensive driving course. Accident Analysis and Prevention, 17(6), 449e460. Maycock, G. (1997). Sleepiness and driving: The experience of UK car drivers. Accident Analysis and Prevention, 29(4), 453e462. McGwin, G., Jr., Owsley, C., & Ball, K. (1998). Identifying crash involvement among older drivers: Agreement between self-report and state records. Accident Analysis and Prevention, 30(6), 781e791. Michon, J. A. (1985). A critical review of driver behaviour models: What we know, what should we do? In L. Evans, & R. C. Schwing (Eds.), Human behavior and traffic safety (pp. 485e520) New York: Plenum. Mourant, R. R., & Rockwell, T. H. (1972). Strategies of visual search by novice and experienced drivers. Human Factors, 14(4), 325e335. Na¨a¨ta¨nen, R., & Summala, H. (1976). Road-user behavior and traffic accidents. Amsterdam/New York: North-Holland/Elsevier. ¨ zkan, T., Lajunen, T., Chliaoutakis, J. E., Parker, D., & Summala, H. O (2006). Cross-cultural differences in driving behaviours: A comparison of six countries. Transportation Research Part F: Traffic Psychology and Behaviour, 9(3), 227e242. ¨ zkan, T., Lajunen, T., & Summala, H. (2006). Driver Behaviour QuesO tionnaire: A follow-up study. Accident Analysis and Prevention, 38 (2), 386e395.

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Self-Report Instruments and Methods

Parker, D., Reason, J. T., Manstead, A. S. R., & Stradling, S. G. (1995). Driving errors, driving violations and accident involvement. Ergonomics, 38(5), 1036e1048. Paulhus, D. L. (1984). Two-component models of socially desirable responding. Journal of Personality and Social Psychology, 46(3), 598e609. Paulhus, D. L., & Reid, D. B. (1991). Enhancement and denial in socially desirable responding. Journal of Personality and Social Psychology, 60(2), 307e317. Quimby, A. R., & Watts, G. R. (1981). Human factors in driving performance (Report No. 1004). Crowthorne, UK: Transport and Road Research Laboratory. Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: A real distinction? Ergonomics, 33(10e11), 1315e1332. Sackeim, H. A., & Gur, R. C. (1979). Self-deception, other-deception, and self-reported psychopathology. Journal of Consulting and Clinical Psychology, 47, 213e215. Smith, R. S., & Wood, J. E. A. (1977). MemorydIts reliability in the recall of long distance business travel (Supplementary Report No. 322). Crowthorne, UK: Transport and Road Research Laboratory. Spolander, K. (1983). Bilfo¨rares uppfattning om egen ko¨rfo¨rma˚ga (Drivers’ assessment of their own driving ability) (Report No. 252). Linko¨ping: Swedish Road and Traffic Research Institute. Staplin, L., Gish, K. W., & Joyce, J. (2008). ‘Low mileage bias’ and related policy implicationsdA cautionary note. Accident Analysis and Prevention, 40(3), 1249e1252. Sullman, M. J. M., & Taylor, J. E. (2010). Social desirability and selfreported driving behaviours: Should we be worried? Transportation

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Research Part F: Traffic Psychology and Behaviour, 13(3), 215e221. Summala, H. (1980). How does it change safety margins if overtaking is prohibited: A pilot study. Accident Analysis and Prevention, 12(2), 95e103. Summala, H. (1985). Modeling driver behavior: A pessimistic prediction? In L. Evans, & R. C. Schwing (Eds.), Human behavior and traffic safety (pp. 43e65) New York: Plenum. Summala, H. (1987). Young driver accidents: Risk taking or failure of skills? Alcohol, Drugs and Driving, 3, 79e91. Summala, H. (1988). Risk control is not risk adjustment: The zero-risk theory of driver behaviour and its implications. Ergonomics, 31(4), 491e506. Summala, H. (1996). Accident risk and driver behaviour. Safety Science, 22(1e3), 103e117. Summala, H., & Hietamaki, J. (1984). Drivers’ immediate responses to traffic signs. Ergonomics, 27(2), 205e216. Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103, 193e210. Van Der Molen, H. H., & Botticher, A. M. T. (1988). A hierarchical risk model for traffic participants. Ergonomics, 31(4), 537e555. ¨ zkan, T., Lajunen, T., & Tzamalouka, G. (2010). CrossWalle´n Warner, H., O cultural comparison of driving skills. Submitted for publication. West, R., French, D., Kemp, R., & Elander, J. (1993). Direct observation of driving, self reports of driver behaviour, and accident involvement. Ergonomics, 36(5), 557e567. Wilson, T., & Greensmith, J. (1983). Multivariate analysis of the relationship between drivometer variables and drivers’ accident, sex, and exposure status. Human Factors, 25(3), 303e312.

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Chapter 5

Naturalistic Observational Field Techniques for Traffic Psychology Research David W. Eby Michigan Center for Advancing Safe Transportation throughout the Lifespan and University of Michigan Transportation Research Institute, Ann Arbor, MI, USA

1. INTRODUCTION As the name implies, naturalistic observation takes place in the setting in which the behavior of interest occurs. In terms of traffic psychology, this setting consists of the roadway network and the vehicle occupants who travel on these roadways. The research method of naturalistic observation involves a researcher (or, more commonly, several researchers) making careful observations about what he or she sees on the roadways. These observations can occur as the behavior is happening, or the behavior can be video recorded and observed at a later time. Naturalistic observation is a hallmark of scientific inquiry and is central to many empirical data collection efforts. There are two main strengths of this method. The first is that it taps directly into the behavior of interest and does not rely on having to interpret proxies of behaviors such as self-reports. Second, because the behaviors observed occur in natural settings, naturalistic observation has strong construct and face validity; that is, it very likely represents realitydan argument that is more difficult to make with other research methods, such as a driving simulator. On the other hand, naturalistic observation as a research method has some drawbacks. The main disadvantage is generalizability. Because the observed behaviors are only a sample of all of the behaviors that occur, it is difficult to conclude that the observed behaviors would also occur for other people who have not been observed. Although a good sampling design can minimize this issue, it cannot be eliminated. Another limitation is that the technique involves observers (data collectors) who may have biases that affect what they see and record. Such observer bias, however, can be diminished through training. Finally, naturalistic observational methods can be labor-intensive and, therefore, costly. To enhance generalizability and reduce observer bias, well-conducted studies utilizing naturalistic observation methods often require a team of well-trained researchers and many hours of observation during the study in which behaviors are observed.

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10005-0 Copyright Ó 2011 Elsevier Inc. All rights reserved.

Naturalistic observation was one of the earliest research methods used in traffic safety research. Indeed, based on naturalistic observation of driver behavior nearly a century ago, Dodge (1923) argued for a systematic exploration of human behavior in traffic to improve safety. This argument appeared in an article published in the proceedings of the Highway Research Board, now known as the Transportation Research Board. Naturalistic observation has been used extensively in the past century, and it is still commonly used today. An examination of the research methods used in highway safety studies reported in the first issue in 2010 of a highly rated traffic safety journal, Accident Analysis and Prevention, showed that approximately 10% of the studies used naturalistic observation for data collection. As a comparison, in this same issue, 55% of studies analyzed existing crash/injury databases; 16% used driving or crash simulation; 10% used self-reported questionnaire data; and the rest used other research techniques, such as radar-acquired proximity data from a vehicle.

2. TECHNIQUES The value of naturalistic observation techniques to any area of traffic psychology is dependent on how well the study is designed and executed. A study with a flawed design will not yield results that are generalizable, and even a welldesigned study will not be useful if the observational methods are not valid and reliable. This section discusses some of the issues related to conducting high-quality naturalistic observation research.

2.1. Deciding to Use Naturalistic Observation As discussed in the previous section, naturalistic observation is a very good method for answering many questions in traffic psychology and is the preferred method for some research questions. Conversely, there are many issues in 61

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traffic safety that are not amenable to naturalistic observation. How does one decide whether to use naturalistic observation or some other research method? Responding to several questions can be helpful: l

l

l

l

What is the purpose of your research project? If the purpose is to document the frequency or occurrence of some behavior, then this method might be useful. If you are interested in understanding the underlying causes of a behavior, then some other method would be more appropriate. Can the behavior of interest be accurately and reliably judged through visual inspection? As I have described, behaviors such as safety belt use in passenger vehicles can be easily seen. However, before I conducted my first large-scale survey of child safety seat use, I made sure that use could be observed through a number of pilot tests, including under controlled conditions using infant crash-test dummies and real-world settings (Eby & Kostyniuk, 1999; Eby, Kostyniuk, & Christoff, 1997). Not all in-vehicle behaviors can be accurately observed. What is the population of interest? It is important to think about this question for two reasons. First, it can be very difficult to design a cost-effective naturalistic observation study that is generalizable to a large population, such as the population of a state, region, or country. Second, some populations may be difficult to locate in natural settings. For example, a study on the use of safety belts in a certain make and model of vehicle might present sampling challenges while attempting to find enough of those specific vehicles for the survey to be valid. What are your resources? Naturalistic observation can be labor-intensive. Depending on the scope of the study and its design, available resources may not cover what is necessary for a high-quality research project.

Theories, Concepts, and Methods

Because direct observation allows for the vehicle occupants to see the researchers and know they are being observed, occupants may change the behavior of interest. I have witnessed, for example, occupants both putting on belts and taking them off during direct observation studies of safety belt use. This generally occurs when traffic queues at red lights and occupants have time to watch and think about what researchers are doing. This potential bias is often minimized by collecting data only on the first three or four vehicles that stop at a traffic light.

2.3. Variables As with any research project, the selection of variables is important. There are three classes of variables used in naturalistic observational studies: descriptive, inferential, and evaluative. For descriptive variables, a researcher simply records what he or she sees without any interpretation or inference. Examples of these variables include use or non-use of helmets or belts. In these cases, the vehicle occupant is either using the safety device or not. Inferential variables require the researcher to make an assumption about what he or she sees. For example, the visual assessment of an occupant’s age requires an inference based on visual cues. Observation of cell phone use is also inferential (particularly for hands-free use) because one has to assume that a conversation is taking place. The final type of variable, evaluative, requires the researchers to make both an inference and a judgment. For example, if one were interested in studying risky driving behaviors in a natural setting, the conclusion that a certain behavior was risky or not would be an evaluative variable. Evaluative variables are not commonly used in observational studies in traffic psychology.

2.4. Sampling Design 2.2. Direct versus Unobtrusive Observation Once naturalistic observation has been chosen as the research method, one still needs to consider whether to use direct or unobtrusive observation. In the context of traffic psychology, direct observation means researchers standing along roadways, or in some other location that is accessible to traffic, looking into vehicles and recording what they see. The researchers are clearly visible to vehicle occupants. In contrast, unobtrusive observation involves efforts to conceal the researchers from the vehicle occupants. Concealment can mean physically hiding the researchers as they collect data along the roadway or, more commonly, using camera or video technology that can be placed in inconspicuous locations (Elmitiny, Yan, Radwan, Russo, & Nashar, 2010; Retting, Williams, Farmer, & Feldman, 1999).

Perhaps the most common error in naturalistic observational studies is a poor sampling design. A good design is essential to be able to generalize results to the larger population being studied and to reduce potential bias. The goals of a good sample design are to select observation sites and times that accurately represent the behavior of interest, minimize survey error and bias, and be economically feasible to conduct. A complete discussion of sampling design is beyond the scope of this chapter, but a brief discussion of some of the issues is warranted. To maximize the generalizability of the survey results to the larger population, it is important to minimize potential biases in the design. One way to do this is to randomize as much of the design as possible. An ideal design, in terms of generalizability, would have completely random observation sites at which data collection takes place on random days and times. Thus, any location, day-of-week, or

Chapter | 5

Naturalistic Observational Field Techniques for Traffic Psychology Research

time-of-day biases in the behavior of interest would be minimized. This is not practical in practice, but there are ways to introduce randomization and still keep survey costs low. Another way to improve generalizability is to statistically weight the raw observational data to make it proportional to the larger population of interest. Any naturalistic observational study design will involve increments of time observing and recording the behavior of interest, known as sampling. The data collected during these relatively short sampling periods are considered to be reasonable estimates of the behavior that occurs when researchers are not observing (provided the sampled location and time were randomly selected). The accuracy of the estimate of the behavior’s frequency can be improved if it is corrected (weighted) by the actual population of interest. In traffic psychology, raw data are often weighted by traffic volumes or population numbers. Thus, a site where traffic volumes are high would count more toward the behavioral estimate than would sites with low traffic.

2.5. Observer Training Proper training of observers to conduct naturalistic observational studies is paramount. If the raw data are not collected accurately and efficiently, then the study will not yield valid results. There are two main issues concerning the training of observers. The first is training for consistency and accuracy. It is important that an observer collects data following the same procedures throughout the study, including following protocols and coding data identically each time the behavior is observed. Observers should practice the procedures prior to starting the actual study. The second issue is that if more than one observer is collecting data, all observers should be trained together and tested for interobserver reliability to ensure that the data collected by each observer are comparable. This can be

FIGURE 5.1 Observers training on direct observation methods in order to conduct a survey of safety belt use.

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done by having observers practice together looking into the same vehicles but recording data separately. The data can then be compared for consistency. If the interobserver reliability is lowdfor example, less than 85%dthen the observers should discuss how they are coding data and continue practicing until interobserver reliability is greater than 85%. Figure 5.1 shows two observers training to collect safety belt use data through direct observation.

2.6. Nighttime Observations There has long been interest in knowing about naturalistic driving behaviors at night, particularly the use of safety belts. The challenge, of course, is being able to visually assess the behavior of interest because of a lack of proper lighting. Early studies used the headlights of a van traveling on the road to illuminate the inside of a vehicle while an observer recorded data (Williams, Lund, Preusser, & Blomberg, 1987; Williams, Wells, & Lund, 1987). Later studies used locations that were well lit at night, such as parking lots (Lange & Voas, 1998; Malenfant & Van Houten, 1988). Recent studies, however, take advantage of newly developed military-grade night vision equipment to see inside vehicles (Chaudhary, Alonge, & Preusser, 2005; Chaudhary & Preusser, 2006; Vivoda, Eby, St. Louis, & Kostyniuk, 2007, 2008). The main advantage of night vision equipment is that any roadside location can be observed, which allows for the same level of generalizability as is obtained during daytime surveys. Two main night vision technologies are essential for nighttime observations. The first technology is the night vision goggle. Figure 5.2 shows two PVS-7B night vision goggles. The key features of the night vision goggle are the image intensifier tube and an integrated auto-gating

FIGURE 5.2 Night vision goggles that can be used for nighttime naturalistic observational studies. Source: Photograph courtesy of Jonathon M. Vivoda.

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3.1. Pedestrian and Bicyclist Behavior

FIGURE 5.3 Spotlight with infrared filter that is used with night vision goggles to illuminate the inside of vehicles during naturalistic observational studies at night. Source: Photograph courtesy of Jonathon M. Vivoda.

function. Auto-gating constantly operates to improve the quality of the image, not only during dayenighteday transitions but also under dynamic lighting conditions, such as the headlights of an approaching vehicle. The goggles can be handheld or positioned in a head mount. The other necessary technology is, preferably, a 2million candle power spotlight fitted with an infrared filter, as shown in Figure 5.3. When fitted with an infrared filter, this spotlight is invisible to vehicle occupants but brightly illuminates a vehicle interior when viewed by the night vision goggles. These spotlights are powered by portable battery packs. Although night vision equipment allows for much greater flexibility in nighttime naturalistic observational studies than was possible with previous methods, there are some differences from daytime surveys in both techniques and issues. Chaudhary, Leaf, Preusser, and Casanova (2010) discusses guidelines and techniques for conducting nighttime studies of safety belt use as well as general hints on how to effectively use night vision equipment to see into vehicles for any nighttime naturalistic observational study.

3. APPLICATIONS IN TRAFFIC PSYCHOLOGY Naturalistic observation methods have been applied to a number of problems in traffic psychology. Indeed, just about any behavior that can be judged visually can be studied using naturalistic observation. Here, some of this work is reviewed to demonstrate the breadth of issues that lend themselves to observational methods. Advances in technology have expanded the capabilities of naturalistic observation by allowing vehicles to be equipped with tiny video cameras and other sensors, known as instrumented vehicle research (Dawson, Anderson, Uc, Dastrup, & Rizzo, 2009; Dingus et al., 2006; Eby et al., 2009; Stutts et al., 2005). Instrumented vehicle and in-car recording research, however, are reviewed in Chapter 9 and will not be covered here.

According to the National Highway Traffic Safety Administration (NHTSA, 2009), there were 4378 fatalities and 69,000 injuries among pedestrians in the United States during 2008. Bicyclist fatality and injury rates are also high (Beck, Dellinger, & O’Neil, 2007; Kim, Kim, Ulfarsson, & Porrello, 2007). On the other hand, walking and bicycling have been advocated as a means of increasing youth exercise and combating weight problems (Orenstein, Gutierrez, Rice, & Ragland, 2007; Sirard & Slater, 2008) and also as a form of community mobility for older adults (AARP, 2005). Naturalistic observation methods are used extensively to determine pedestrian/ bicyclist volumes and characteristics (Diogenes, GreeneRoesel, Arnold, & Ragland, 2007; Orenstein et al., 2007), for classifying specific behaviors of pedestrians and bicyclists on or near roadways (Albert & Dolgin, 2010; Krizek, Handy, & Forsyth, 2009; Leden, Ga˚rder, & Johansson, 2006), and to understand motor vehicle drivers’ responses to pedestrians and bicyclists (Walker, 2007).

3.2. Driver Distraction Analyses by NHTSA (Ascone, Lindsey, & Varghese, 2009) indicate that of all fatal crashes in 2008, 16% were distraction related, and this percentage has been increasing every year since 2005. Eby and Kantowitz (2006) described the wide variety of potential distracters for drivers, including those that are outside of the vehicle and those that are inside. Naturalistic observation techniques could be applied to gain a better understanding of the frequency of in-vehicle distractions, including the presence of passengers, eating or drinking, smoking, adjusting vehicle controls, pets moving in the vehicle, and use of technology brought into the vehicle such as cellular (mobile) phones and navigation devices. Use of cellular phones and other in-vehicle technologies while driving has become an increasingly important issue in the United States and elsewhere (NHTSA, 2010). Epidemiological research has shown that use of cellular phones can cause a four- to ninefold increase in crash risk (Drews & Strayer, 2009). An important component of understanding driver distraction from technology use is assessing the magnitude of the problem. Handheld cellular phone use rates have been assessed through observational surveys for more than a decade (Eby & Vivoda, 2003; McCartt, Braver, & Geary, 2003; Reinfurt, Huang, Feaganes, & Hunter, 2001; Salzberg, 2002; Utter, 2001; Figure 5.4). Studies show that handheld cell phone use in the United States was approximately 6% at any given time in 2008 (Pickrell & Ye, 2009c), use of handheld cell phones while driving has been increasing (Eby, Vivoda, &

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Naturalistic Observational Field Techniques for Traffic Psychology Research

FIGURE 5.4 A driver talking on a handheld cellular phone while driving, as seen from the perspective of a researcher using naturalistic observation methods. Source: Photograph courtesy of Jonathon M. Vivoda.

St. Louis, 2006; Pickrell & Ye, 2009c), and use of handheld cellular phones is as frequent at night as it is during daytime (Vivoda et al., 2008). Observational studies of cellular phone use have also been conducted in a number of countries other than the United States and generally show lower use rates (Department for Transport, 2009; Horberry, Bubnich, Hartley, & Lamble, 2001; Johal, Napier, BrittCompton, & Marshall, 2005; Rajalin, Summala, Po¨ysti, Anteroinen, & Porter, 2005; Taylor, Bennett, Carter, & Garewal, 2003). Due in part to the passage of laws banning handheld cellular phone use in many jurisdictions (McCartt, Hellinga, & Bratiman, 2006) and in part to work showing that manually dialing a phone significantly raises crash risk (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006), many drivers are switching to hands-free cellular phones. Thus, there have been efforts to determine the frequency of hands-free phone use using direct observation techniques. Visually judging what constitutes use of a hands-free phone has proven challenging. For example, NHTSA’s National Occupant Protection Use Survey collects data on the use of cellular phones but does not use the phrase “hands-free cell phone use” when reporting results (Pickrell & Ye, 2009c). Instead, this study reports on drivers who were speaking with visible headsets with microphones, and it recognizes that this method has the following limitations: Wireless headsets obscured by hair are not included; some headsets may be too small to be seen; drivers who had on headsets but were not talking could have been using the phone but listening at the time; and drivers with headsets who were observed to be talking could have been talking to a passenger or themselves, singing, or using voice-activated software rather than using

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the phone. The UK Department for Transport (2009) reports hands-free cellular phone use and defines it as a driver who is clearly having a conversation with someone not in the vehicle, with an earpiece and/or mobile phone observed on the dashboard or steering wheel. Based on the difficulties and potential for biased results described in this section, I conclude that hands-free cellular phone use cannot be accurately measured using roadside naturalistic observation techniques. Although naturalistic observation techniques could be used for studying many types of in-vehicle distractions other than technology use, only one study has attempted to do so (Johnson, Voas, Lacey, & McKnight, 2004). This study used digital cameras linked with radar at a small set of locations along the New Jersey Turnpike. A random sample of vehicles were photographed when the radar indicated that vehicles were passing the camera. The resulting 38,745 high-definition images allowed trained coders to classify in-vehicle distractions along with other data, such as the presence of passengers, age, sex, and race. The study found that along with cell phone use, smoking, eating, drinking, and grooming were the most frequent invehicle distractions. Further work using naturalistic observation of in-vehicle distraction is warranted.

3.3. Risky Driving Behaviors A final area in which naturalistic observation techniques have been used in traffic safety research is in understanding unsafe and risky driving behaviors. Many behaviors have been studied using direct observation methods, including the use of turn signals and the use of daytime headlights (Zhang, Huang, Roetting, Wang, & Wei, 2006). Because of the implementation of red-light camerasdautomated systems that photograph violators and send citations through the maildin many jurisdictions, red-light running behavior has been extensively studied (Retting, Ferguson, & Farmer, 2008). For example, Retting et al. (1999) used inconspicuous cameras placed at intersections to estimate red-light-violation rates before and after red-light cameras were installed in a California city. Porter and England (2000) used trained observers sitting in cars at intersections to determine the characteristics of red-light violators in three southeastern Virginia cities. Other work has used video cameras to gather behavioral data to model the stop/ go decision at red lights and red-light running behavior (Elmitiny et al., 2010).

3.4. Occupant Protection Device Use More than for any other topic in traffic psychology, naturalistic observation methods are used for documenting the use, non-use, and misuse of safety restraints in vehicles. In fact, in the United States, the NHTSA requires statewide

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FIGURE 5.5 A researcher conducting a naturalistic observational study of child safety seat use.

safety belt use to be measured using naturalistic observation in order for states to qualify for federal incentive programs (NHTSA, 1998). NHTSA’s nationwide safety belt study, the National Occupant Protection Survey, also uses naturalistic observations (Pickrell & Ye, 2009a). Although there are no federal guidelines for occupant protection use measurement other than for safety belt use in passenger vehicles, naturalistic observation is also commonly used for identifying use rates for other types of restraints, including the use of infant and toddler seats (Decina & Knoebel, 1996, 1997; Eby & Kostyniuk, 1999; Figure 5.5), booster seats (Ebel, Koepsell, Bennett, & Rivara, 2003a, 2003b; Eby, Bingham, Vivoda, & Raghunathan, 2005; St. Louis et al., 2008), Lower Anchors and Tethers for Children (LATCH; Decina, Lococo, & Doyle, 2006), and motorcycle helmets (Kraus, Peek, & Williams, 1995; Li, Li, & Cai, 2008; Pickrell & Ye, 2009b). Although most studies of safety belt use are primarily concerned with determining overall use, many are also able to determine differences in belt use among various groups. Much of the data for determining trends can also be collected during naturalistic observation, provided the characteristic can be judged visually. For example, vehicle occupant age is often collected along with belt use (Eby, Molnar, & Olk, 2000; Eby, Kostyniuk, & Vivoda, 2001; Ulmer, Preusser, & Preusser, 1994; Williams et al., 1987). Because exact age is difficult to judge, it is often categorized into large groups, such as 0e15 years, 16e29 years, 30e64 years, and 65 years and older. A number of other visually judged characteristics have been collected in observational studies of belt use, including sex (Glassbrenner, 2003; Williams, McCartt, & Geary, 2003), seating position (Eby et al., 2000), vehicle type (Eby et al., 2000; Glassbrenner, 2003), commercial versus noncommercial vehicle (Eby, Fordyce, & Vivoda, 2002), and race

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(Glassbrenner, 2003; Vivoda, Eby, & Kostyniuk, 2004; Wells, Williams, & Farmer, 2002). Studies using naturalistic observation methods to explore the use of infant, toddler, and booster seats are often concerned with overall use rates, but they are often also focused on identifying misuse of these devices. For example, a statewide study of child restraint device use/ misuse in Michigan found at least some degree of misuse in 89% of cases (Eby & Kostyniuk, 1999). In a similar study conducted at select sites in four states, Decina and Knoebel (1997) found misuse in 80% of devices observed. As a result of these and other research studies, NHTSA recognized the difficulty many motorists have with properly installing these devices. In September 2002, NHTSA responded by launching an effort to make installation easier by requiring U.S. vehicles and child restraint device manufacturers to support a simpler installation system called LATCH. More work is still to be done, however, because an observational survey conducted in seven states found that the LATCH system was used in only 55e60% of LATCH-equipped vehicles (Decina et al., 2006). Naturalistic observation of motorcycle helmet use is generally straightforward, and the use rate is approximately 68% in states that mandate helmet use (Pickrell & Ye, 2009b). There are established standards for motorcycle helmets that ensure that the helmets perform at a certain level to prevent injury during a crash (U.S. Federal Motor Vehicle Safety Standard 218), and compliance of the helmet is signified by a U.S. Department of Transportation sticker on the back of the helmet. Of recent concern is the rate at which motorcycle riders use helmets that are approved by the Department of Transportation. Unfortunately, counterfeit stickers are available for noncompliant helmets. Many, but not all, helmets that do not cover the ears (so-called novelty helmets) are noncompliant with safety standard 218. Thus, it is difficult to determine use of compliant helmets using naturalistic observation, and many researchers simply classify compliant helmets as those that cover the ears or are at least 1 in. thick (Glassbrenner & Ye, 2006; Houry, Kellermann, White, & Corneal, 2005; Kraus et al., 1995; Pickrell & Ye, 2009b).

4. HOW TO DESIGN A COMMUNITYBASED SAFETY BELT USE SURVEY Because the use of safety belts is commonly studied with naturalistic observation techniques, I provide here an example design and procedures that can be followed to obtain a valid safety belt use rate for a given community. Further details about this example and software are available elsewhere (Eby, 2000). There are five steps to designing and conducting a valid study of belt use in a community.

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Naturalistic Observational Field Techniques for Traffic Psychology Research

4.1. Step 1: Get Organized To conduct a good survey of safety belt use, it is important to have a clear idea of what the survey is intended to achieve. A decision needs to be made about the exact boundaries of the survey area. The survey results will only be valid for the survey area. In general, the resources needed to conduct the survey will increase with the size of the survey area. Having a good map of the survey area is also essential. A decision will also need to be made about which types of vehicles to include in the survey and which occupant seating positions will be observed. U.S. Federal guidelines (NHTSA, 1998) require front outboard seating positions for passenger vehicles, which include cars, vans, minivans, sport utility vehicles, and pickup trucks. Passenger vehicles used for commercial purposes (e.g., police cars) and heavy trucks can either be included or excluded. Note that the survey results will only be valid for the vehicle types included in the survey. Vehicles included in the survey are commonly called target vehicles.

4.2. Step 2: Select Observation Sites Once the area and target vehicles have been defined, the next step is to select the intersections (observation sites) where researchers will conduct the observational survey. Before selecting the sites, however, one must determine how many sites are needed. Balancing the need for a large enough number of sites for a representative sample with the need to keep the number of sites low enough for a costeffective survey can be challenging. Fortunately, a great deal of research on belt use has been done, and these studies can be useful to help estimate the number of observation sites. Knowing the approximate belt use of an area allows one to more accurately pick the number of observation sites that will be necessary for the survey results to be meaningful. In the United States, nearly every state conducts and reports a statewide safety belt use rate. There may also be local belt use data available. These sources can be used to get a general idea of the belt use in your area and help you determine the proper number of sites. Table 5.1 provides the estimated number of observation sites that will be needed based on the expected belt use and error rates. Note that for most purposes, a 5% error rate is acceptable. See Eby (2000) for more information on selecting sites for an observational study. Once the number of sites has been determined, one needs to randomly select the locations of the sites. This can be done in at least two ways. The first is to acquire or develop a list of all intersections in the survey area and then pick intersections randomly without replacement. If a list is not available, then a more labor-intensive method can be used that involves drawing a grid pattern over a map of the survey area. A grid on a transparent overlay works well.

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TABLE 5.1 Estimated Number of Data Collection Sites Needed as a Function of Expected Belt Use Rate and Sampling Design Error Rate Expected Safety Belt Use Rate (%)

No. of Sites Needed Error Rate (%) 2

5

7

90

68

11

N/A

80

113

19

N/A

70

174

28

15

60

257

42

21

50

380

61

32

40

579

93

48

30

N/A

152

78

20

N/A

290

148

10

N/A

N/A

449

N/A, not applicable.

The cells in the grid should be small, the same size, and labeled by row and column. One then randomly picks a cell in the grid by randomly generating a row and column number that corresponds to a cell. If there is an intersection within the cell, then this intersection becomes one of the observation sites. If there is no intersection within the cell, then another cell is randomly picked. This process continues until the correct number of sites have been randomly located on the map. The selected sites should be clearly marked on the map. The final issue in selecting sites is to determine at each intersection exactly where the researcher will stand while conducting the safety belt use survey. The number of potential standing locations will depend on the type of intersection and whether or not the roadways are one-way or two-way travel. Regular intersections with two-way travel will have four legs of traffic entering the intersection, whereas “T” intersections will have three, and so on.

4.3. Step 3: Schedule the Survey Because safety belt use might systematically vary by location and time of day, it is important to introduce as much randomization as possible when scheduling the sites. Scheduling will also depend on the number of observers and other resources available for the study. Because a large portion of time and, therefore, cost is spent traveling between observation sites, this example presents a method that reduces travel time while maintaining as much

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randomization as possible in scheduling by grouping the sites. The grouping method involves first determining how many person-days are available to conduct the studydthat is, the number of field data collectors (observers) participating in the study and how many days they are available to collect data. For example, three data collectors who can each work for 4 days are equivalent to 12 person-days. The number of person-days is the number of groups in the study. The number of sites within each group is determined by dividing the total number of observation sites by the number of groups. In most cases, the quotient will not be a whole number, so once the number is rounded, some groups will have fewer sites than others. Next, one needs to look at the sites on the map and draw circles around sites that are next to each other, as shown in Figure 5.6. Within each circle should be the number of sites determined previously. Data collection will occur at each of the sites within the circle during a single day. Each group of sites should then be numbered. The scheduling of the exact day of week and time of day is done as follows. Randomly pick a day for each group of sites to be observed among the days that are available for the survey. If a day is selected that already has been selected for another group, then randomly pick another day.

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Theories, Concepts, and Methods

Scheduling the exact time of day for each site is more complicated. For each group, determine an order in which the sites will be conducted, as shown in Figure 5.6, by numbering each site within each circle consecutively. Then, randomly pick one of the sites to be the first site of the day, with the other sites being observed in the order they were numbered. For example, if there were five sites in the group and the randomly picked first site of the day was site 3, then the order of sites for the day would be 3, 4, 5, 1, and 2. The final part of scheduling is to randomly determine when the first site of the day for each group will be observed. To do this, one needs to first know the number of daylight hours during the day (for daytime studies). Next, the amount of time it will take to conduct observations at all of the sites in the group, the travel time between sites, and the time for breaks, meals, etc., should be added up to determine the total number of work hours for each group. Subtracting the number of work hours for the group from the number of daylight hours yields the number of hours in the morning in which the first site of the day for a group can be randomly scheduled. The start time for the first site should be randomly determined from these potential hours. All of the other sites for the group are then scheduled based on the randomly picked start time for the first site of the day, taking into account travel times, breaks, and meals. FIGURE 5.6 Example map with site groupings and a close-up of one group with sites numbered.

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Naturalistic Observational Field Techniques for Traffic Psychology Research

With this scheduling technique, one has minimized travel times while maintaining a high degree of randomness in selecting the day of week and time of day for observation sites.

4.4. Step 4: Collect Naturalistic Observation Data After training observers to conduct the naturalistic observation survey, the following procedures can be used at each site. Once the proper standing location and any site-specific information is collected (example data collection forms can be found in Eby, 2000), safety belt data collection will entail three parts. The first part is a 5-min count of all target vehicles entering the intersection from the assigned direction (the pre-survey vehicle count) in all travel lanes. The second part is to observe safety belt use for 50 min by recording belt use and any other desired demographic information for all occupant seating positions included in the study. The observer should only look into target vehicles in a single traffic lane if there is more than one present. If traffic is so heavy in the selected traffic lane that not all vehicles can be observed, then the observer should record data from one vehicle completely and then from the next target vehicle he or she sees when looking back up from the data collection form. The third part is to conduct another 5-min count of target vehicles (post-survey vehicle count) in the same way as the pre-survey vehicle count. Note that the 50-min observation time was chosen so that when the two 5-min counts are included, the total observation time is 60 minda duration that is convenient for scheduling sites.

4.5. Step 5: Estimate the Community Safety Belt Use Rate The community belt use rate is determined by calculating the weighted numbers of belted and total occupants at each site. A count for weighting each site is calculated by adding the pre- and post-target vehicle counts and multiplying this number by 5. This weighted count reflects the total number of target vehicles that should have passed the observer during the 50-min survey period if all target vehicles could have been surveyed. If this number is less than the actual number of vehicles surveyed, then the site weighting factor should be 1 and no weighting is done for that site. If the weighting count is larger, however, then the weighting factor for the site is computed by dividing the weighting count by the total number of target vehicles surveyed. This weighting factor should then be used to calculate the weighted number of belted occupants by multiplying the actual number of belted occupants surveyed by the weighting factor. A similar calculation

69

should be done for the total number of occupants. The community belt use rate (%) is calculated by dividing the weighted number of belted occupants across all sites by the weighted number of total occupants and multiplying the result by 100. The variance of the belt use estimate is computed as follows: n X gi 2 ðri rall Þ2 Variance ¼ gall n1 where n is the number of observation sites, gi is the weighted number of target vehicle occupants at observation site i, gall is the total weighted number of target vehicle occupants summed over all observation sites, ri is the belt use rate at site i, and rall is the overall belt use rate for the survey area. More detail on the procedures and weighting scheme can be found in Eby (2000). The 95% confidence interval can be computed as follows: 95% Confident interval ¼ belt use rate 1:96 pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ variance The relative error value, which is a measure of the precision of the belt use estimate, can be calculated using the following formula: pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ Variance Relative error ¼ Belt use rate Note that if belt use by certain demographic characteristics is desired, this can be obtained by determining separately weighted belted and total occupant counts for each demographic variable and using the same equations.

5. CONCLUSIONS This chapter presented a brief overview of naturalistic field observation in traffic psychology. This method has had a long history of use in the study of driver behavior and traffic safety, and it is still in common use today. Indeed, a wide range of topics in traffic psychology have been studied using naturalistic observation, and our understanding of these topics has improved greatly as a result. Naturalistic observational studies, however, require appropriate study designs, reproducible protocols, extensive observer training, and adequate resources to yield valid and generalizable results. The example design and protocols for a community-based safety belt survey were presented to help illustrate the issues and challenges of conducting naturalistic observational studies. The design, procedures, and analytic techniques presented in the example can be used directly for developing and implementing a small-scale study of belt use or other in-vehicle behaviors.

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ACKNOWLEDGMENTS This chapter was developed through the support of the Michigan Center for Advancing Safe Transportation throughout the Lifespan, a University Transportation Center sponsored by the U.S. Department of Transportation’s Research and Innovative Technology Administration (Grant No. DTRT07-G-0058), University of Michigan (U-M), U-M Transportation Research Institute, and donations from several organizations. Jonathon M. Vivoda, Lisa J. Molnar, and Rene´e St. Louis provided valuable feedback. Amanda Dallaire provided administrative support. The contents of this chapter reflect the views of the author, who is responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.

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Naturalistic Observational Field Techniques for Traffic Psychology Research

Glassbrenner, D. (2003). Safety belt use in 2003 (Report No. DOT HS 809 646). Washington, DC: U.S. Department of Transportation. Glassbrenner, D., & Ye, J. (2006). Traffic safety facts: Research note. Motorcycle helmet use in 2006dOverall results (Report No. DOT HS 810 678). Washington, DC: U.S. Department of Transportation. Horberry, T., Bubnich, C., Hartley, L., & Lamble, D. (2001). Drivers’ use of hand-held mobile phones in Western Australia. Transportation Research Part F: Traffic Psychology and Behavior, 4, 213e218. Houry, D., Kellermann, A., White, M., & Corneal, K. (2005). “Phony” motorcycle helmet use in Georgia. American Journal of Emergency Medicine, 23, 409e410. Johal, S., Napier, F., Britt-Compton, J., & Marshall, T. (2005). Mobile phones and driving. Journal of Public Health, 27, 112e113. Johnson, M. B., Voas, R. B., Lacey, J. H., & McKnight, A. S. (2004). Living dangerously: Driver distraction at high speed. Traffic Injury Prevention, 6, 1e7. Kim, J.-K., Kim, S., Ulfarsson, G., & Porrello, L. A. (2007). Bicyclist injury severity in bicycleemotor vehicle accidents. Accident Analysis and Prevention, 39, 238e251. Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact on driver inattention on near crash/ crash risk: An analysis using the 100 Car Naturalistic Driving Study data (Report No. DOT HS 810 594). Washington, DC: U.S. Department of Transportation. Kraus, J. F., Peek, C., & Williams, A. (1995). Compliance with the 1992 California motorcycle helmet use law. American Journal of Public Health, 85, 96e99. Krizek, K. J., Handy, S. L., & Forsyth, A. (2009). Explaining changes in walking and bicycling behavior: Challenges for transportation research. Environment and Planning B: Planning and Design, 36, 725e740. Lange, J. E., & Voas, R. B. (1998). Nighttime observations of safety belt use: An evaluation of California’s primary law. American Journal of Public Health, 88, 1718e1720. Leden, L., Ga˚rder, P., & Johansson, C. (2006). Safe pedestrian crossings for children and elderly. Accident Analysis and Prevention, 38, 289e294. Li, G. L., Li, L. P., & Cai, Q. E. (2008). Motorcycle helmet use in southern China: An observational study. Traffic Injury Prevention, 9, 125e128. Malenfant, J. E. L., & Van Houten, R. (1988). The effects of nighttime seat belt enforcement on seat belt use by tavern patrons: A preliminary analysis. Journal of Applied Behavior Analysis, 21, 271e276. McCartt, A. T., Braver, E. R., & Geary, L. L. (2003). Drivers’ use of handheld cell phones before and after New York State’s cell phone law. Preventive Medicine, 36, 629e635. McCartt, A. T., Hellinga, L. A., & Bratiman, K. A. (2006). Cell phones and driving: Review of research. Traffic Injury Prevention, 7, 89e106. National Highway Traffic Safety Administration. (1998). Uniform criteria for state observational surveys of seat belt use (Docket No. NHTSA98-4280). Washington, DC: U.S. Department of Transportation. National Highway Traffic Safety Administration. (2009). Traffic safety facts: 2008 Data. Pedestrians (Report No. DOT HS 811 163). Washington, DC: U.S. Department of Transportation. National Highway Traffic Safety Administration. (2010). Overview of the National Highway Traffic Safety Administration’s Driver Distraction

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Program (Report No. DOT HS 811 299). Washington, DC: U.S. Department of Transportation. Orenstein, M. R., Gutierrez, N., Rice, T. M., & Ragland, D. R. (2007). Safe routes to school safety and mobility analysis. Safe Transportation Research & Education Center. Berkeley: University of California Berkeley. Pickrell, T. M., & Ye, T. J. (2009a). Traffic safety facts: Research note. Seat belt use in 2008dOverall results (Report No. DOT HS 811 036). Washington, DC: U.S. Department of Transportation. Pickrell, T. M., & Ye, T. J. (2009b). Traffic safety facts: Research note. Motorcycle helmet use in 2009dOverall results (Report No. DOT HS 811 254). Washington, DC: U.S. Department of Transportation. Pickrell, T. M., & Ye, T. J. (2009c). Traffic safety facts: Research note. Driver electronic device use in 2008 (Report No. DOT HS 811 184). Washington, DC: U.S. Department of Transportation. Porter, B. E., & England, K. J. (2000). Predicting red-light running behavior: A traffic safety study in three urban settings. Journal of Safety Research, 31, 1e8. Rajalin, S., Summala, H., Po¨ysti, L., Anteroinen, P., & Porter, B. E. (2005). In-car cell phone use and hazards following hands free legislation. Traffic Injury Prevention, 6, 225e229. Reinfurt, D. W., Huang, H. F., Feaganes, J. R., & Hunter, W. W. (2001). Cell phone use while driving in North Carolina. Chapel Hill: University of North Carolina Highway Safety Research Center. Retting, R. A., Ferguson, S. A., & Farmer, C. M. (2008). Reducing red light running through longer yellow signal timing and red light camera enforcement: Results of a field investigation. Accident Analysis and Prevention, 40, 327e333. Retting, R. A., Williams, A. F., Farmer, C. M., & Feldman, A. F. (1999). Evaluation of red light camera enforcement in Oxnard, California. Accident Analysis and Prevention, 31, 169e174. Salzberg, P. (2002). Cell phone use by motor vehicle drivers in Washington State. Olympia: Washington Traffic Safety Commission. Sirard, J. R., & Slater, M. E. (2008). Walking and bicycling to school: A review. American Journal of Lifestyle Medicine, 2, 372e396. St. Louis, R. M., Parow, J. E., Eby, D. W., Bingham., C. R., Hockanson, H., & Greenspan, A. I. (2008). Evaluation of communitybased programs to increase use of booster seats. Accident Analysis and Prevention, 40, 295e302. Stutts, J., Feaganes, J., Reinfurt, D., Rodgman, E., Hamlett, C., Gish, K., & Staplin, L. (2005). Driver’s exposure to distraction in their natural driving environment. Accident Analysis and Prevention, 37, 1093e1101. Taylor, D. M., Bennett, D. M., Carter, M., & Garewal, D. (2003). Mobile telephone use among Melbourne drivers: A preventable exposure to injury risk. Medical Journal of Australia, 179, 140e142. Ulmer, R. G., Preusser, C. W., & Preusser, D. F. (1994). Evaluation of California’s safety belt law change to primary enforcement (Report No. DOT-HS-808-205). Washington, DC: U.S. Department of Transportation. Utter, D. (2001). Research note: Passenger vehicle driver cell phone use results from the fall 2000 National Occupant Protection Use Survey (Report No. DOT HS 809 293). Washington, DC: U.S. Department of Transportation. Vivoda, J. M., Eby, D. W., & Kostyniuk, L. P. (2004). Differences in safety belt use by race. Accident Analysis and Prevention, 36, 1105e1109.

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Vivoda, J. M., Eby, D. W., St. Louis, R. M., & Kostyniuk, L. K. (2007). A direct observation study of nighttime belt use in Indiana. Journal of Safety Research, 38, 423e429. Vivoda, J. M., Eby, D. W., St. Louis, R. M., & Kostyniuk, L. P. (2008). Cellular phone use while driving at night. Traffic Injury Prevention, 9, 37e41. Walker, I. (2007). Driver overtaking bicyclists: Objective data on the effects of riding position, helmet use, vehicle type and apparent gender. Accident Analysis and Prevention, 39, 417e425. Wells, J. K., Williams, A. F., & Farmer, C. M. (2002). Seat belt use among African Americans, Hispanics, and whites. Accident Analysis and Prevention, 34, 523e529.

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Theories, Concepts, and Methods

Williams, A. F., Lund, A. K., Preusser, D. F., & Blomberg, R. D. (1987). Results of a seat belt use law enforcement and publicity campaign in Elmira, New York. Accident Analysis and Prevention, 19, 243e249. Williams, A. F., McCartt, A. T., & Geary, L. (2003). Seat belt use by high school students. Injury Prevention, 9, 25e28. Williams, A. F., Wells, J. K., & Lund, A. K. (1987). Shoulder belt use in four states with belt use laws. Accident Analysis and Prevention, 19, 251e260. Zhang, W., Huang, Y.-H., Roetting, M., Wang, Y., & Wei, H. (2006). Driver’s views and behaviors about safety in ChinadWhat do they not know about driving? Accident Analysis and Prevention, 38, 22e27.

Chapter 6

Naturalistic Driving Studies and Data Coding and Analysis Techniques Sheila G. Klauer, Miguel Perez and Julie McClafferty Virginia Tech Transportation Institute, Blacksburg, VA, USA

1. INTRODUCTION Governments and citizens worldwide are mobilizing to reduce motor vehicle fatality and injury rates. Sweden has a goal of zero fatalities (Vision Zero) nationwide by 2020, and many European countries are following Sweden’s lead with major reduction goals of their own. The Chinese government has doubled its spending on transportation every year for the past 5 years and has set goals for reductions in fatalities and automobile injuries. The United States has set ambitious safety goals for commercial drivers but is also set on changing the U.S. safety culture regarding distracted driving (see the Department of Transportation’s website at www.distraction.gov). Although roadway fatality and injury rates have dropped significantly during the past 50 years, these reductions have been primarily the result of improved safety belt use, air bag technology, improved crashworthiness of automobiles, and improved infrastructure (i.e., better guardrail design, roadway lighting, etc.). These improvements have had a major impact on fatality and injury rates; however, it is generally acknowledged that any further reduction in fatality and injury rates will be due to modifications in driver behavior. It is widely accepted that driver error contributes to more than 90% of all automobile crashes (Lum & Reagan, 1995). To better understand human errors made while driving, traffic safety professionals have used either epidemiological research methods or controlled experimentation. Large crash databases based on information gleaned from police accident reports have been useful for broad questions but lack sufficient detail to study driver behavior that results in a crash. Although empirical research possesses sufficient detail, these data are collected in contrived environments and are unable to adequately capture normal driving environments and/or real crash situations. Technological improvements have enabled traffic safety researchers to better study driver behavior in situ or in real-world traffic environments. Improvements in computer Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10006-2 Copyright Ó 2011 Elsevier Inc. All rights reserved.

processing speed and data storage coupled with the reduction in physical size of these components have not only allowed instrumented vehicle studies to gather more parametric data but also resulted in vast improvements in video data collection. These improvements have not only allowed safety professionals to retrofit vehicles with stateof-the-art eye tracking systems, physiological monitoring equipment, and collision warning systems but also allowed for the large-scale collection of driving performance data for long periods of time (e.g., 100 vehicles for 1 year). It is these technological improvements that will help to increase the amount of data regarding driver behavior in the seconds leading up to crashes. Thus, safety professionals will be able to assess data on actual crashes (like epidemiological databases) with high-resolution, detailed driving performance data (like empirical studies). These instrumented vehicle studies are an important tool for researchers to add to their safety tool box to improve safety on our roadways. This chapter describes the traffic conflict technique and the theory behind the power of instrumented vehicle or naturalistic driving studies, the life cycle of naturalistic driving studies, and powerful analytic techniques that can and have been used with these data. Although instrumented vehicle studies range from one vehicle for a 30-min test period with an experimenter present to large-scale deployment of instrumented vehicles with data collected over a long period of time, this chapter focuses on the larger scale deployment studies, also known as naturalistic driving studies.

2. TRAFFIC CONFLICT TECHNIQUE Many industrial safety researchers face challenges similar to those faced by transportation researchers when attempting to directly measure safety or predict the probability that an accident will occur given certain circumstances. In most settings, accidents that lead to 73

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injury or death are fairly rare events, and therefore any corrective action is reactive rather than proactive. Thus, it would be very beneficial to safety engineers if they were able to be more proactive in their abilities to determine unsafe acts that may eventually lead to injury or death. Heinrich, Petersen, and Roos (1980) developed a hazard analysis technique based on the underlying premise that for every injury accident, there are many similar accidents in which no injury occurs. For example, for a unit group of 550 accidents of similar type and involving the same person, approximately 500 of these accidents would result in no injury, 49 would result in minor injuries, and only 1 may result in a major injury. The theory suggests that the same contributing factors occur for the no injury and minor injury accidents as for the major injury accidents. Thus, if the industrial engineer can identify contributing factors and reduce the number of no injury and minor injury accidents, it is possible to prevent the major injury accidents. The relationship among major injury accidents, minor injury accidents, and no injury accidents is called Heinrich’s triangle (Figure 6.1). Following this premise, transportation researchers from GM developed a method using cameras and observing traffic conflicts at intersections (Parker & Zegeer, 1989). Their general definition of a traffic conflict is as follows: “An event involving two or more road users, in which the action of one user causes the other user to make an evasive maneuver to avoid a collision” (p. 4). In the field of transportation research, this hazard analysis method has become known as the traffic conflict technique. This method has been used to estimate crash risk at intersections using a count of traffic conflicts rather than crashes. Wierwille et al. (2002) employed the traffic conflict technique by unobtrusively videotaping traffic at intersections to identify causes of driver errors (critical incidents), near-crashes, and crashes. They chose rural, suburban, and urban intersections that had a high percentage of collisions. Variations of the traffic conflict technique have been developed for use on an instrumented vehicle. This modification involves cameras being strategically placed on one vehicle to determine the number of traffic conflict

1

Major injury

29

Minor injury

300

No injury accidents

FIGURE 6.1 Heinrich’s triangle for industrial safety suggests that for a similar industrial accident, there are hundreds of no injury accidents and tens of minor injury accidents for every one major injury accident. Thus, if the frequency of no injury and minor injury accidents can be reduced, the major injury accidents would also be reduced.

Theories, Concepts, and Methods

involvements for a particular driver (Dingus et al., 2001; Hanowski, Wierwille, Garness, & Dingus, 2000; Mollenhauer, 1998; Wierwille et al., 2001). Dingus et al. (2006) used this modified version of the traffic conflict technique in their “100-Car Study” by videotaping a single driver and the environment surrounding a single vehicle to identify driver errors (critical incidents), near-crashes, and crashes that impacted the instrumented vehicle. This technique proved valuable in identifying the impacts of fatigue and distraction on light vehicle drivers and how fatigue and distraction increase crash risk among light vehicle drivers.

3. PHILOSOPHY OF LARGE-SCALE INSTRUMENTED VEHICLE STUDIES Large-scale instrumented vehicle studies can be conducted to assess the safety aspects of a particular in-vehicle system (these types of studies are also called field operational tests) or to better understand the driver behaviors that result in crashes. For both, it is important to note that it is the driver behavior in real-world conditions that is of the utmost importance for all resulting analyses. Instrumented vehicle studies are conducted to assess driver behavior under normal, daily pressures, on normal routes, and under normal traffic conditions. Thus, external validity will remain very high at the cost of internal validity or experimental control. Although the experimenter cannot control the type of traffic patterns, environmental conditions, or driver state, the researcher will collect large amounts of data to assess and categorize these variables of interest. Thus, researchers recruit large numbers of participants and each participant experiences a long, continuous data collection period to ensure that adequate data are collected in all types of traffic patterns, environmental conditions, and driver states to make statistically valid assessments. The belief that large amounts of data will provide the critical amount of data is paramount in epidemiological research and is also true for large-scale instrumented vehicle studies. One of the primary strengths of naturalistic driving studies is that they provide high-resolution driving performance data (parametric data) coupled with video data. These high-resolution data provide a rich and precise source of information about a driver’s behavior in a normal driving environment. These rich data sources are extremely valuable due to their precision regarding driver behavior. From these data sources, very large data sets (i.e., multiple terabytes) can also be produced that yield hundreds of nearcrashes and tens of crashes. For example, in Dingus et al.’s (2006) 100-Car Study, continuous data were collected on 109 vehicles for a minimum of 12 months. The resulting data set was slightly more than 42,000 hours of driving data, more than 6 terabytes (TB) of video, and it included

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69 crashes, 761 near-crashes, and 8295 incidents. Depending on one’s background, this is either a large data set (6 TB) or a small data set (i.e., only 69 crashes). The important aspect is the precision of these data, whether they contribute to the large 6-TB data set or the 69 crashes. In both cases, the video of the driver’s behavior with the corresponding vehicle parametric data (i.e., vehicle speed, deceleration, Global Positioning System (GPS) location, lane position, lane deviation, etc.) yields a very rich data set. These precise data lose power if the drivers are constantly reminded that they are participating in a driving study. In the spirit of maintaining “real-world” driving conditions, it is imperative that the drivers are not needlessly reminded that they are participating in a driving study. Thus, the instrumentation should be as minimal and unobtrusive as possible. The participants should not be required to consciously interact (i.e., turn the system on/off) with the data collection system, nor should the drivers be forced to drive to or from data collection points, etc. The drivers should be allowed to go about their daily lives as unimpeded as possible in order to collect the most realistic driving data under normal conditions.

4. LIFE CYCLE OF NATURALISTIC VEHICLE STUDIES Planning successful naturalistic driving studies requires the following four steps (Figure 6.2): 1. 2. 3. 4.

Study design and data collection Data preparation and storage Data coding Data analysis

4.1. Study Design and Data Collection For the completion of a successful naturalistic driving study, well-defined research questions should guide the selection of the critical data elements collected in the naturalistic

Study design and data collection

Data preparation and storage

Data analysis

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study, the appropriate data analysis plan, and the successful design of the study. Although these steps may be critical for all research studies, the versatility and flexibility of naturalistic driving studies make it tempting to collect a large volume of data that do not directly relate to the primary research objectives. This is sometimes advantageous, but it will typically result in more expense and consumption of resources. Thus, researchers must carefully weigh the advantages and disadvantages of adding additional variables. Second, given the large volume of data collected, it is even more important to understand and plan for the analyses as part of the study design. If the data analysis plan truly dictates the data needs, the analyses will be successful. If this step is not thoughtfully considered, the data collected will not appropriately answer the research questions or will require extensive and resource-intensive data processing to produce the data to answer the research questions. The appropriate study design will lead to the selection of the appropriate participant population as well as the collection of the appropriate video views and parametric data. Although there are a wide variety of potential data elements, commonly used elements are presented in Table 6.1. As part of the study design, participants must be selected and recruited who are of the appropriate age, gender, and demographic distribution. Participants must also be protected because naturalistic driving procedures collect large amounts of identifying data. Identifying data not only include the driver’s face video data but also the GPS data that could indicate the location of residence, workplace, etc. Sensitivity and strict data collection procedures must be followed to ensure that participants’ privacy is protected. For researchers in the United States, applying for a Certificate of Confidentiality from the National Institutes of Health is a key step in protecting participant privacy. This certificate ensures that participants’ identifying data cannot be subpoenaed for use in legal proceedings due to their participation in the research program. For example, if a participant made a serious error while on camera that resulted in an injury or fatal crash, the participant’s data should be protected from subpoena for use in court proceedings or insurance negotiations. Without this protection, safety research would prove very difficult because recruitment of driving study participants would be severely restricted.

4.2. Data Preparation and Storage

Data coding FIGURE 6.2 Life cycle of naturalistic driving studies

After the key data elements have been selected and incorporated into the data acquisition system (DAS), the appropriately sized data storage device must also be selected for the DAS. The collection of continuous data from vehicles, both video and parametric data, is not trivial.

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TABLE 6.1 Description of Potential Sensor Technologies Used in Naturalistic Driving Studies Sensor Component

Description

Vehicle network box

Collection of data directly from the in-vehicle network box. Data include vehicle speed, brake application, percentage throttle, use of turn signals, and seat belt use.

Accelerometer

Collection of lateral, longitudinal, and gyro.

Forward headway detection

Collection of radar data (range, range-rate, azimuth, etc.) to indicate the presence of up to seven targets in front of the vehicle.

Rear headway detection

Collection of radar data (range, range-rate, azimuth, etc.) to indicate the presence of up to seven targets behind the vehicle.

Side vehicle detection

Collection of radar data indicating the presence of a vehicle on the sides of the vehicle.

Global Positioning System (GPS)

Collection of latitude, longitude, and horizontal velocity as well as other GPS-related variables.

Automatic collision notification system

High bandwidth collection of acceleration to detect a severe crash.

Cellular communications

Communication system designed for vehicle tracking and system diagnostics.

Driver-identified events/glare sensor

Collection of lux value (for nighttime conditions only) as well as event button.

Lane position

Collection or processing of video data to identify the vehicle position in between lane markings.

Video data

Multiple video views are typically collected. These might include forward view, rear view, driver’s face, over the driver’s shoulder, rearward from the passenger side, interior cabin view, driver’s foot/pedal view, and dashboard/instrument panel view. Infrared lighting is used in the vehicle cabin to ensure visibility of driver’s behavior at night.

Driver’s head position

Collection of driver’s head position that can serve as a gross measure of driver distraction.

Driver eye glance

Collection of driver’s eye glance location. These typically require calibration.

Drowsy driver detector

Collection of driver’s eye glance patterns that signals when a driver’s eyelids are closing or are closed.

Passenger detector

Uses sensors in the seat to detect if there is sufficient weight in the seat to suggest passenger presence.

System initialization

Overall system operation.

Reasonable quality video data rates are 6e8 megabytes of video per minute, which results in a passenger vehicle collecting 20 gigabytes of data per month. The video data typically comprise 80e95% of the total data collection compared to 5e15% for the vehicle parametric data. Data collection at these rates currently precludes the use of wireless data transfer. Thus, the DAS must also incorporate data storage systems that can be easily removed and replaced, such as flash drives, hard disks, or solid-state disk devices. These data are removed from the vehicle and copied to permanent data storage for subsequent data preparation and eventual analysis. After retrieving data from the vehicles, these data are copied and stored on network file servers for subsequent data processing and analysis. The selection of storage architecture can have considerable implications for the research project. When selecting file servers, key features such as processing speed, file servers that deliver data to

researcher workstations, and links to database servers for hosting annotated data sets are critical components. After the vehicle data are uploaded to the servers, software on these servers perform initial analyses, check data quality, summarize, and prepare the data for import into the data warehouse. Other processes may be applied at this time, such as any automated analyses of video data (i.e., automatic identification of driver via face video). Following this, the sensor data are imported into the data warehouse using standard extract, transform, and load processes. These data can then be made available to researchers to perform subsequent data mining and analysis.

4.3. Data Coding Data coding using naturalistic driving data involves training multiple data coders to review the video and driving performance data and then record observed driver

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Naturalistic Driving Studies and Data Coding and Analysis Techniques

behaviors. To glean the rich driver behavior data from the video, some form of data coding will likely be required to aid in the usability of the data. Various examples of the power of data coding include obtaining estimates of relative risk for driving behavior, cataloging exposure, evaluating the sequence of actions in the seconds prior to crashes and near-crashes, and recording environmental/roadway variables that are not automatically recorded. One goal of data coding is to define a relatively complete set of data elements, while not precluding further data coding in the future, and to make the database more directly useful for search and analysis. Given the vast amounts of data collected in naturalistic driving studies, it is not feasible to observe and record measurements for all data collected. Thus, depending on the research objectives, it is important to devise a method to automatically scan the driving performance data to identify moments in time when further data coding would be beneficial (i.e., automated trigger). At a top level, manual data coding will be used to calibrate automated triggering based on driving performance data. Prior experience with automated triggering will be very useful in setting the initial criteria. However, some calibration will likely be required in order to use the upgraded DAS to its fullest potential. The goal of trigger calibration is to design triggering criteria so that as many potentially relevant moments are captured along with as few invalid (noncritical) triggers as possible. Several types of automated triggering can be performed, with the most common types using vehicle kinematic data, GPS location data, or random selection. Triggering using the vehicle kinematic data identifies moments in time when the driver exceeds a set value (i.e., -0.55 g longitudinal deceleration) or a combination of values (i.e., -0.55 g longitudinal deceleration and time-to-collision value of 2.0 s or less). Both researchers and practitioners have used kinematic triggered data to identify safety critical events, such as crashes, near-crashes, or critical incidents, or identify “coachable” events when drivers are exceeding the safe limits of their vehicle. Using kinematic data to identify safety-relevant events is process intensive. First, trained data reductionists view a sample of triggers created under a given set of criteria and place each trigger into a “valid” or “invalid” category. For example, to create triggers to identify crashes and nearcrashes, valid triggers represent any conflict that did or could have resulted in a crash. Invalid triggers may appear as normal driving and are often driver specific (e.g., a driver who normally breaks hard) or as anomalies in the performance data. When criteria are tightened up to reduce the number of invalid triggers, this review of a sample of triggers is repeated, and a cross-check is also performed to ensure that the valid triggers found with the less restrictive criteria are still found with the more restrictive criteria and

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that the number of invalid triggers is reduced. When the criteria are finalized and the final triggers are generated, event classification begins. GPS location can also be used to identify when a driver is negotiating a geographic location of interest. Locations of interest can include a specific geographic location or a geographic region. In the case of a geographic region, it is possible to use a “geofence” to encompass an area of interest to determine if the driver entered this area. These data, identified by research question, can include a priori defined intersections, merge ramps, road segments, shopping districts, or simply distance away from home that is not advisable for a novice driver. GPS data can also be used in conjunction with other sensor data such as vehicle speed. For example, all events when vehicle speed exceeds 70 mph and the vehicle is not on an interstate can be used to identify those moments when the driver is most likely speeding greater than 15 mph. Trained data coders typically must evaluate these trigger criteria as well, depending on how the GPS trigger is developed. It may also be important to include a heading value and/or sequence of GPS locations to ensure that the events selected are not vehicles driving on an overpass (i.e., on top of an intersection of interest) and/ or through a geographic location from the preferred direction (i.e., traveling on a roadway through a specific intersection with multiple turn lanes/traffic signals). Random selection of events can also be conducted to obtain a measure of “normal” or baseline driving. These samples are typically stratified based on a driver’s vehicle miles traveled or some other measure to ensure a normalized distribution of baseline samples. There are difficulties with the stratification procedure because the appropriate information is not always known throughout the entire data set (i.e., who the driver is for every given trip). Thus, the researcher must carefully select and establish appropriate selection criteria that will produce a statistically valid sample of baseline driving. After the sample of events has been selected and identified, trained data coders then review the events. Trained data coders watch the appropriate video and the corresponding kinematic data for each of these events to verify that each event met all of the selection criteria, and then they record the relevant event, driver behaviors, environmental and roadway variables, and scenario-specific variables of interest. These elements include the following: l

Event variables: Variables used to establish the scenario and sequence of events prior to and through the critical event. These variables include event severity, event nature (e.g., conflict with lead vs. crossing vehicle), preincident maneuver, precipitating event, driver reaction, post-maneuver control, information about other drivers/ vehicles/objects involved (e.g., type, position, maneuvers, and impairments), and fault assignment.

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Driver variables: Variables used to systematically describe driver state prior to and during the critical event. These include driver ID, driver behavior (e.g., speeding and aggressive driving), driver impairments (e.g., drowsiness, anger, and substance abuse), secondary task engagement and duration (e.g., cell phone use), placement of hands on the wheel, visual obstructions, and seat belt use. Environmental variables: Variables used to describe environmental and/or roadway conditions consistent with General Estimates System and other crash databases. These include roadway surface condition, traffic flow, number of travel lanes, traffic density, traffic control device at event onset, relation to junction of vehicle at event onset, roadway alignment (e.g., curve and grade), locality type (e.g., residential and interstate), ambient lighting, weather, and windshield wiper status.

The research objectives and research questions will dictate the types of data triggering and coding that are required for each project. The researcher will then develop the triggers and the coding protocols for the trained data coders to follow to ensure that the coders record the appropriate information. Data coding quality and control is a critical element and is the topic of the next section.

4.3.1. Coder Training and Quality Control Policies A suggested data coding quality assurance/quality control (QA/QC) workflow that has been developed, tested, and implemented by the Virginia Tech Transportation Institute (VTTI) is provided here. This workflow has four phases, all of which are equally critical to the quality of manual coding data. These phases are protocol development, coder training, data coding, and post-coding, with tasks assigned as each level of one of four roles. Each role and phase is discussed here and also illustrated in Figure 6.3. The following four roles are critical to the data reduction process: 1. The researcher or research manager (either internal or external) oversees the research project from research design to data collection, coding, analysis, and reporting. At the data coding step, the researcher takes the lead in protocol and data dictionary development and provides input and feedback throughout all four phases. 2. The data coding manager serves as the direct liaison between the researcher and the data coding team and oversees all QA/QC steps. Most questions from coders can be fielded by the data coding manager; those that cannot are taken to the researcher. 3. Senior data coders (or lab proctors) are generally experienced, high-quality data coders who monitor

Theories, Concepts, and Methods

a project’s progression through the QA/QC workflow, assist the data coder manager with coder training, test new protocols before coding work begins, create and score tests to formally measure coder reliability, and monitor the workflow to coders. 4. Data coders, of course, perform the bulk of the data coding. They also participate in the QA/QC process by completing required tests and assisting with spot checks. Data coders are ideally limited to working no more than 4 or 5 h per day. 4.3.1.1. Phase 1: Protocol Development QA/QC of manual data coding actually begins before coders ever see a protocol. After the researcher has drafted a preliminary protocol and data dictionary based on the research questions, it usually goes through several rounds of review with the data coder manager. Due to the reduction-intensive and exclusive nature of the data coder manager’s work, this person often has more experience in finding potential ambiguities, knowing when categories may be missing from certain variables, and adapting new protocols to be consistent (if possible) with previous protocols for later cross-analysis. After both the researcher and the data coder manager are satisfied with the draft protocol, it enters the first loop to be tested. The senior coder takes the lead in this testing by viewing a variety of events and completing the coding for those events based on the draft protocol. As the senior coder works, he or she takes notes about where uncertainties arose, what types of events were difficult to categorize, other variables that may seem important to the coding, and areas where the written protocol may need to be elaborated further. These comments are then reviewed by the data coder manager and the researcher. Discussions at this point should address how well the protocol performed in answering the research question, whether the data have been coded as intended, and whether this coding provides the information required. If changes are significant, a second round of testing is recommended. Once the protocol is satisfactory, it enters the second phase, coder training. However, when the protocol leaves this stage, there may still be changes made to it later in the process. Depending on the complexity of the protocol (and the time required to reduce each event), phase 1 may require from 1 or 2 days to 1 week or more to complete. As with any research, unexpected scenarios, driver behaviors, or environmental conditions often arise during data coding and/or protocol development, and this sometimes results in a need to edit, append, or further clarify the working data dictionary. For instance, if a road type, secondary task, or conflict type is observed in the video that does not clearly fit into the categories provided by the dictionary, then the data reduction manager, in conjunction

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Revised protocol Researcher and DC manager

Data coding QA/QC workflow

Phase 1: Protocol development

79

Draft protocol Based on research queslions & review of data Researcher and DC manager

Protocol revisions Upon review of notes & prellm data. Researcher & DC manager

Test protocol View variety of events, note questions, Sr.data center

No Protocol satisfactory?

Yes Tested protocol

Phase 2: Data Coder training

Train/retrain reductionists Complete x hours or x events, then stop for QA. Sr. data coder & coder

Develop initial test Develop first inter-rater test w/ representative events. Sr.data coder

Train addltional coders. (if necessary) Sr. data coder

Initial QA 100% review of initial work. Sr. data coder

Yes Take initial test Coder

Initial coders satisfactory?

Yes

Initial test satisfactory?

No

Roles

Yes

Researcher—Takes lead in protocol development, provides feedback for questions throughout process.

Begin data coding Sr. data coder & data coders Phase 3: Data coding

Data coding manager—Oversees all QA/QC steps, provides support to Sr. data coders, updates Researcher.

QA inspections (ongoing) (% checked is TBD) Third reviewer makes call on all disagreements, Sr. data coder, data coders Feedback to Coders Sr. data coder Periodic tests Additional Inter-rater tests at pre-determined intervals Sr. data coder, data coders

Phase 4: Post-coding

Complete QA inspections Sr. data coder, DC manager

No

Data verification Create matrix to check internal consistency; resolve known lssues. DR manager, Sr. data coder, coders

FIGURE 6.3 QA/QC data coding workflow

Sr. data code—Monitors. progression through workflow, conducts training, tests protocols, creates tests, & monitors workflow to data coders. Data code—Completes data coding, takes required tests, assists with spot checking. Limited to 4-5 hours per day per person.

Final data review Review data in Excel DC manager, Sr. data coder, data coders

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with the researchers, must decide and thoroughly document how to code that scenario for immediate and future reference. When this occurs, it is imperative that a formal process for updating the data dictionary and/or data reduction manual be followed: 1. Assess the situation. Can the situation be described adequately by existing operational definitions given the research questions? (If no, continue to step 2.) 2. Consider the frequency. How frequently will this situation arise? How critical is this situation to the outcome of the event? Does it warrant a new category and definition, or can existing operational definitions be modified to include this new situation without compromising the research question? 3. Modify the dictionary. If existing definitions do not adequately describe the scenario, and the scenario is deemed to be a critical aspect of the event, then a new option and operational definition must be added to the variable(s) used to describe those aspects of the event. If the research question does not require a unique description for the scenario, then, at minimum, the data dictionary needs to be modified so that an existing operational definition comprises the scenario in question. 4. Publish and distribute the updates. Whenever the data dictionary is modified, all reductionists and researchers working with that data set must be notified of the update as soon as possible. The most up-to-date data dictionary should be used at all times, and reductionists need to know that they are working with the most current definitions. Active notifications and reductionist compliance is strongly recommended. VTTI requires all reductionists to read and sign update documents after an opportunity to ask questions and clarify any uncertainties. 4.3.1.2. Phase 2: Coder Training The tested and revised protocol enters the coder training phase. On the right side of the phase 2 loop, the senior data coder and the data coding manager work together to train the first cohort of data coders, ideally no more than three or four trainees at a time to keep the initial quality control manageable. The protocol is reviewed in detail with the coders, and both paper and electronic copies are made available for reference. After the formal training session has been completed, coders begin coding under the supervision of a senior coder. This initial coding should be short term (no more than one full reduction shift) and then stopped for an accuracy assessment. Ideally, 100% of each coder’s work will be reviewed by a senior coder or data coder manager. Corrections are made, and detailed feedback is provided to coders to review. These comments are reviewed with the coders, and coders are retrained if necessary.

PART | I

Theories, Concepts, and Methods

If coders are able to meet reliability standards in this initial review (e.g., 90% accuracy, although this level may vary with the complexity and level of subjectivity present in the reduction), then a random sample (e.g., 10e20%) of all remaining work should be checked (see phase 3). If the initial review was unsatisfactory, then another day of work is completed after retraining, and the 100% review is repeated. At this point, there may be times when all trainees are struggling with a certain aspect or variable in the coding. This is usually a sign that either more in-depth training on that variable needs to be conducted or the protocol needs to be modified or clarified to increase reliability. On the left side of the phase 2 loop, the senior coder develops the first inter-rater test by selecting a sample of events that represents the range and frequency of conditions expected to be present in the data set. Depending on the complexity and length of the protocol, the test may include 10e30 events. After meeting the initial reliability standards during the 100% review, coders should then take this test. By having all coders complete the same set of events, the ability to consistently code variables within the group can be measured. If scores on this test are satisfactory (e.g., 90% or greater), then coders may move to phase 3. If scores are unsatisfactory, retraining or additional protocol revisions may be necessary. Finally, once the first cohort of three or four coders completes the training loop and moves to phase 3, additional coders can enter the training loop in similar groups of three or four each. Depending on the complexity of the protocol and the experience of the coders, the training period may require approximately 2e5 days. 4.3.1.3. Phase 3: Data Coding Three tools for ongoing quality control are used during the data coding phase: spot checks, inter-rater tests, and intrarater tests. A “spot check” is simply a second-person review of data coding work in which corrections can be made and comments provided to the original coder to confirm and/or improve accuracy. Spot checks are a very useful tool for assessing coders’ understanding of protocols for a variety of potential circumstances, revealing potential ambiguities that may need to be addressed in the protocol or data dictionary, and monitoring of overall data quality. Spot checks can be performed by the data coding manager, senior data coder, or experienced data coders (usually a combination of all three). However, discrepancies between the original reduction and the spot check should generally be reviewed by a manager (data coding manager or senior coder) as a third reviewer before changes are made. Feedback on all spot checks should be provided to the original coder immediately, and coders then should review all comments and be encouraged to revisit the

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Naturalistic Driving Studies and Data Coding and Analysis Techniques

events and ask questions so that mistakes can be avoided in the future. If consistent or increasing errors are found during later spot checks, a larger sample of coding work for that individual (or a time-specific or variable-specific complete sample if the error type is consistent and easily contained) should be checked and corrected until the issue is resolved. Spot checks should begin very soon after entering phase 3. Generally, each person involved in spot checking will spend a certain amount of time each day/week performing spot checks (e.g., 1 h per day or more if necessary to complete the desired sample) and the rest of his or her time completing additional coding. Spot checks should be performed either blindly so that the reviewer does not know who completed the original coding (in which case the reviewer may also review his or her own work) or openly so that each original coder can receive individualized feedback and errors can be easily contained and systematically corrected (in which case the reviewer should only review the work of others). Inter-rater and intra-rater tests are used periodically during a coding project to ensure consistency both between coders at a given point in time (inter-rater) and within individual coders over time (intra-rater). Depending on the duration and complexity of the coding, these may be conducted once a week, once a month, or once every couple of months. Similar to the initial inter-rater test conducted in phase 2, this test is developed by the senior coder to include a sample of events that represents the range and frequency of conditions present in the data set for a total of 10e30 events. For the inter-rater test, these should be different events than those that were included in the original test, with the goal of similar responses from all coders. The intra-rater test can be conducted simultaneously if some of the events from the first test are duplicated in each periodic test; the goal in this test is for each coder to record these events in the same way he or she coded them during the first test. If scores on this test are unsatisfactory, some retraining may be necessary. Spot checks and periodic inter- and intrarater tests should be continued until all events have been coded. Phase 4 begins at this point. 4.3.1.4. Phase 4: Data Delivery In phase 4, the data coding team works to prepare the data set for delivery back to the researcher so that statistical analysis can begin. First, any remaining spot checks are completed, and any remaining discrepancies between original coder and reviewer are resolved. Then, based on the spot check review and pragmatic review of the protocol, all known errors or potential inconsistencies are reviewed. This is the data verification step. Examples of these issues include a known confusion between the coding of a particular location as interstate or open country in the Locality

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variable or a potential inconsistency between two variables that should be internally consistent or coded similarly (e.g., if the subject is using a cell phone during an event, it should be marked as “cell phone” in the Distractions variable and “distracted or inattentive” in the Driver Behavior variable). With all of the QC measures taken during the first three phases, this step should be minimal, but it is still critical. The data coding manager and senior coders should identify these issues and then work with the coders to resolve them. Data verification should be completed until the data are internally consistent with the data dictionary. As a final review before data delivery, pulling all the data out into a spreadsheet and checking the relationship between events and variables is a good QA step. Any questionable events should be flagged for a final review. These events may be ones that are coded with either unexpected or rarely used categories that the data coding manger would like verified, or they may be clear random errors such as a subject ID recorded that was never in that vehicle. The amount of time required for data verification depends on the complexity of the protocol (as with phases 1 and 2), in addition to the number of events included in the coding protocol and how consistently the QA/QC workflow was followed during the first three phases. Thus, data verification can take from 1 or 2 days to several weeks.

4.4. Data Analysis Naturalistic driving studies have some characteristics that are similar to traditional empirical driving research, in which a driver’s precise position, speed, acceleration, etc. are known at every moment in time. Naturalistic driving studies also have some characteristics that are identical to traditional epidemiological research, in which the data set includes millions of vehicle miles traveled, thousands of events, and tens or hundreds of participants with no experimental control (i.e., did not control when or what type of traffic conditions drivers experienced at any given moment in time). These qualities or characteristics of naturalistic driving studies present a unique data analysis environment for transportation researchers to explore and understand different aspects of driver behavior never before examined. The driver behavior in the seconds leading up to a crash or near-crash includes the precise vehicle kinematic data but can also include observed driver behavior such as dialing a cell phone or drowsiness. The appropriate data analysis technique may be very different, depending on the research question and the type of data used. The following sections highlight a variety of different analytic techniques that have been used to answer a variety of research questions. However, this discussion is not meant to be exhaustive but, rather, to whet the appetite of the safety researcher who is planning to use naturalistic driving data to answer a specific research question.

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PART | I

4.4.1. Assessing Crash Risk Relative risk or crash risk refers to the amount of risk a driver assumes (above the normal level of risk of driving/ riding in a motor vehicle) when he or she engages in a particular behavior. Crash risk is typically calculated using an odds ratio. The odds is a commonly used measure of the likelihood of an event occurrence. The odds measures the frequency of event occurrence (i.e., the presence of inattention type) to the frequency of event non-occurrence (i.e., the absence of inattention type). That is, the odds of an event occurrence is defined as the probability of event occurrence divided by the probability of non-occurrence. The ratio of the odds is a commonly employed measure of association between the presence of cases (crash and near-crash events) and the controls (baseline driving epochs). Odds ratios are used as an approximation of relative crash risk in caseecontrol designs. This approximation is necessary due to the separate sampling employed for the events and baselines and is valid for evaluations of rare events (Greenberg, Daniels, Flanders, Eley, & Boring, 2001). The odds ratio would be defined as n11 p1=ð1 p Þ n11 n22 n 1 ¼ n12 ¼ q ¼ 21 n12 n21 p2=ð1 p Þ 2 n22

(3.1)

and is a comparison of the odds of success in row 1 versus the odds of success in row 2 of the table. Algebraically, this equation can be rewritten with crude odds ratios calculated as shown in Eq. (3.2): Odds ratio ¼ ðA DÞ=ðB CÞ

(3.2)

where A is the number of crashes/near-crashes where was present without any other type of inattention; B is the number of baseline epochs where was present without any other type of inattention; C is the number of crashes/near-crashes where was not present or was present but in combination with other types of inattention; and D is the number of baseline epochs where was not present or was present but in combination with other types of inattention. To interpret an odds ratio, a value of 1.0 indicates no significant danger above normal driving. An odds ratio less than 1.0 indicates that an activity is safer than normal driving or creates a protective effect. An odds ratio greater than 1.0 indicates that an activity increases one’s relative risk by the value of the odds ratio. For example, if reading while driving obtained an odds ratio of 3.0, then this indicates that a driver is three times more likely (or 300% more likely) to be involved in a crash or near-crash while reading and driving than if just driving the vehicle.

Theories, Concepts, and Methods

Results from our previous naturalistic studies suggested that tasks that take the driver’s eyes off the forward roadway, such as reaching for a moving object, applying makeup, dialing a cell phone, text messaging on a cell phone, and reading, significantly increase crash risk (Hickman, Hanowski, & Bocanegra, 2010; Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). Those activities that did not take the driver’s eyes off the forward roadway for extended periods of time showed no increase in crash risk. These activities included talking to passengers (for adult drivers), adjusting radio/HVAC, talking on a CB, talking on a cell phone, and drinking.

4.4.2. Prevalence or Driving Exposure Naturalistic driving studies can provide precise driving exposure information for the sample of drivers involved in the study. Although crash risk is an important assessment, it must be weighed in comparison to the prevalence in which drivers engage in the “risky” behavior. For example, dialing a cell phone is considered high risk; however, the time required to complete this task is relatively short compared to that for text messaging. Thus, safety professionals who observe the increased frequency of text messaging while driving combined with the observed increased crash risk find that this behavior is quite worrisome compared to eating or drinking while driving. In the field of transportation safety, exposure measures are typically limited to drivers’ self-reported vehicle miles traveled, the number of licensed motorists, or highway vehicle counts for a specific location. Although naturalistic driving studies typically recruit fewer participants than do survey- or questionnaire-based studies, the exposure information is much more precise than self-report. This precision encompasses exposure to specific risk factors (i.e., driving at night) that can be more precisely measured using naturalistic driving data than in a self-report or highway vehicle count. One of the key findings of the original Dingus et al. (2006) 100-Car Naturalistic Driving Study was the much higher frequency of crashes compared to police-reported crashes, from which most of the traffic safety analyses were previously conducted. Drivers were involved in crashes with their vehicles four times more frequently than they reported to police. This is a rich source of data and rich source of exposure that was completely unavailable prior to the 100-Car Study.

4.4.3. Contributing Factors for Crashes and Near-Crashes The Indiana Tri-Level Study (Treat, 1980) and the Large Truck Crash Causation Study (Starnes, 2006) both reviewed hundreds of police accident reports as well as

Chapter | 6

Naturalistic Driving Studies and Data Coding and Analysis Techniques

a sample of on-site crash investigation reports to provide a comprehensive list of all the causal factors present in actual crashes. These studies were conducted post hoc on actual police-reported crashes. A great deal was learned from these studies; however, these types of analyses can also be conducted using naturalistic driving studies to obtain additional knowledge and insight. The data coding for naturalistic driving studies can be easily developed to produce a list of potential causal factors. The video provides greater precision concerning the driver behaviors in the seconds leading up to crashes as well as the environmental, surrounding traffic, and general roadway conditions. Several analyses have been conducted using 100-Car Study data for this purpose (Dingus et al., 2006). One example of a tree diagram shows the breadth of causal factors for a conflict with a lead vehicle (Figure 6.4). Results from the 100-Car Study indicate that there are typically multiple causal factors for crashes and nearcrashes and only one or perhaps two causal factors for most critical incidents. Comparisons have also been performed between crashes and near-crashes that indicate that the most dramatic difference between crashes and near-crashes

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does not reside in the type of causal factors. Rather, it is whether an evasive maneuver was initiated or not, where no evasive maneuver is initiated for crashes and an abrupt evasive maneuver is initiated for near-crashes. These results support the use of near-crashes as a safety surrogate (Guo, Klauer, Hankey, & Dingus, 2010).

4.4.4. Advanced Product Testing Due to the nature of drivers being studied/observed in their normal environment, the naturalistic driving study presents a unique opportunity for both auto manufacturers and nomadic device manufacturers to study product use. Specifically, naturalistic driving studies provide manufacturers with an in-depth view of how drivers actually use and interact with their products under normal, daily driving scenarios. Although the recruitment for these types of studies may originate with the original equipment manufacturers providing a list of potential participants who own the desired system, the data can be very valuable for future system design and also for an understanding of the potential unintended consequences or driver misuse of specific devices.

Crash: Conflict with lead vehicle (15)

Rear-endstrike (14) 93.3% Incident type: Crash (15)

Pre-event maneuver

Precipitating factor (15) 100%

Associated vehicle/ roadway states

Contributing factors

Road departure (left/right) (1) 6.7%

Avoidance maneuver

Post avoidance maneuver (15) 100%

AM

PM Driver factors

DF

Driving environment

DE

Control maintained (13) 86.7%

Other vehicle stopped on roadway more than 2 seconds (7) 46.7%

Infrastructure / driving environment factors

IF

Infrastructure

I

Skidded laterally and rotated in unknown direction (1) 6.7%

Other vehicle slowed and stopped less than 2 seconds (7) 46.7%

Vehicle factors

VF

Other vehicle lane change – left other (1) 6.7%

Skidded longitudinally (1) 6.7%

t FIGURE 6.4 Tree diagram of the causal factors for lead vehicle conflict crashes in the 100-Car Naturalistic Driving Study

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PART | I

Naturalistic driving studies can be designed to assess various products, but post hoc analyses can also be performed using data already collected. For example, McLaughlin, Hankey, Dingus, and Klauer (2009) performed a study that examined different forward collision warning device algorithms using Dingus et al.’s (2006) 100-Car data. Different algorithms were compared to actual driving data to assess the number of potential crashes and near-crashes that may have been avoided if drivers had been given an alert that they were about to collide with an obstacle. Many naturalistic driving studies have been conducted to assess the feasibility of various safety warning devices. These studies are typically referred to as field operational tests or (FOTs). Some of the more recent FOTs that have been performed in the United States include the Run Departure Crash Warning System FOT (LeBlanc et al., 2006), the Drowsy Driver Warning System FOT (Blanco et al., 2009), and the Integrated, Vehicle-Based Safety System Heavy-Truck Field Operational Test (Sayer et al., 2010), which evaluated combined warning system alerts to drivers.

5. CONCLUSIONS Naturalistic driving data provide powerful tools for safety researchers that incorporate some characteristics of epidemiological data analysis techniques with empirical data analysis techniques. Although these characteristics are very beneficial, they also provide novel new data and analytic methods in which to explore and study driver safety, specifically driver behavior. The life cycle of naturalistic driving studies includes the following: 1. 2. 3. 4.

Study design and data collection Data preparation and storage Data coding Data analysis

Each of these steps is complex primarily due to the size and extent of the data being collected. As stated previously, naturalistic driving studies typically collect 6e8 gigabytes of video per minute, which can easily result in thousands of hours of video collected, and 6e10 TB of data that must be prepared, stored, coded, and analyzed. Naturalistic driving studies are typically lengthy and resource-intensive but worth the rich, detailed data that can be collected. These types of studies are complex and require extensive planning both prior to data collection and through the entire life cycle of the study to ensure that the initial research objectives are appropriately evaluated. Detailed planning at every step in the life cycle will result in a much easier and efficient data analysis phase of the project. Inefficient and/or minimal planning can easily result in a failed project that cannot evaluate the original research objectives.

Theories, Concepts, and Methods

Results from previous naturalistic driving studies have quantified the inherent dangers in driving drowsy and driving while engaging in text messaging, cell phone dialing, applying makeup, and any other task that requires more than 2 s total time of eyes off the forward roadway. Future studies may provide answers to even more complex issues regarding driver age, geographic location, and vehicle type. The Strategic Highway Research Program 2 (SHRP) Naturalistic Driving Study will be a national resource for traffic safety professionals, with preliminary data sets available as early as 2012. Naturalistic driving study databases from past and future studies will be available to the safety research community. For example, Dingus et al.’s (2006) 100-Car Naturalistic Driving Study data are already available online at http://www.vtti.vt.edu. Given this accessibility, this chapter focused primarily on the data reduction and analysis of naturalistic data because these steps will be critical to researchers who want to use these data sets for safety research. Although the data reduction and analysis task is critical, researchers also need to have a clear understanding of how the DAS worked and the limitations of the data collection system. All phases of the naturalistic data study life cycle are important to understand in order to effectively and accurately analyze the data. The SHRP 2 Naturalistic Driving Data, as well as the European and Canadian naturalistic driving studies that are being planned, will provide extensive driver behavior databases. The power of naturalistic driving studies and the more in-depth analyses of driver behavior is an important step toward achieving greater reductions in driver injuries and fatalities on our roadways. The safety research community must become adept and develop improved analytic techniques to use with naturalistic driving data. With this additional tool in the safety researcher tool box, there is hope of making great strides toward zero deaths on our roadways.

REFERENCES Blanco, M., Bocanegra, J. L., Morgan, J. F., Fitch, G. M., Medina, A., Olson, R. L., Hanowski, R. J., Daily, B., & Zimmerman, R. P. (2009). Assessment of a drowsy driver warning system for heavy-vehicle drivers: Final report. (Report No: DOT 811 117). Washington, DC: National Highway Traffic Administration. Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S. E., Sudweeks, J., Perez, M. A., Hankey, J., Ramsey, D., Gupta, S., Bucher, C., Doerzaph, Z. R., Jermeland, J., & Knipling, R. R. (2006). The 100-Car Naturalistic Driving Study: Phase IIdResults of the 100-Car Field Experiment. (Interim Project Report for DTNH22-00C-07007, Task Order 6; Report No. DOT HS 810 593). Washington, DC: National Highway Traffic Safety Administration. Dingus, T. A., Neale, V. L., Garness, S. A., Hanowski, R. J., Kiesler, A. S., Lee, S. E., Perez, M. A., Robinson, G. S., Belz, S. M., Casali, J. G., Pace-Schott, E. F., Stickgold, R. A., & Hobson, J. A. (2001). Impact of sleeper berth usage on driver fatigue. (Technical Contract Report

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No. DTFH61-96-00068). Washington, DC: Federal Motor Carrier Safety Administration. Greenberg, R. S., Daniels, S. R., Flanders, W. D., Eley, J. W., & Boring, J. R. (2001). Medical epidemiology (3rd ed.). New York: McGraw-Hill. Guo, F., Klauer, S. G., Hankey, J. M., & Dingus, T. A. (2010). Nearcrashes as crash surrogate for naturalistic driving studies. Transportation Research Record: Journal of the Transportation Research Board, 2147, 66e74. Hanowski, R. L., Wierwille, W. W., Garness, S. A., & Dingus, T. A. (2000). Impact of local/short haul operations on driver fatigue. (Technical Contract Report No. DTFH61-96-C-00105). Washington, DC: Federal Motor Carrier Safety Administration. Heinrich, H. W., Petersen, D., & Roos, N. (1980). Industrial accident prevention. New York: McGraw-Hill. Hickman, J. S., Hanowski, R. J., & Bocanegra, J. (2010). Distraction in commercial trucks and buses: Assessing prevalence and risk in conjunction with crashes and near-crashes. (Technical Contract Report No. FMCSA-RRR-10-049). Washington, DC: Federal Motor Carriers Safety Administration. Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact on driver inattention on near crash/ crash risk: An analysis using the 100 Car Naturalistic Driving Study data. (Report No. DOT HS 810 594). Washington, DC: National Highway Traffic Safety Administration. LeBlanc, D., Sayer, J., Winkler, C., Ervin, R., Bogard, S., Devonshire, J., Hagen, M., Bareket, Z., Goodsel, R., & Gordon, R. (2006). Road departure crash warning system field operational test methodology and results. (Report No. UMTRI-2006-9-1, Contract No. DTFH6101-X-0053). Washington, DC: National Highway Traffic Safety Administration.

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Lum, H., & Reagan, J. A. (1995). Interactive highway safety design model: Accident predictive module. Public Roads Magazine, 55(2). McLaughlin, S. B., Hankey, J. M., Dingus, T. A., & Klauer, S. G. (2009). Development of an FCW algorithm evaluation methodology with evaluation of three alert algorithms: 100-Car follow-on subtask 5. Washington, DC: National Highway Traffic Safety Administration. Mollenhauer, M. (1998). Proactive driving safety evaluation: An evaluation of an automated traveler information system and investigation of hazard analysis data. Iowa City: Unpublished doctoral dissertation, University of Iowa. Parker, M. R., & Zegeer, C. V. (1989). Traffic conflict techniques for safety and operations: Observers manual. (Technical Contract Report No. FHWA-IP-88-027). Washington, DC: Federal Highway Administration. Sayer, J. R., Bogard, S. E., Funkhouser, D., LeBlanc, D. J., Bao, S., Blankespoor, A. D., Buonorosa, M. L., & Winkler, C. B. (2010). Integrated vehicle-based safety systems: Heavy-truck field operational test key findings report. (Report No: DOT HS 811 362). Washington, DC: National Highway Traffic Safety Administration. Starnes, M. (2006). Large-truck crash causation study: An initial overview. (Report No: DOT HS 810 646). Washington, DC: National Highway Traffic Safety Administration. Treat, J. D. (1980). A study of precrash factors involved in traffic accidents. HSRI Research Review, 10(6), 2e35. Wierwille, W. W., Hanowski, R. J., Hankey, J. M., Kieliszewski, C. A., Lee, S. C., Medina, A., Keisler, A. S., & Dingus, T. A. (2002). Identification of driver errors: Overview and recommendations. (Technical Contract Report No. FHWA-RD-02-003). Washington, DC: Federal Highway Administration.

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Chapter 7

Driving Simulators as Research Tools in Traffic Psychology Oliver Carsten and A. Hamish Jamson University of Leeds, Leeds, UK

1. INTRODUCTION Driving simulators are now a major tool, arguably the major tool, for research on driver performance and behavior. Using two major journalsdTransportation Research Part F and Human Factorsdas the benchmark, it can be seen that studies based on simulator research constitute a major proportion of the published papers in the driving domain. In 2009, 32% (11 of 34) of the papers published on driving in Transportation Research Part F were based on experimental studies conducted in driving simulators, and those papers constitute a far higher proportion of the overall experimental work that was published since a large proportion of the other papers were based on questionnaire studies. In the same year, of 6 papers published in Human Factors in the area of “surface transportation,” 5 (83%) were drawn from simulator experiments. This preeminence of the driving simulator for research on driving is relatively new, and hand-in-hand with the growth of simulator studies has been the growth of the experimental approach for studying driving. The tool (simulators) and the method (experiments) are inextricably linked. The simulator is used for the investigation of experimental manipulations; comparison of the efficacy of treatments; what-if scenarios related to new systems and technologies; and the investigation of a variety of impairments, including alcohol, drugs, fatigue, and distraction. This preeminence of the laboratory over the real world is rather surprising. With millions of drivers and millions of vehicles in the real world, one might well ask why use a driving simulator. Surely, in order to carry out research on driver behavior, road safety, road infrastructure design, the impact of new technologies, driver impairment, and so on, all we need to do is to collect and analyze real-world data. And yet, the number of driving simulators in universities and research establishments is constantly growing, and year-on-year considerable effort is invested

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10007-4 Copyright Ó 2011 Elsevier Inc. All rights reserved.

in enhancing their capabilities. Indeed, the driving domain is quite unique among transport modes in the focus of its simulators on research as opposed to training. In aviation, maritime transport, and rail, where the vehicles are very costly in relation to the capital cost of a simulator, simulators are mainly used for operator training. In the driving domain, training simulators make only a small contribution compared with in-vehicle training and practice. But in the driving domain, the number of research simulators and the elaborateness of their specification continue to increase.

2. WHAT IS A DRIVING SIMULATOR? This may appear to be a question with an obvious answer, but it is not really possible to give a precise definition. Since simulators can vary from simple facsimiles of driving using a joystick control with a simplified road environment displayed on a PC screen to multi-million-dollar laboratories providing full-size vehicles mounted on motion systems with up to 9 degrees of freedom and a field of view of up to 360 , there is no straightforward answer to the question. A simulator has a set of physical features that usually include the following: l

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One or more screens to display the scene: The image may be displayed on computer monitors or may be projected onto a flat or curved surface. Vehicle controls: The minimum is mouse or joystick control, but more common is a version of normal vehicle controls, either in the form of a steering wheel, pedals, and gearshift from a real car or in the form of a controller made for computer driving games by such companies as Logitech and Microsoft. A sound system to deliver road and vehicle noise. A dashboard: This may be a virtual dashboard, displayed on a monitor or by projection, or a dashboard from a real car. 87

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These minimal physical features can be enhanced in numerous waysdfor example, with multichannel projection, partial or complete vehicle cabins, working auxiliary controls (e.g., indicators), a vibration table, and a motion system to provide a facsimile of the acceleration forces felt in real-world driving. Currently, simulator images are generated by real-time animation. But even before real-time graphics became feasible, a driving simulator could be built around miniature vehicles driving on rolling road, usually with a motorway layout. The windscreen view was typically generated with a small camera positioned above the rolling road. The accelerator and brake controlled the speed at which the roadway was rolled, and driving was restricted to a single roadway, with the only permitted lateral movement being a lane change. A simulator of this type was used by Mortimer (1963) to study the effect of alcohol impairment and headlight glare, and a rolling road simulator was in operation at the Transport and Road Research Laboratory in the United Kingdom until the 1990s (Irving & Jones, 1992). Other early driving simulators used images from filmed scenes, whereas the use of computer-generated graphics displays became feasible in the 1970s (Wierwille & Fung, 1975). The standard approach for road scene display in modern simulators is to apply real-time graphic image generation. This gives almost total flexibility in terms of the scenes and situations that can be displayed, within the limits of the projection system that is used. It has to be recognized that projectors and monitors have some natural limitations, such as resolution and limited luminous intensity. This latter limitation means that it is not possible to directly create the true optical effect of glare with a projector or a monitor or display, although the halo effect of nighttime glare can be mimicked by means of animation. However, image generation alone does not define what constitutes a driving simulator. Simulators are commonly classified into the categories of high-level, mid-level, and low-level (Kaptein, Theeuwes, & van der Horst, 1996; Slob, 2008; Weir & Clarke, 1995). In this classification, simulators incorporating motion systems and full vehicle cabs are in the highlevel category, static simulators based around projection systems and full cars are in the mid-level, and those built around simple components such as game controllers and computer monitors are in the low-level category. Of course, this classification is quite arbitrary. It is possible to think about simulators that combine some low-cost and relatively low-fidelity components with other high-end features. A laboratory simulator with games-based controls can have an elaborate projection system and even (in theory) a motion platform. Similarly, there are systems with extremely sophisticated motion that have quite limited visualization capabilities. It therefore makes sense to

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Theories, Concepts, and Methods

consider simulator cost on a single scale, but not simulator capability. A simulator may be highly capable in terms of one subsystem but only moderately capable in terms of another. Also, not surprisingly in view of the major effort required, no studies have been run in which every aspect of a simulatordfrom field of view to graphics resolution, motion system design and tuning, sound system performance, and software and mechanical delaydwas systematically varied in order to assess the effect on participant performance.

3. WHY USE A DRIVING SIMULATOR? There are a large number of motivations for using simulators. Perhaps the greatest incentive is the ability to control the experience of the participants and to create repeatable situations, scenes, and scenarios. This control creates a degree of efficiency in experiments that cannot be matched by conducting observations in the real world. In tens of minutes on a simulator, it is possible to accomplish a study that might take months of real-world driving. The full controldover participant selection, instructions to the participants, ordering of conditions, and event triggeringdis virtually impossible to equal in real-world studies. Also, because of this efficiency and effectiveness, the cost of conducting a simulator study tends to be far lower than that of a counterpart study in real-world conditions. The control element reduces random extraneous effects in the data so that for a given number of participants, experimental power is greater. Studies that would be very difficult or impossible to conduct in the real world are feasible in simulators. New vehicle technologiesddriver assistance systems, vehicle handling systems, and even novel vehicle controls such as joystick control replacing pedals and steering wheeldcan be created through software and electronic interfaces. Simulators have played a major role in system development and system design: The usability, acceptance, and effectiveness of alternative system specifications and humanemachine interfaces can be systematically evaluated (Jamson, Lai, & Carsten, 2008). Impaired driving can be investigated without serious risk to participants. Studies of the impact on driving performance of distraction such as from mobile phones, of the effects of fatigue, of alcohol, of over-the-counter and prescription drugs, and even of illicit drugs such as marijuana have all been conducted on simulators. Thus, the meta-analysis of the impact of alcohol on driving carried out for the European DRUID project found a large number of studies that had carried out the investigation using a driving simulator (Schnabel, Hargutt, & Kru¨ger, 2010). The investigation of the impact of cannabis on driving is much more difficult to carry out for legal reasons, but even

Chapter | 7

Driving Simulators as Research Tools in Traffic Psychology

in this area, seven separate simulator studies had been performed prior to 1999 (Ward & Dye, 1999). Some types of investigation, by their very nature, almost have to be carried out on a simulator. Studies of the impact of illicit drug use fall into this category: It would be unethical and illegal to perform a real-world experiment of the impact of a substance whose use prior to driving is banned. Also, there could be ethical issues with an on-road study of sleep-deprived individuals. Similarly, research on the impact of cognitive impairment, such as caused by Alzheimer’s disease, can be and has been carried out on a simulator (Rizzo, McGehee, Dawson, & Anderson, 2001). Thus, one of the major motivations for using a simulator is that of health and safety: Experiments that would be difficult to carry out in the real world for ethical reasons can be performed in the risk-free environment of a simulator. Driver awareness of and response to risky situations, near crashes, and even real crashes (McGehee & Carsten, 2010) can be investigated in a simulator. Drivers can be subjected to levels of primary task demand and/or secondary task distraction that could not be investigated in real-world conditions or could only be investigated in the depleted and unnatural environment of a test track. In addition, driver ability to cope with vehicle or electronic system failure can be studied (Jamson, Whiffin, & Burchill, 2007). Researchers can be provided with a huge range of data from a simulator. Essentially, the “vehicle” can provide the full variety of data that would be provided by a real-world instrumented vehicledsteering data, pedal data, engine data, and so ondas well as data about how the vehicle relates to the road environment, such as precise position. From position, recorded for example at 60 Hz, it is possible to derive speed, acceleration, variation of lateral position, lane position, position relative to other objects and vehicles in the virtual world (and hence time headway, time to collision, etc.), as well as vehicle handling information (friction and lateral g). Driver head and eye movements can be monitored and recorded, as can a whole suite of physiological information, such as data from electroencephalograms and electrocardiograms. All these types of objective data can be supplemented with subjective data on workload, acceptance, trust, behavioral intention, and so on. This potential wealth of data necessarily brings with it the responsibility for the researcher to identify important research questions and hypotheses in advance and to ensure that participants are not overloaded with questionnaires and protocols. It is normal to carry out a data reduction process on the raw objective data to prepare them for analysis in a statistical package. However, the simulator raw data should be fully archived so that further data reduction or calculation can subsequently be carried out. Researchers also need to be aware that there are limitations to studies carried out on a simulator. One important

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issue relates to validity and concerns the motivation of participants in a simulator study. Participants will never be fully motivated in the way that they are in real-world driving. They are potentially aware (although they may be oblivious) of being observed, and they may not feel the same time pressure that they would when, for example, delayed by congestion in real traffic. Participants may be motivated to “obey” the experiment, resulting in compliance bias, or they may believe that in an artificial environment they are free from the normal constraints imposed by driving laws and norms. It is difficult to investigate the impact of these factors, so one must generalize and conclude that since simulator driving tends to reproduce the patterns of real-world drivingdso that, for example, young males drive in a more risky manner than other groupsdthe data obtained can be considered sufficiently reliable. It also has to be accepted that there are inherent limitations to the scope of simulator studies. One such limitation is a restriction on investigating learning effects using a within-participant experimental design. Even with repeat visits (and these are often quite difficult to arrange), it is not possible for an individual to accumulate extensive experience of a new road feature or a new electronic system from a few hours of driving in a simulator. Learning and adaptation effects can be studied. Thus, Jamson, Lai, Jamson, Horrobin, and Carsten (2008) examined the persistence of road engineering treatments for speed management by designing an experiment with repeated exposure to each treatment. But investigating the implications of a few repeats of exposure to a treatment is not equivalent to a study of long-term acclimatization to a treatment. For that purpose, driving simulators are ineffective except perhaps as an adjunct to real-world exposure.

4. TO MOVE OR NOT Perhaps the most hotly debated area of simulator design is that of motiondhow desirable it is and, if it is provided, what type of motion platform should be employed. Cost is a major consideration: Provision of motion substantially increases the cost of establishing a simulator, with a consequent effect on operating costs. It is difficult to argue against the desirability of motion, and various validation studies have shown that driving behavior becomes more life-like with the provision of motion feedback. Thus, Alm (1995) found that, with the motion system of the VTI simulator enabled, drivers were able to drive a more steady course on curves than when the motion system was disabled. Similar effects of a motion system on lateral control have been found in other studies. Greenberg, Artz, and Cathey (2003) used a hexapod with and without motion cueing. In one experiment, drivers were administered a number of secondary tasks. With handheld tasks (but not

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with other tasks), there was a substantial increase in lane violations when the motion system was disabled compared to the with-motion condition. Lack of motion also increased heading errordthat is, the angular difference of the driven vehicle from the instantaneous road. In a second experiment, drivers had to negotiate a course involving two lane changes laid out by cones. There were four levels of lateral motion scaling: 0 (i.e., no motion), 25, 50, and 70% of real-world acceleration. Heading error decreased with increased motion scaling, and, interestingly, the variability of yaw error also decreased with increasing scale factor, indicating that behavior was more consistent with greater motion capability. The authors concluded that drivers need to pay more attention to heading angle when motion cues are absent or reduced. This is perhaps not surprising because without motion, only visual feedback on vehicle path is available. They also concluded that there is a potential interference between this extra effort and the impact of distracting tasks on driving performance. As indicated previously, they found an interaction between secondary task type and simulator motion in impact of heading error. They stated, The implications of these interactions for the widespread and growing use of fixed-base simulators to measure distraction caused by secondary tasks are serious. A common rationale for using these simulators is that, while results may not be comparable to actual driving in an absolute sense, relative comparisons of performance metrics across secondary task type are still meaningful. The interactions presented in this paper imply that such relative comparisons are specific to the motion cueing environment provided by the simulator.

The most common motion platform for driving simulators is the so-called Stewart platform (it was actually invented by Eric Gough) or hexapod, which uses six struttype actuators linking a base platform and a simulator platform to provide motion in six degrees of freedomdthat is, x, y, and z plus roll, pitch, and yaw. The potential to present realistic longitudinal and lateral accelerations as experienced in real cars is quite limited with a hexapod, and it is even more limited with the now quite common minihexapods. The driver is tricked into feeling such accelerations by means of tilt. To provide at least some extent of the true accelerations, the more elaborate simulators, such as the U.S. NADS (National Advanced Driving Simulator), mount the hexapod on an xey table, which is able to surge both longitudinally and laterally (Figure 7.1). In some cases, a yaw table is also included. But no matter how large the motion system, it will not be feasible to provide, for example, the continued lateral accelerations that a driver would feel in real-world negotiation of a long curve. Accelerations are generally scaled down substantially from what would be felt in real driving. Even with an xey table, surge motion needs to be blended

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Theories, Concepts, and Methods

FIGURE 7.1 The motion system in the University of Leeds Driving Simulator.

at some point with tilt, and the motion system must be returned to its neutral point (known as “washout”). With a hexapod, almost all the accelerations are unreal, and too rapid onset of tilt can result in participants becoming aware of the unnatural motion. In addition, there are transport delays in motion systems. All of these factors mean that there is an art as well as a science to selecting the algorithm to be used by the motion system, and the evaluation of one algorithm over another tends to rely on subjective responses from drivers (Dagdelen, Reymond, Kemeny, Bordier, & Maızi, 2009). There is a substantial literature on the advantages of motion, the type of motion system to implement, and the choice of motion cueing strategy.

5. WHAT KIND OF SIMULATOR TO USE What level of simulator is required for a given study? The U.S. National Research Council committee of experts that was tasked with estimating demand for using the U.S. NADS (Transportation Research Board, 1995) was unable to produce a clear scientific justification for the large-scale motion system proposed for NADS: There may be a scientific justification for a large motion base to simulate crash-avoidance maneuvers in NHTSA-sponsored research. The need for a motion base in other applications, however, is less apparent and cannot be specified with confidence unless and until a simulator with a large motion base is built and tested. Even so, past assessments of potential uses of driving simulators have found that most research can be performed satisfactorily on simulators without the range of motion that NADS will provide. A large motion base probably would be useful in vehicle design applications, but, as noted, representatives of the automobile industry have indicated very limited interest in using the simulator. Nevertheless, this does not mean that others would not use it. NADS is intended to be the most advanced driving simulator in the world. If it functions as designed, there are likely

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Driving Simulators as Research Tools in Traffic Psychology

to be users willing to pay a premium for the additional realism that its motion base will provide. Certainly the most advanced simulators built for space and aviation have been used heavily, often for applications that the builders could not have imagined. (p. 5)

Of course, a justification can always be made for using the most elaborate tool for scientific research. But cost is also a major consideration. Funding agencies and sponsors have limited budgets, and very high costs will result in fewer studies being performed. Researchers should carefully consider whether using a low-cost simulator can be justified, and the case for doing so will depend on the focus of the study, the participant numbers required, the practicalities of using a high-end simulator, and so on. Nevertheless, over time, there is a consistent trend of technologies becoming cheaper in real terms, and the advent of small and relatively low-cost hexapods may mean that motion systems become more commonplace. In terms of image projection, once again, more tends to be better. There are clear arguments for providing a large field of view (360 horizontally is the ideal), rearview and side mirrors, high graphics resolution, as well as high contrast and brightness. But on a small monitor, very high resolution is pointless. Comparisons of lower cost with more elaborate simulators do not invalidate the cheaper alternative. Instead, they indicate that simulator quality is a continuum. Santos, Merat, Mouta, Brookhuis, and de Waard (2005) compared a “laboratory”dthat is, a very simple fixed-base simulator that used a 21-in. monitor and a low-cost games-style steering wheel and pedalsdwith a more elaborate fixedbase driving simulator in which the driver sat in a fullsized car and that used five-channel front projection on a curved screen to give a 230 horizontal field of view as well as a back view that enabled using the central review mirror. Both systems ran the identical software. The study investigated the impact of visual distraction from an invehicle information system (IVIS) on driving performance. The results indicated broadly similar effects in both the laboratory and the simulator. In both environments, a difference in lateral position variation and in lane exceedences was found between driving without and with the visual distraction. However, in the simulator, but not in the laboratory, differences in lateral position variation could also be observed between the levels of visual distraction. A parallel real-world (field) study found effects that were similar to but generally less powerful than those observed in the simulator. The authors concluded, A simple, low-cost laboratory simulator setup is able to provide a first-shot test-facility to the automotive industry for assessing the impact of an IVIS under design or development. For more detailed analyses of the nature and seriousness of the influence of IVIStype systems, a (medium cost) simulator is indicated, whereas

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some of the earlier established problems with field studies in an instrumented vehicle have been confirmed. (p. 145)

¨ stlund (2005) compared the Engstro¨m, Johansson, and O impact of both visual and cognitive task load in a fixed-base simulator, a moving-base simulator, and real-world driving in an instrumented vehicle. The road environment in each case was a motorway. The simulated and real roads had a similar layout. The fixed-base simulator used a full-size car and had a horizontal field of view of 135 to the front with no rear projection. The moving-base simulator had the front part of a full-size car and presented a horizontal field of view of 120 , again with no rear view. The motion system provided large linear motion via a track, as well as tilt and vibration. The study found generally consistent results between the two simulators. However, lateral variation was substantially greater in the fixed-base simulator. This is consistent with the results of Greenberg et al. (2003). What is not known is the extent to which greater effort is required to control the vehicle in a static simulator as opposed to a moving-base one. If there is a substantial increase in primary task workload because of the increased difficulty of steering in the absence of the vestibular cues, then some experimental results obtained in static simulatorsdparticularly on secondary task interference from, for example, mobile phone usedmight be partially invalidated. Certainly, drivers’ subjective rating of workload has been reported to be higher in a driving simulator than in real-world driving (De Waard & Brookhuis, 1997). How much this effect is caused by the extra effort of steering without vestibular feedback and to what extent this effect is mitigated with a motion system have not been investigated. However, it should be remembered that many factors affect simulator quality, not just the presence or absence of a motion system. Any driving simulator, however elaborate, has its limitations. Even the most capable motion systems scale down real-world accelerations, and even the largest motion systems are not able to sustain a longitudinal or lateral acceleration for very long before they run out of track. Also, the art of motion cueing in simulators is a significant field of study in its own right. Other notable simulator qualities, apart from motion, include the following: l

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Display capabilities: Field of view, pixel resolution, brightness, contrast, and capability for blending the images from more than one projector. Delay: Scene display lag and motion system lag following a driver input. Scene animation: The provision of textured (as opposed to flat-shaded) graphics, number of objects in the scene, and lighting algorithms. The physical models used to calculate vehicle dynamics: These can vary from a simple “bicycle” model of vehicle dynamics (Segel, 1956) to complex

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multibody models that simulate the linkages in a vehicle’s power train, steering, and suspension systems as well as the interaction between tires and road surface. The vehicle interface: Games controller, vehicle mockup or real vehicle, and the engineering of that interface (e.g., the provision of steering feel). Sound provision in terms of both hardware and software. The programming environment and its capability to deliver a wide variety of road layouts and traffic environments, the ability to create ambient traffic with appropriate behavior, and a capability to script scenarios to order.

It is clear that the quality of the software is as crucial as the quality of the physical environment. The capability of electronics hardware is now such that many of the low-cost simulators in use or being sold by simulator providers use the identical software as that used by their larger brethren. It is no longer the case that low cost means dumbed down.

6. HOW VALID ARE DRIVING SIMULATORS AS RESEARCH TOOLS? The issue of the validity or nonvalidity of driving simulators for the purposes of research on driving is a contentious one. In the aviation and maritime domains, simulators are used largely for training rather than for research. They have to meet minimum performance requirements, but in general the justification for their use is a combination of the huge cost of the airplane or vessel and the potential for simulators to provide training in the handling of specific scenarios, particularly hazardous or emergency events. In driving, simulators have been used mainly, but not exclusively (there are low-cost training simulators on the market), as platforms for research studies. There is a long history of such studies, dating back to the 1960s. But if simulators do not elicit normal or real-world behavior, then it can be argued that such studies lack validity and should properly be performed on real roads or on specialized test tracks. Certainly, the arguments of critics of driving simulators are both forceful and plausible. Leonard Evans, in his influential book Traffic Safety and the Driver (1991), drew a distinction between driver performance, which represents an individual’s capabilities and skills, and driver behavior, which refers to how an individual chooses to drive, given his or her skills. An example of performance is reaction time, whereas examples of behavior are speed choice and chosen time headway. Evans argued that driving simulators were appropriate tools for the investigation of performance but not of behavior: As driver performance focuses on capabilities and skills, it can be investigated by many methods, including laboratory tests,

Theories, Concepts, and Methods

simulator experiments, tests using instrumented vehicles and observations of actual traffic. As driver behavior indicates what a driver actually does, it cannot be investigated in laboratory, simulator, or instrumented vehicle studies. (p. 133)

Not content with this blanket prohibition of simulator studies for behavioral investigation, Evans also impugned the validity of simulators for the investigation of performance: The discussion . on reaction time showed the primacy of expectancy; even in real-world experiments, reaction times of participating subjects are substantially shorter than unalerted drivers. Thus, any estimate of reaction times using a simulator, no matter how realistic, would be suspect unless the subject drove for many hours to establish arousal and anxiety levels characteristic of normal driving, thus limiting data collection rates to a few per day. (p. 126)

He continued with some scornful remarks about the lack of progress in addressing research topics such as the impact of alcohol and fatigue, the design of road markings and signs, and reduced visibility on driving: “Can the lack of progress [over the previous 20 years] be traced specifically to insufficient realism in the simulator, thus justifying a more sophisticated simulator?” (p. 127). He commented that simulators lacked the ultimate element in eliciting realistic behavior, namely giving drivers the fear that crashing could result in real damage or injury. These opinions were repeated in Evans’ later book, Traffic Safety (2004): “It is exceedingly unlikely that a driver simulator [sic] can provide useful information on a driver’s tendency to speed, drive while intoxicated, run red lights, pay attention to nondriving distractions, or not fasten a safety belt” (p. 188). Perhaps we may concede on drink driving and belt wearing, but the other phenomena have all been investigated in simulator studies that produced meaningful results. These criticisms of simulator studies have been echoed by Olson, Hanowski, Hickman, and Bocanegra (2009) in their report on truck driver distraction as observed through naturalistic driving studies carried out at Virginia Tech Transportation Institute (VTTI): It is important to highlight that some results of the current study and other recent naturalistic driving studies . are at odds with results obtained from simulator studies . and future research should be conducted to explore the reasons why such study results often differ from studies conducted in actual driving conditions (i.e., the full context of the driving environment). It may be, as Sayer et al. (2007) note, that controlled investigations cannot account for driver choice behavior and risk perception as it actually occurs in real-world driving. If this assessment is accurate, the generalizability of simulator findings, at least in some cases, may be greatly limited outside of the simulated environment. (p. xxvi)

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Driving Simulators as Research Tools in Traffic Psychology

The press release from VTTI that accompanied the release of the report by Olson et al. (2009) went even further. Discussing “the disconnect between naturalistic and simulator research,” it stated, It is important to keep in mind that a driving simulator is not actual driving. Driving simulators engage participants in tracking tasks in a laboratory. As such, researchers that conduct simulator studies must be cautious when suggesting that conclusions based on simulator studies are applicable to actual driving. (VTTI, 2009, p. 2)

It is interesting to note that when faced with a disjuncture between the findings from simulator studies and those based on naturalistic driving, these researchers do not consider the possibility that the analysis techniques adopted for the real-world studies might be faulty. The particular finding that was most out of line with simulator-based evidence was the conclusion in Olson et al. (2009) that talking on a handheld mobile phone did not increase risk, whereas talking on a hands-free mobile phone actually reduced risk. The authors do not appear to have considered the possibility that there was a methodological flaw in their analysis, for example, regarding the identification of distracted and nondistracted episodes for comparison purposes. Nor do they discuss other real-world studies that point in the opposite direction. Simulator driving is by definition an attempt at convincing participants that they are engaged in an analogue of real-world driving. The success with which that is achieved will determine the validity of a given simulator. A common distinction (Blauw, 1982; Wang et al., 2010) is between physical validity and behavioral validity. Physical validity refers to the physical components and subsystems of a simulator, whereas behavioral validity refers to how close the experience of the participants and the driving elicited approximates that in a real vehicle on real roads. The two are not necessarily aligned: It is possible for a simple static simulator with a visual display on a single monitor and a gaming-style vehicle interface to produce driving that is close to real-world behavior, whereas a very elaborate simulator does not necessarily produce “real” behavior. But it is reasonable to suppose that a more elaborate environment will be more realistic and more immersive. Physical validity can be further broken down into various components: l

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The fidelity and elements of the sound systemdroad noise, engine noise, etc. The elaborateness of the physical vehicle controls and displays with which the driver interactsdmore capable simulators generally use a real vehicle cabdand the accuracy with which pedal feel, steering wheel feel, and gearshift feel (where this is provided) are conveyed. In a simulator with a motion base, there are the numbers of degrees of freedom (up to nine) provided, the scaling factor relative to real-world forces used for the direct motion cues (surge in the x axis, sway in the y axis, and heave in the z axis), the strategies used for tilt coordination (there is a quasi-standard here in the form of the classic motion drive algorithm as described by Nahon and Reid (1990)), and the inertia and mechanical delays imposed by the motion platform.

Behavioral validity is also not a single construct. One can refer to the basic levels of driving performance such as speed and lateral position, or one can consider more demanding tasks, such as the control of deceleration in approaching a stop line or the ability to carry out a smooth lane change or lateral positioning in fast negotiation of curves. In addition and in accordance with the previous discussion on whether simulators can provide accurate studies of the impact of driver distraction, one could examine task prioritization between the primary task of driving and potential distracters such as mobile phone use. Another distinction that has been made in the literature on simulator validity is between absolute and relative validity (Blauw, 1982; Kaptein et al., 1996). Blauw’s distinction between the two is as follows: All methods [of validation] give parameters describing validity by comparing conditions of driving in the simulator in relation to driving under the same road conditions. A modification of this approach is to compare performance differences between experimental conditions in the simulator with performance differences between similar conditions in the car. When these differences are of the same order and direction in both systems, then the simulator is defined to have relative validity. If, in addition, the numerical values are about equal in both systems, the simulator can be said to have absolute validity as well. (p. 474)

It would perhaps be more accurate to state that, in order to achieve relative validity, a simulator should not only produce the same ordering of effects as would occur in the real world but also not induce any spurious interactions between conditions, participant groups, and rank ordering of effects. One would not want one group of participants, such as young males, to be differently affected from another group, such as older females, in terms of the reproduction in a simulator of real-world orderings. What do the simulator validation studies that have been carried out generally indicate? First, not every type of

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simulator has been validated. Not surprisingly, validation studies have been concentrated on mid-level and top-end simulators. Also, for driving simulators, unlike training simulators for flight, there is no standard set of evaluation tests. In terms of the performance of the simple vehicle control task of speed maintenance and lateral control, the validation studies on mid-level simulators are not in full agreement with each other, perhaps because the simulator designs differ and because there is no proper control for the contribution of the various design elements across the studies. Kaptein et al. (1996) reported on a study of the TNO Human Factors simulator carried out in the early 1990s that examined the impact of road width and curve layout on speed. In the simulator and in driving on the real road, speed reduced with decreased road width and with sharper curves. However, in the simulator, speeds were generally higher, including on sharp curves. By contrast, Blana (2001) found in her study of the then similarly configured Leeds Driving Simulator that speeds on straights were generally higher than in real road traffic, but that speeds on sharp curves were in line with those observed on the real road. In terms of lateral position, correlations with real-road traffic was less good: Less curve straightening (corner cutting) was observed in the simulator (perhaps not surprising in a static simulator), and there was a smaller lateral shift away from opposing traffic in the simulated environment. Also, variation of lateral position was higher than for real-road traffic (Blana & Golias, 2002). Kaptein et al. (1996) examined the impact of research question on validity. For example, they found absolute validity in using a mid-level simulator for the study of driver route choice. They also concluded that the provision of a moving base substantially reduced variation in lateral position and could lead to absolute validity for that measure. Overall, they concluded, Tasks that depend on estimation of speeds and time duration may be affected by image resolution limitations. Yet, a number of experimental results in simulators with limited image resolution and without a moving base have been validated satisfactorily, indicating that such limitations are not important to all driving tasks. (p. 35)

Perhaps the most thorough behavioral validation of a single simulator has been carried out by Wang et al. (2010). They compared performance in a typical midrange simulator, the MIT AgeLab driving simulator, with performance data collected in an instrumented vehicle. The study data used were on secondary task load from three different input devices used for destination entry in a surrogate navigation system. They noted that relative validity becomes more complex in a situation in which, in the real world, no significant difference is found between

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some conditions (here, devices) for a particular performance indicator. They also proposed that relative validity is good when not only the rank ordering of effects is similar but also there is correspondence in the relative magnitude of the effectsdthat is, there is no interaction between experimental condition and the environment in which the data are collected (simulator or real-world driving) in terms of effect. Wang et al. (2010) used two groups of participantsdone for on-road driving in an instrumented vehicle and another in the simulator. They analyzed a wide range of dependent variables, examining response time to initiate the task, task completion time, glance frequency, total glance time, eyes on-the-road time, and maximum glance duration. They also examined a number of parameters of driving performancedmean speed, standard deviation of speed, and standard deviation of lane position. They found that the measures of task time and visual attention indicated both relative and absolute validity of the simulator. On the other hand, the driving performance measures were problematic because there was generally no differentiation in either environment between the devices tested in terms of these measures, although the standard deviation of speed measure did meet the criteria for both relative and absolute validity, They concluded, Fixed-based driving simulation is a safe method of assessing basic task performance and visual distraction for purposes of comparing manual user interface designs and provides valid estimates of these behaviors on-road for the type of in-vehicle interface interactions examined in this study. (p. 419)

7. PROBLEMS IN USING SIMULATORS: SIMULATOR SICKNESS One major problem encountered in simulator studies is that of simulator sickness. This is not just an issue with research driving simulators but also with simulators for other applications, such as the training of tank drivers by the military. Simulator sickness is a form of motion sickness caused by a mismatch between the visual perception of acceleration or deceleration and vestibular sensation of the same motion. Clearly, there is no vestibular feedback in static simulators, but even the most elaborate motion platforms employ trickery in the form of tilt to maintain the illusion of sustained acceleration, and in any case there will be transport and other delays in a motion system so that even “true” motion cues will not be totally accurate. The issue for research is whether simulator sickness is just an inconvenience for researchers and participants or whether it causes more profound problems. One issue is that not all types of participants are affected at an equal

Chapter | 7

Driving Simulators as Research Tools in Traffic Psychology

rate. In one of the experiments conducted for the HASTE European project on driver distraction, we attempted to conduct an experiment with a group of elderly drivers (older than 60 years) to study the impact of visual distraction on driving performance. The visual distraction was created by means of a task displayed on an LCD screen positioned close to the driver. However, the proportion of elderly participants who experienced simulator sickness was so large that we had to abandon using that group of participants. The consequence was that we were unable to investigate the impact of visual distraction on elderly drivers, although we were able to successfully perform an experiment on the impact of cognitive distraction using an auditory memory task. Simulator sickness is more than just an inconvenience. In a study carried out on a driving simulator with a small motion base, Bittner, Gore, and Hooey (1997) confirmed a significant interaction between age and display type in the prediction of sickness as indicated by a factor “faintness” calculated from participant comfort questionnaires. In a further step, the same study carried out an analysis of driving performance data with and without simulator sickness as a covariate. The dependent variable was reaction time in an emergency situation. Inclusion of faintness and vehicle speed as covariates resulted in a substantial increase in the number of independent factors that were significant (p < 0.05) and near significant (p > 0.055) in the analysis of variance. In other words, simulator sickness affected performance in the emergency task. The authors concluded, “It is strongly recommended that researchers explore and control the potential confounding effects of simulator sickness to assure meaningful performance assessments” (p. 1092). Thus, discomfort can both prevent studies from being completed and affect the results obtained in driving simulators. Anecdotally, it can be stated that the rate of simulator sickness is reduced with a motion system, and especially with a large-scale motion system, but as far as we are aware, this has not been investigated systematically.

8. EXPERIMENTAL DESIGN No particular experimental design can be considered as standard, although within-subject designs have major advantages in terms of experimental power. However, they can also have disadvantages, both in terms of the time required for participants to experience all the required conditions and because repeated-measures designs tend to induce familiarity with the scenarios included in the experiment and may therefore make surprise events nonviable. Similarly, counterbalancing of conditions can be considered as the norm because of the ability to control for learning effects. But the disadvantage is that counterbalancing makes

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it very difficult to investigate learning and ordering effects when these might be considered to be important. Thus, in experimental designs, as in other aspects of the setting up of simulator experiments, there is no right way and no wrong way. Experimenters should be guided by the research questions and hypotheses that they wish to address, and they should carefully weigh the advantages and disadvantages of alternative designs. Another experimental design issue relates to the amount of control over scenarios. There is a strong impetus to create scenarios that are equal in severity for all participants. Thus, it may be considered desirable to have a carfollowing scenario in which the lead vehicle is controlled in terms of a give time headway to the driven car. Then an event such as a sudden braking of the lead vehicle can be triggered such that all participants have to respond to an event of equal severity. However, participants cannot be forced to drive at a given speed (unless speed control is automated), and a given participant may find that the chosen time headway is too close for comfort. The participant will react by slowing down, the preceding vehicle will come closer, the participant will slow down more, and so on. This phenomenon of participants trying to “override” the scenario design has been observed in the University of Leeds Driving Simulator. It is also discussed by Donmez, Boyle, and Lee (2008), who carried out an analysis of such a scenario using the inverse of actual headway distance (rather than time headway, which was preset) at the time of accelerator release as a covariate. The finding from this analysis was that the experimental results changed depending on whether the covariate was taken into account: Without consideration of the covariate, distraction of various types appeared to improve reaction time, but once the covariate was considered, it was found that distraction resulted in longer reaction times.

9. CONCLUSIONS Simulators provide the opportunity to investigate driving under controlled conditions in a manner that is unparalleled by the alternatives. Real-world studies lack the equivalent control element, whereas test tracks offer a very depleted and inflexible driving environment. Simulator capability, particularly in terms of the graphics performance of PCbased systems, has grown very fast in recent years, and the advent of small-scale and relatively low-cost motion systems means that it may soon become standard for a midrange simulator to be equipped with six degrees of freedom of motion. The number of research simulators worldwide continues to increase, and simulator studies constitute an increasing proportion of the research literature on driving performance and behavior. Simulators may not be total replicates of the real world, and indeed they cannot be. But they offer the researcher of driver behavior an

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advantage that real-world studies cannot match: the ability to control experimental conditions and create prescripted scenarios.

REFERENCES Alm, H. (1995). Driving simulators as research tools: A validation study based on the VTI driving simulator. In GEM validation studies: Appendix. DRIVE II Project V2065 GEM. Bittner, A. C., Gore, B. F., & Hooey, B. L. (1997). Meaningful assessments of simulator performance and sickness: Can’t have one without the other. In Proceedings of the Human Factors and Ergonomics Society 41st annual meeting (pp. 1089e1093). Blana, E. (2001). The behavioral validation of driving simulators as research tools: A case study based on the Leeds Driving Simulator. PhD dissertation. Institute for Transport Studies. Leeds, UK: University of Leeds. Blana, E., & Golias, J. (2002). Differences between vehicle lateral displacement on the road and in a fixed-base simulator. Human Factors, 44, 303e313. Blauw, G. J. (1982). Driving experience and task demands in simulator and instrumented car: A validation study. Human Factors, 24, 473e486. Dagdelen, M., Reymond, G., Kemeny, A., Bordier, M., & Maızi, N. (2009). Model-based predictive motion cueing strategy for vehicle driving simulators. Control Engineering Practice, 17(9), 995e1003. De Waard, D., & Brookhuis, K. A. (1997). Behavioral adaptation of drivers to warning and tutoring messages: Results from an on-theroad and simulator test. International Journal of Vehicle Design, 4, 222e235. Donmez, B., Boyle, L. N., & Lee, J. D. (2008). Accounting for timedependent covariates in driving simulator studies. Theoretical Issues in Ergonomics Science, 9(3), 189e199. ¨ stlund, J. (2005). Effects of visual and Engstro¨m, J., Johansson, E., & O cognitive load in real and simulated motorway driving. Transportation Research Part F: Traffic Psychology and Behavior, 8(2), 97e120. Evans, L. (1991). Traffic safety and the driver. New York: Van Nostrand Reinhold. Evans, L. (2004). Traffic safety. Bloomfield Hills, MI: Science Serving Society. Greenberg, J., Artz, B., & Cathey, L. (2003). The effect of lateral motion cues during simulated driving. In Proceedings of the Driving Simulator Conference North America, Dearborn, 8e10 October. Irving, A., & Jones, W. (1992). Methods for testing impairment of driving due to drugs. European Journal of Clinical Psychology, 43, 61e66. Jamson, A. H., Lai, F. C. H., & Carsten, O. M. J. (2008). Potential benefits of an adaptive forward collision warning system. Transportation Research Part C: Emerging Technologies, 16(4), 471e484. Jamson, A. H., Whiffin, P. G., & Burchill, P. M. (2007). Driver response to controllable failures of fixed and variable gain steering. International Journal of Vehicle Design, 45(3), 361e378. Jamson, S., Lai, F., Jamson, H., Horrobin, A., & Carsten, O. (2008). Interaction between speed choice and road environment (Road Safety Research Report No. 100). London: Department for Transport.

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Kaptein, N. A., Theeuwes, J., & van der Horst, R. (1996). Driving simulator validity: Some considerations. Transportation Research Record, 1550, 30e36. McGehee, D. V., & Carsten, O. M. (2010). Perception and biodynamics in unalerted precrash response. Annals of Advances in Automotive Medicine, 54, 315e332. Mortimer, R. G. (1963). Effect of low blood-alcohol concentrations in simulated day and night driving. Perceptual and Motor Skills, 17, 399e408. Nahon, M. A., & Reid, L. D. (1990). Simulator motion-drive algorithms: A designer’s perspective. AIAA Journal of Guidance, Control and Dynamics, 13(2), 356e362. Olson, R. L., Hanowski, R. J., Hickman, J. S., & Bocanegra, J. (2009). Driver distraction in commercial vehicle operations (Report no. FMCSA-RRR-09-042). Washington, DC: Federal Motor Carrier Safety Administration, U.S. Department of Transportation. Rizzo, M., McGehee, D., Dawson, J., & Anderson, S. (2001). Simulated car crashes at intersections in drivers with Alzheimer disease. Alzheimer Disease and Associated Disorders, 15, 10e20. Santos, J., Merat, N., Mouta, S., Brookhuis, K., & de Waard, D. (2005). The interaction between driving and in-vehicle information systems: Comparison of results from laboratory, simulator and real-world studies. Transportation Research Part F: Traffic Psychology and Behavior, 8(2), 135e146. Schnabel, E., Hargutt, V., & Kru¨ger, H.-P. (2010). Meta-analysis of empirical studies concerning the effects of alcohol on safe driving (Deliverable D 1.1.2a of DRUID (Driving under the Influence of Drugs, Alcohol and Medicines)). Germany: University of Wu¨rzburg. Wu¨rzburg. Segel, L. (1956). Theoretical prediction and experimental substantiation of the response of the automobile to steering control. In Proceedings of the Institution of Mechanical Engineers Automobile Division (pp. 310e330). Slob, J. J. (2008). State-of-the-art driving simulators, a literature survey (DCT Report No. 2008.107). Department of Mechanical Engineering, Eindhoven University of Technology. In: Eindhoven. The Netherlands: Control Systems Technology Group. Transportation Research Board. (1995). Estimating demand for the National Advanced Driving Simulator. Washington, DC: Author. Virginia Tech Transportation Institute. (2009, July 27). New data from VTTI provides insight into cell phone use and driving distraction. Blacksburg, VA: Author. [Press release]. Ward, N. J., & Dye, L. (1999). Cannabis and driving: A review of the literature and commentary (Road Safety Research Report No. 12). London: Department of Environment, Transport and the Regions. Wang, Y., Mehler, B., Reimer, B., Lammers, V., D’Ambrosio, L. A., & Coughlin, J. F. (2010). The validity of driving simulation for assessing differences between in-vehicle informational interfaces: A comparison with field testing. Ergonomics, 53(3), 404e420. Weir, D. H., & Clarke, A. J. (1995). A survey of mid-level driving simulators (SAE Technical Paper No. 950172) Society of Automotive Engineers. Warrendale, PA. Wierwille, W. W., & Fung, P. P. (1975). Comparison of computergenerated and simulated motion picture displays in a driving simulation. Human Factors, 17(6), 577e590.

Chapter 8

Crash Data Sets and Analysis Young-Jun Kweon Virginia Department of Transportation, Charlottesville, VA, USA

1. INTRODUCTION The focus of this chapter is traffic safety, not transportation safety, which means crashes involving modes using roads are of interest and not those involving other modes, such as air, rail, and marine. Subjects of interest for analysis vary depending on study purposes and design. For example, our interests might be persons (e.g., drivers), vehicles (e.g., trucks), facilities (e.g., signalized intersections), or geographical areas (e.g., cities). The choice of subject for analysis often dictates the level of data aggregation affecting types and formats of data suitable for analysis, which in turn affects analysis methods. For example, if we want to compare the traffic safety situation among cities, the annual number of fatal crashes for each city could be obtained by counting all fatal crashes that occurred for each city in a certain year. In this chapter, data useful for traffic safety analysis are introduced, and typical methods for analyzing such data are described. However, this chapter is not intended to be exhaustive with regard to traffic safety data and analysis but, rather, introduces the most frequently used data sources and analysis methods for traffic safety studies.

2. DATA Two types of data are often used in traffic safety analysis: traffic safety data and supplement data. Traffic safety data can be classified into three types: police crash data, medical crash data, and safety survey data. Use of traffic safety data alone might mislead data analysts in attempts to understand factors that contribute to occurrences and outcomes of crashes. For example, we cannot make a fair comparison of the traffic safety situation between two cities based only on the total annual number of traffic crashes if the two cities are quite different in size. In such a case, information reflecting the size of the cities, such as population, road mileage, and/or registered vehicles of the cities, should also be incorporated. Such information does not usually exist in traffic safety data but can be obtained from other data sources. Because those data supplement traffic safety data Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10008-6 Copyright Ó 2011 Elsevier Inc. All rights reserved.

so that we can understand factors that contribute to occurrences and outcomes of crashes in a more complete manner, they are called supplement data here. Various kinds of data can serve as supplement data for traffic safety analysis, including roadway and traffic data, license and registration data, travel survey data, and sociodemographic/ economic data. Use of supplement data for traffic safety analysis helps us avoid biases that might exist in traffic safety data or might be introduced when only traffic safety data are used for analysis, and it also helps us better identify and understand factors contributing to occurrences and outcomes of crashes.

2.1. Traffic Safety Data Three types of traffic safety data are police crash data, medical crash data, and traffic safety survey data. Police crash data are the most frequently used data for traffic safety studies.

2.1.1. Police Crash Data Crash data used for traffic safety analysis are typically obtained from police crash reports; thus, they are called police crash data. These data contain the most important information regarding traffic crashesdinformation on a crash and persons and vehicles involved in the crash. Regulation or statute of each jurisdiction establishes a threshold of property damage or injury severity of a motor vehicle crash to be reported to the police; for instance, many jurisdictions in the United States require a report of any crash on a public road sustaining a minimum of $1,000 to $1,500 in property damage or any level of injury severity including fatality. Although crash reports are completed by police officers in most jurisdictions, a state highway agency or vehicle/driver registration agency, not a police agency, typically has custodial responsibility for statewide crash databases containing crash data provided by local and state police departments in the United States. To perform meaningful analysis, it is necessary to extract a uniform data set from a crash database. However, 97

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it is possible that different jurisdictions use different definitions and criteria and record different sets of information on crash reports. Thus, caution should be exercised when police crash data from different jurisdictions are used for analysis. In the United States, there have been various efforts to establish uniformity in the fields and values on crash report forms so that crash data across jurisdictions can be shared and analyzed comparably. The American National Safety Institute’s D-16.1 Manual on Classification of Motor Vehicle Accidents has been a source for consistent data definitions on crash data, and the Model Minimum Uniform Crash Criteria (MMUCC) has been a source for a minimum set of data elements to conduct meaningful analysis. The Fatality Analysis Reporting System (FARS) promotes collecting data on all crashes involving a fatality according to a shared standard so that consistent data analysis on fatal crashes can be performed.

(e.g., sequential unique number within each crash), unit type (e.g., vehicle, pedestrian, or bicyclist), vehicle type (e.g., pickup truck and bus), make/model and year, vehicle movement and maneuver prior to the crash (e.g., going straight), and an estimate of property damage.

2.1.1.1. Data Elements Several basic data elements are commonly found in police crash data, five of which are described here.

2.1.1.1.5. Injury Severity All jurisdictions use some method to quantify the level of injury severity; at a minimum, two levels are useddfatal and nonfatal injury. The five-level KABCO (killed, A, B, C injury, and property damage only) severity scale is frequently used to record the severity of injuries to each person on police crash reports. K and O are for fatal and no injury, respectively, and A, B, and C are three levels of injurydincapacitating, capacitating (or non-incapacitating), and possible (or minor) injury, respectively. An alternative to the KABCO scale is the Abbreviated Injury Scale, which is more detailed and generally matches better with codes in medical data systems than the KABCO scale. The police record the injury severity for each person involved in a crash based on visual inspection. Because most jurisdictions require information to be recorded only for injured persons, information on persons who are involved in a crash but not injured based on visual inspection is not found in police crash reports. In the United States, most jurisdictions adopt a definition of fatality as death from injuries resulting from a traffic crash within 30 days of the crash, which is consistent with national crash data systems such as FARS.

2.1.1.1.1. Location Information The location where a crash occurs is critical information for traffic safety analysis. The police typically record the location information of a crash using street names and an estimated distance from a physical marker, such as an intersection, bridge, or milepost. Great effort and resources are required when using this text-based location record and the estimated distance on crash reports to identify crashes systematically, and even if crash location is identified, the accuracy of the location may be in question. 2.1.1.1.2. Environment Information Environment in crash data refers to the conditions that are the same for all vehicles and persons involved in the crash. Environment information includes crash report number (a unique identifier), location (e.g., city and street name), time and date, weather condition (e.g., rain or snow), lighting condition (e.g., dusk or dark), surface condition (e.g., wet or icy), and crash type (e.g., head-on or rear-end). Other useful environment information includes work zone indicator and first and most harmful events. 2.1.1.1.3. Vehicle Information A single crash can involve more than one vehicle; thus, a record containing environment information can be linked to several vehicle (or unit) records. “Vehicle” is somewhat deceiving in crash records because typically pedestrian, bicyclist, and vehicles are viewed as a “unit” involved in the crash and a separate vehicle record is created for each unit. Typical vehicle information includes crash report number (the same as that for environment information), vehicle/unit number

2.1.1.1.4. Driver Information Driver information describes the person operating the vehicle or possibly a pedestrian or bicyclist involved in the crash. Information that can be found on or inferred from a driver’s license, such as gender and age, is recorded on police crash reports. Alcohol involvement is an important piece of information recorded on crash reports, and in some cases, blood alcohol content (BAC) test results are entered into the crash data system. However, not all drinking drivers are tested, and not all BAC test results are submitted for entry into the crash data system.

2.1.1.2. National Databases Three national databases based on police crash reports in the United States are frequently used for traffic safety studies: FARS, General Estimate System (GES), and Highway Safety Information System (HSIS). These databases have been used for various traffic safety studies, ranging from policy studies to engineering studies. 2.1.1.2.1. FARS FARS is a census system of traffic crashes that have resulted in the death of a person involved in a crash within 30 days of the crash, and it contains all fatal crashes that occurred in the 50 states, the District of

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Columbia, and Puerto Rico. Each crash record has more than 100 coded data elements characterizing a fatal crash and persons and vehicles involved in the crash. However, to protect privacy, personal information such as name, address, or specific crash location is not included. FARS data are available for every year since 1975 and have three principal filesdthe accident, vehicle, and person files. The accident file contains environment information such as the time and location of the crash, collision type, roadway alignment, the number of vehicles and persons involved, the first harmful event, and weather and surface conditions. The vehicle file contains vehicle and driver information, such as vehicle type, crash avoidance maneuver, height and weight of a driver, initial and principal impact points, and the most harmful event. The person file contains data on each person involved in the crash, such as age, gender, person type (e.g., driver, occupant, pedestrian, and bicyclist), seating position, injury severity, and restraint use. Some information is found in more than one file, such as hit-and-run status in the accident and vehicle files and the first harmful event in all three files. 2.1.1.2.2. GES National Automotive Sampling System GES is a system of sample traffic crashes involving all levels of severity ranging from no injury (i.e., property damage only) to fatal injury, and it contains crashes reported by approximately 400 police agencies in 60 geographic sites throughout the United States. Each crash record has more than 100 coded data elements, and injury severity is coded using the KABCO scale. GES data are available for every year since 1988, and GES, like FARS, has three principal filesdthe accident, vehicle, and person files. The three files of GES are similar to those of FARS in terms of included information. Since 2000, the event file has also been available in GES data. This file contains a brief description of each harmful event in a crash. There have been efforts to unify the two national systems, FARS and GES, with regard to definitions, entry, and analysis of data. To bring the two systems in alignment with the MMUCC, the first of a three-phase standardization process was implemented in 2009 by unifying 45 data elements; the second phase in 2010 unified more data elements and produced one coding manual; and the final phase scheduled to be implemented in 2011 will produce one data entry system for both systems. 2.1.1.2.3. HSIS The HSIS is a multistate database merging crash, roadway, and traffic data that are processed from databases of nine states selected based on the data quality and the ability to merge data from various files. Each state provides different sets of data in different levels of details. For example, the Illinois data system includes four basic files (accident, road log, bridge, and railroad

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grade crossing), whereas the Minnesota data system includes seven basic files (accident, road log, reference post, traffic, intersection, bridge, and railroad grade crossing).

2.1.2. Medical Crash Data Medical data on any person injured for any reason, including persons injured in traffic crashes, are recorded in a system, and the collection, storage, and analysis of medical transportation, treatment, and outcome data of injured persons are referred to as the Injury Surveillance System (ISS). The ISS does not refer to a single database but, rather, a system that is used to track causes, extent, treatment, and recovery from injuries. ISS data are usually available only to health-related agencies, such as state departments of health and hospital associations, but aggregate statistics of such data are often made available for use by outside agencies. Among several databases in the ISS, only a few are useful and available for traffic safety studies. In the United States, the National Highway Traffic Safety Administration (NHTSA) recommends three of the databases for use: (1) emergency medical service (EMS) run report database, (2) trauma registry database, and (3) hospital discharge database. Although ISS data provide detailed information on the extent of injuries and long-term consequences of traffic crashes, issues concerning accuracy and completeness of the data exist. 2.1.2.1. EMS Run Report Database The EMS run report database includes records of injured persons who received immediate care and transportation from EMS providers. Typically, EMS providers are required to submit a report on each run to state agencies such as the department of health. The EMS run reports are not standardized in general, even within a jurisdiction, and the National Emergency Medical Systems Information System (NEMSIS) standard was designed to promote the consistent collection of comprehensive data elements throughout the United States. The NEMSIS standard defines data elements in two sets: (1) service information on the EMS run, treatments, and charges and (2) demographic information on the EMS provider, personnel, and equipment. Most of the states agreed to adopt the standard for their statewide reporting systems. 2.1.2.2. Trauma Registry Database The trauma registry database includes records of persons receiving treatment of trauma cases typically at designated trauma centers. The American College of Surgeons certifies trauma centers and sets guidelines for data elements collected on trauma cases. States certifying trauma centers usually establish a statewide trauma registry database to

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combine registry data on all trauma cases treated at emergency departments of hospitals and designated trauma centers. 2.1.2.3. Hospital Discharge Database The hospital discharge database includes records of inpatients and outpatients admitted to a hospital and contains information on reasons for hospital visits, treatment codes, charges, and a code indicating involvement in traffic crashes. The Universal Billing Code of 1992 (UB-92) is a standard database for generating itemized medical charges at hospitals, and its data requirements serve as the standard for the hospital discharge database. The state hospital associations and the hospitals treating the patients maintain the database. 2.1.2.4. CODES Database Under privacy laws, much of the medical data are strictly available only to authorized personnel of health-related agencies. Even sanitized dataddata for which any personal identifying information, such as name and address, is removeddare not available for use by outside agencies because there is a possibility that a specific individual could be identified when the medical data are combined with other sources of data, such as police crash reports. This use restriction makes it very difficult to link records in the ISS to the police crash data, significantly lessening the usefulness of the medical crash data for traffic safety studies. Because medical data are highly useful for traffic safety studies, the NHTSA developed the Crash Outcomes Data Evaluation System (CODES) for linking the police crash database and the medical crash database. CODES is a probabilistic method matching records in the two databases without using the personal identifying information. The CODES database can be used to produce aggregate statistics so that a specific individual in the database cannot be traced, and it has been proven useful for traffic safety analysis at state and national levels, especially for analyzing outcomes and costs of traffic crashes.

2.1.3. Traffic Safety Survey Data Examples of national traffic safety surveys in the United States are the Motor Vehicle Occupant Safety Survey (MVOSS), National Survey of Drinking and Driving Attitudes and Behavior (DDAB), and the National Occupant Protection Use Survey (NOPUS). NHTSA has periodically conducted national telephone surveys for MVOSS since 1994 and for DDAB since 1991 to understand the public’s attitudes, knowledge, and self-reported behaviors related to restraint use and drinking and driving. MVOSS data contain survey responses regarding occupant protection issues from approximately 6000 respondents, whereas

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DDAB data contain survey responses regarding drinking and driving issues from approximately 7000 respondents. Whereas MVOSS and DDAB are telephone survey data, NOPUS is probability-based observed data on the use of different types of restraints (e.g., front shoulder belts, child restraints, and motorcycle helmets) and driver electronic device use. NOPUS is the only survey that provides nationwide observed data regarding occupant’s restraint use and driver’s electronic device use, and it has been conducted since 2000. NOPUS is composed of two sets of surveysda moving traffic survey and a controlled intersection surveyd and in 2009 its data were collected from approximately 1800 sites throughout the country. There are also other nationwide surveys, such as the American Automobile Association Foundation for Traffic Safety’s telephone survey (Traffic Safety Culture Index), and many local and state-level surveys.

2.2. Supplement Data Traffic safety data alone may not provide a correct view of the traffic safety situation and sometimes might disguise or even distort it. To understand the traffic safety situation more completely, information other than that found in the traffic safety data may be required. Understanding of how traffic safety is affected by various factors such as geometric features of roads, traffic control types, and traffic volume is critical for making informed decisions regarding safety improvements, and these factors are not typically found in the traffic safety data. For example, when we compare the traffic safety situation of two cities with different sizes, we need to obtain not only their crash data (e.g., number of traffic deaths) but also information reflecting their sizes (e.g., number of registered drivers). Among various data that could supplement traffic safety data, four popular sources are roadway and traffic data, license and registration data, travel survey data, and sociodemographic/economic data.

2.2.1. Roadway and Traffic Data It is crucial to know the location where a crash occurs and any roadway and traffic characteristics that may have contributed to the crash. Roadway data help to identify the physical and use characteristics (e.g., horizontal curve and functional classification) of the location that may have contributed to the occurrence or severity of the crash. Traffic data are used to control for use intensity of the location (e.g., traffic volume) and to calculate crash rates (e.g., number of crashes per 100 million, vehicle miles traveled). Highway agencies at the local (e.g., city or county department of public works) and the state (e.g., state department of transportation or highway administration) levels maintain roadway and traffic data on roadways and intersections under their administration.

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Although some guidance on roadway and traffic data elements exist, such as the Highway Performance Monitoring System (HPMS), they are not designed for traffic safety analysis. Model Inventory of Roadway Elements (MIRE) is a data standard for roadway and operations data elements that are critical for traffic safety analysis, and it is anticipated to serve as a companion to the MMUCC. 2.2.1.1. Roadway Inventory Database The roadway inventory database contains physical and use characteristics of a roadway segment and an intersection. The database includes lanes (e.g., the number of lanes and width), shoulders (e.g., type and width), medians (e.g., type and width), access control (e.g., full or partial control), functional classification (e.g., rural/urban interstate highways), speed limits, junction type (e.g., T intersection and ramp), and surface type (e.g., bituminous or Portland cement concrete). There are large variations with regard to elements and the accuracy of the database across jurisdictions. A roadway segment in the database is defined as a stretch of a roadway whose characteristics (e.g., functional classification, speed limit, number of lanes, and shoulder width) are identical, called a homogeneous segment. 2.2.1.2. Traffic Database The traffic database contains traffic volume and characteristics (e.g., traffic counts by vehicle type) of the roadway and can typically be linked to the roadway inventory database. The database includes annual average daily traffic (AADT), speeds, seasonal and directional adjustment factors, and percentage of trucks and buses. 2.2.1.3. Highway Performance Monitoring System The HPMS is a national highway system database containing data on the extent, condition, performance, use, and operating characteristics of highways in the United States, and it supports a data-driven decision process regarding national highway issues. HPMS data are used for assessing performance and investment needs of highway systems and for apportioning federal highway funds. Although the HPMS is not designed specifically for traffic safety analysis, its data contain information useful for traffic safety analysis, covering various aspects of highway characteristics such as roadway inventory (e.g., facility type, turn lanes, and speed limit), traffic operations and controls (e.g., AADT by vehicle type and signals and stop signs), geometric features (e.g., lane width, median type, and grade), and pavement (e.g., surface type, rutting, and base type).

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departments of motor vehicles or departments of public health. Although the raw data are not typically accessible by outside agencies, aggregate statistics of the data can be made available for use by outside agencies on a regular basis (e.g., annual). For example, percentages of teen and elderly drivers for each jurisdiction can be released from the registration agency and are considered to be important for explaining changes in the traffic safety situation over years or differences across jurisdictions.

2.2.3. Travel Survey Data Individual or household travel survey data for transportation planning purposes are collected periodically or need-based nationally or locally. In the United States, the Nationwide Household Transportation Survey (NHTS) collects such data every 5e7 years, and this database contains various travel-related information that is helpful in understanding factors contributing to occurrences and outcomes of traffic crashes. For example, trip purpose (e.g., work and shopping), travel mode (e.g., car and bus), travel time, and travel time of day and day of week are recorded in the survey. It is not possible to link records of individuals in the survey to records in the crash database. However, we could use aggregate forms of the survey data such as summary statistics for traffic safety studies. For example, annual crash rates can be calculated by combining GES and NHTS (Kweon & Kockelman, 2003). Specifically, crash counts have been estimated from GES, and vehicle miles traveled (VMT) have been estimated from NHTS for groups of drivers defined by age, gender, and vehicle type. The annual crash rates have been calculated by dividing the crash counts by the VMT for different crash types and injury severities.

2.2.4. Sociodemographic/Economic Data Sociodemographic and economic data are collected and made available periodically in an aggregate form. Examples of such data include population, age distribution, education level distribution, and unemployment rate. Such data are helpful in understanding changes in the traffic safety situation of an area over years and differences across areas. For example, when analyzing state-level data, percentages of rural VMT, poverty, interstate highway lane mile and seat belt use, and consumption of beer and wine were found to be useful in predicting traffic fatality rates (Kweon, 2007).

3. DATA ANALYSIS 2.2.2. License and Registration Data Driver’s license and vehicle registration data are maintained by licensing and registration agencies such as state

Data analysis for traffic safety studies refers to the use of data from one or more data sources to describe a traffic safety situation and to understand factors contributing to

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occurrences and outcomes of crashes using numbers. Data analysis is neither simply extracting and summarizing numbers from the data nor simply creating tables and figures using the data. Data analysis is telling a story embedded in the data and is interpreting the data reliably, validly, and responsibly. Therefore, as data analysts, we are interpreters of the data and storytellers about the data that we analyze. In this respect, we should be able to relate crashes to factors that might have contributed to occurrences and outcomes of the crashes. The ultimate goal of crash data analysis is to reduce occurrences and improve outcomes of crashes. Data analysis can be classified into two types depending on the level of data aggregation: aggregate and disaggregate analyses. Aggregate data analysis is an analysis using numbers aggregated from information on individual records, whereas disaggregate data analysis is one directly using information on individual records. Aggregation can be done temporally and/or spatially. For example, if we are interested in comparing cities with regard to crash occurrences, we could obtain the total number of crashes that occurred in each city in a certain year by counting individual crash records of each city (i.e., spatial aggregation) in that year (i.e., temporal aggregation). Because this uses aggregated forms of individual data records, it is called aggregate data analysis. If we are interested in identifying personal characteristics likely contributing to outcomes of crashes, we need to use individual records of drivers in police crash data (e.g., age, gender, and violation history) and link them to outcomes of crashes. Because this uses individual data records, it is called disaggregate data analysis. Regardless of analysis type, data analysis is performed primarily to describe the traffic safety situation using numbers and to understand contributing factors. This discussion focuses on aggregate data analysis.

3.1. Aggregate Data Analysis Crash frequencies and crash rates are typical data forms for aggregate data analysis, and crash severity can be incorporated in the process of producing these data. Aggregating individual crash data records suitable for the design and purposes of an analysis is the first step in aggregate data analysis and involves a combination of four basic tasks.

3.1.1. Four Basic Tasks Individual crash data records should be aggregated in a suitable and meaningful form for aggregate data analysis, and data aggregation can be performed through a combination of the four basic tasks: selection, aggregation, integration, and normalization. These tasks are performed not only for aggregate data analysis but also for disaggregate data analysis. The selection task is almost always

PART | I

Theories, Concepts, and Methods

performed for any kind of traffic safety analysis, both aggregate and disaggregate levels, whereas the other three tasks might be performed depending on the design and purpose of an analysis. Thus, understanding these tasks is crucial for preparing appropriate and valid data for analysis. 3.1.1.1. Selection Crashes appropriate for the design and purpose of an analysis should first be selected from the crash database. This process is also called “subsetting” or “filtering.” Selection of crash records can be made based on various factors, such as location/area, type of person/vehicle involved in a crash, weather conditions, or a combination of these factors. For example, if we are interested in assessing the crash risk of a driver in a sport utility vehicle (SUV) in rainy conditions, all crashes involving SUVs that occurred in rain should be extracted from the crash database. If we are interested in comparing injury severity of crashes among different types of roadway (e.g., interstate highway, arterial, and collector), crashes should be extracted separately by roadway type. 3.1.1.2. Aggregation Once crashes of interest are selected, we can aggregate those crashes to generate summary statistics in various ways. Using the example of the crash risk of a driver in an SUV in rainy conditions, if we are interested in comparing the crash risk of such a driver across different ages, the selected crash records should be aggregated by age group (e.g., young, middle-aged, and elderly) to generate the total number of such crashes for each age group. Note that factors used as criteria for aggregation should be categorical in nature. If the values of these factors are continuous (e.g., age of a driver) or categorical but contain many unique values (e.g., speed limits ranging from 15 to 75 mph by 5-mph increments, resulting in 13 unique values), these factors can be recoded so as to have a manageable number of categories (e.g., young, middle-aged, and elderly age groups recoded from continuous age values). 3.1.1.3. Integration To understand factors contributing to crashes in a more complete manner, it is often necessary to incorporate information that is not found in traffic safety data, including roadway characteristics (e.g., functional classification, speed limit, lane width, and junction type) and driver and vehicle registrations (e.g., the number of registered drivers and vehicles in a city). For example, it might be necessary to know the type of roadway on which a driver was traveling (e.g., four-lane rural interstate highway) at the time of a crash to properly understand how personal characteristics (e.g., age and gender) affected the occurrence and outcome

Chapter | 8

Crash Data Sets and Analysis

of the crash. In the example of the crash risk of a driver in an SUV in rainy conditions, the crash risk might differ by roadway characteristics (e.g., speed limit) due to the different performance of the SUV on different types of roads (e.g., high-speed vs. low-speed roads). Thus, the roadway inventory database should be integrated with the crash database. In this example, integration should be completed prior to aggregation because individual crash records should be matched with roadway inventory records before aggregation. 3.1.1.4. Normalization Normalization is typically performed to obtain a crash rate that supports valid comparisons among groups of people (e.g., age groups), vehicle types, roadway types, and so on. In the example comparing two cities in terms of the crash risk of SUVs, the number of crashes involving SUVs and the number of registered SUVs for each city in a certain year are obtained through aggregation. Dividing the number of SUVinvolved crashes by the number of registered SUVs for each city gives a crash rate of SUVs so that the SUV safety situation of the two cities can be evaluated in a fair manner by comparing the SUV crash rates between the two cities.

3.1.2. Example Suppose we would like to compare two cities with regard to the traffic safety situation for senior drivers. One way of doing so is to compare the traffic fatality rate per registered drivers for senior drivers in a specific year. Thus, we need to calculate the traffic fatality rate per drivers for senior drivers. First, the selection task is performed. For the numerator of the rate (the number of fatal senior drivers), records of crashes that occurred in the two cities in the specific year are extracted from the crash database. Among the records, drivers who were older than 64 years (here, a senior driver is defined as one who is older than 64 years) and were recorded “fatal” or “killed” in the database are selected. For the denominator of the rate (the number of registered senior drivers), records of drivers who were registered in the two cities and were older than 64 years during that year are extracted from the driver’s license database. Second, the aggregation task is performed. For the numerator, counting those drivers in the selected crashes for each city produces the annual number of senior fatalities in traffic crashes. For the denominator, counting the selected license records for each city produces the number of registered senior drivers. Third, the normalization task is performed. Simply dividing the number of fatal senior drivers by the number of registered senior drivers for each city produces the traffic fatality rate per registered driver for senior drivers. Senior drivers’ fatality risk in a traffic crash in the two cities can

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now be assessed by comparing the calculated crash rates between the two cities. A couple of points are noteworthy in this example. First, in the aggregation task, for the numerator, we should count drivers in the selected crash records, not the crashes, because there might be crashes involving multiple vehicles resulting in more than one fatal senior driver. Counting the crashes would probably lead to an underestimation of the rate. Second, we may not be able to directly access the crash and/or driver’s license database. Thus, we cannot perform the selection and aggregation tasks on our own but, rather, must request aggregated statistics from agencies managing those databases.

3.1.3. Basic Aggregate Data Analysis Three basic ways of analyzing aggregate data are frequency, rate, and trend analysis. 3.1.3.1. Frequency Analysis A frequency in traffic safety is a tally of crashes, vehicles, or victims, and the traffic safety situation can be quantified by simply counting them, grouped by factors such as location/area (e.g., city or state), group of people (e.g., males age 35 years or older), vehicle type, time period (e.g., holiday weekend night), or a combination of these. Injury severity can be incorporated in the frequency analysis by counting crashes, vehicles, or victims by injury severity sustained. For example, the annual total number of crashes that occur on rural interstate highways during weekends can be obtained for each of three severity levelsdfatal, injurious, and property damage only. 3.1.3.2. Rate Analysis A rate in traffic safety is obtained by dividing the frequency by a normalizing factor that is typically known as a crash exposure measure. Rate analysis is most popular among practitioners because it is straightforward to calculate and easy to understand. Examples of the rate include the fatal crash rate per million vehicles entering into an intersection and the crash rate per 10000 registered drivers in a city. The normalizing factor is intended to support valid comparisons in the traffic safety situation among different groups formed by various factors, such as age, vehicle type, and geographical area. For example, if two roads have the same crash frequency and similar geometric features, the highway with higher traffic volume is deemed safer. Thus, the crash frequencies of the two roads should be normalized by traffic volume (e.g., AADT) so that the safety comparison between the two roads can be made using the resulting crash rate. Examples of frequently used normalizing factors (i.e., exposure measures) include VMT, population, and the numbers of registered drivers and vehicles. VMT is

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typically obtained from a state highway agency’s database, population from census data, and the numbers of registered drivers and vehicles from a state driver/vehicle registration agency’s database. The normalizing factor (denominator) can be the number in the same kind (e.g., same severity level or same crash type) as the frequency (numerator), and dividing the frequency by such a normalizing factor produces a ratio or proportion, not a rate. For example, dividing the number of fatalities by the number of fatal crashes results in the fatality ratio, and dividing the number of single-vehicle rollover crashes by the total number of single-vehicle crashes results in the single-vehicle rollover proportion (or percentage when being multiplied by 100). However, the ratio, proportion, and percentage can be viewed as the rate in a broader sense. Thus, rate analysis is used to analyze them. For rate analysis, it is best for the numerator and the denominator to be in the same unit (e.g., person, vehicle, crash, or roadway) because it is easier to explain the resulting rate. For example, the number of fatalities divided by population is easier to understand than the number of fatalities divided by registered vehicles. There are numerous ways to calculate the rate, but not all rates make sense, even if the numerator and denominator are in the same unit. For example, the number of teen fatalities divided by the number of registered drivers does not make sense even though the numerator and denominator are in the same unit (i.e., persons). Analysts should carefully examine the meaning of the resulting rate. Also, analysts should examine if rates are valid comparison measures (Kweon, 2008). 3.1.3.3. Trend Analysis A trend is simply listing frequencies or rates in chronological order, typically in a graph, so that patterns in frequency or rate over time can be identified and simple prediction of frequency or rate for the future can be made. For example, for a certain city, the annual number of fatalities or fatality rate per population during the past decade can be listed in a graph and a decreasing or increasing trend during the decade can be identified. In the case in which a large fluctuation in the frequencies or rates is present, the moving average technique is especially helpful for identifying the overall trend.

3.1.4. Regression-Based Aggregate Data Analysis The three aggregate data analyses described previously can account for various factors but in a limited way. For example, the crash rate per VMT can be calculated for two cities and a fair comparison between the two cities is possible only with regard to VMT. We can further divide the rate, for example, by urban/rural classification so that we obtain the crash rates for rural and urban roads and

PART | I

Theories, Concepts, and Methods

TABLE 8.1 Illustration of Simpson’s Paradox in Traffic Safety Analysis Region

Fatality rate (per 100 million vehicle miles traveled) Total

Rural

Urban

A

1.27

0.92

2.68

B

2.12

0.87

2.49

a fair comparison is possible for urban and rural separately. We could continue to narrow the crash rates by introducing more factors into consideration for calculating the rates. However, it is very difficult to make an overall assessment of traffic safety using such fine-tuned crash rates. We can make a valid safety assessment only under the conditions considered to calculate those rates. To make a valid assessment under general conditions, it is necessary to control for various contributing factors simultaneously, and a regression analysis is the most popular way of doing so. Including many factors contributing to crashes in the regression equation normalizes those factors simultaneously so that the safety situation of the cities can be evaluated considering different conditions in those factors among the cities being compared. Analysts should be aware that the aggregate analysis, especially the rate analysis, is subject to Simpson’s paradox. Table 8.1 illustrates the paradox in traffic safety. Suppose two regions are compared in the traffic safety situation based on the fatality rate per 100 million VMT. According to the total rate, region A appears to be much safer than region B. However, when the rate is broken down by urban/ rural classification of roads, region B is safer than region A based on both rural and urban rates. The two regions are very different in their VMT ratio between urban and rural roads. VMT of region A is 20% rural and 80% urban, whereas that of region B is 77% rural and 23% urban. A failure to account for the urban/rural classification of roads in calculating the fatality rates in this example leads to the erroneous conclusion that region A is safer than region B.

3.2. Disaggregate Data Analysis Disaggregate analysis is analysis directly using information from individual crash records. A case study examining crash records individually can also provide insight into the traffic safety situation and uses data to some extents but is not considered a data-driven approach. Thus, it is not viewed as disaggregate data analysis. Disaggregate data analysis is typically performed using regression analysis. In preparing data for disaggregate analysis, the aggregation task is not

Chapter | 8

Crash Data Sets and Analysis

usually involved but the other three basic tasks, especially the integration task, may be involved. For example, if we are interested in identifying the contributing characteristics of drivers in SUVs to occurrences and outcomes of crashes on interstate highways in rain while controlling for roadway characteristics of crash locations, we first need to integrate roadway inventory data into crash data (integration), and then we need to select crashes that occurred on interstate highways in rainy conditions (selection) to obtain data suitable for the disaggregate analysis. Various types of regression models can then be applied to the formed datad Typical types of models suitable for crash data are discrete response models (e.g., binary logit and ordered probit models) and count data models (e.g., Poisson and negative binomial models).

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ACKNOWLEDGMENT http://www.trafficrecords101.net is an excellent web-based source to learn about traffic safety data and analysis and served as the main source for this chapter.

REFERENCES Kweon, Y.-J. (2007). Prediction of fatality rates for state comparison. Transportation Research Record, 2019, 127e135. Kweon, Y.-J. (2008). Examination of macro-level annual safety performance measures for Virginia. Transportation Research Record, 2083, 9e15. Kweon, Y.-J., & Kockelman, K. M. (2003). Overall injury risk to different drivers: Combining of exposure, frequency and severity models. Accident Analysis and Prevention, 35(4), 441e450.

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Part II

Key Variables to Understand in Traffic Psychology 9. Neuroscience and Young Drivers 10. Neuroscience and Older Drivers 11. Visual Attention While Driving: Measures of Eye Movements Used in Driving Research

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12. Social, Personality, and Affective Constructs in Driving 13. Mental Health and Driving 14. Person and Environment: Traffic Culture 15. Human Factors and Ergonomics

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Chapter 9

Neuroscience and Young Drivers A. Ian Glendon Griffith University, Queensland, Australia

1. YOUNGER DRIVERS Although not applicable to all individuals, adolescents and emerging adults tend to exhibit various forms of reckless behavior, characterized by sensation seeking and risk taking (Arnett, 1992; Igra & Irwin, 1996; Moffitt, 1993; Spear, 2000). Engaging in one form of risky or reckless behavior increases the likelihood of engaging in others (Dryfoos, 1991; Duangpatra, Bradley, & Glendon, 2009). Risk perception, risk taking, and crash involvement among young novice car drivers, particularly males, has received considerable research attention (Arnett, 2002; Brown & Groeger, 1988; Deery, 1999; Deery & Fildes, 1999; Harre´, 2000; Jonah, 1997; McKnight & McKnight, 2003). Younger drivers are a heterogeneous population aged between 16 and 24 years. Older (20e24 years) and younger (16e19 years) cohorts have been distinguished (Corley, 1999; Jurkiewicz, 2000; Zemke, Raines, & Filipczak, 2000). Giorgio et al. (2008) identified adolescents as aged 13.5e21 years and young adults 22e42 years, reporting that across studies the age differentiating adolescence from young adulthood varied between 17 and 22 years. Notwithstanding individual differences, adolescence could be considered as the teenage years and up to 21 years, whereas emerging adulthood comprises ages 22e29 years. The age range of interest in this chapter is between 16 and 29 years. Driver crash rates increase up to age 18 or 19 years and decline slowly thereafter (Marin & Brown, 2005; Williams, 2003). Crash risk is greatest during the first 6 months or 1000 km (625 miles) of independent driving (Mayhew, Simpson, & Pak, 2003).

2. EVIDENCE FROM DEVELOPMENTAL NEUROSCIENCE RESEARCH 2.1. Prefrontal Cortex The prefrontal cortex (PFC) is the site of executive functions, including high-level cognitive processes through which we develop and execute detailed plans, make judgments about long-term goals, and weigh risks and Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10009-8 Copyright Ó 2011 Elsevier Inc. All rights reserved.

consequences of actions. The dorsolateral PFC (DLPFC), responsible for executive decision making and impulse control, is among the last brain regions to develop, becoming fully mature during approximately the mid-20s (Giedd, 2004). Key cognitive functions ascribed to the PFC include judgment, decision making, working memory (dorsal and lateral regions), and response inhibition (dorsolateral and orbitofrontal regions) (Casey et al., 1997). Casey et al. suggested that the greater the activation of the orbitofrontal cortex during a decision-making task, the greater the inhibition. Growth in this area between ages 10 and 12 years is followed by a dramatic decline that continues into the early 20s due to pruning unused neuronal pathways. PFC maturation is assumed to correspond to higher level cognitive development throughout childhood and adolescence (Casey et al., 1997). Overman (2004) found that in a decision-making gambling task involving the PFC, children were better than adolescents at probability matching, and that adolescents tended to become frustrated by the task, making more errors than did adults. Adolescent behaviors that may be labeled as “irrational” and “disorganized,” including those that can increase driving crash risk, may result from the process of dendritic tree maturation as learning affects higher cortical functions (Dicks, 2005). Implications of this maturation process for driving might include attenuated competence in developing plans and goals for the driving task, impaired ability to weigh consequences of risk-taking behaviors, and a lower threshold for impulse control. Further specific neuroscience evidence on the driving task is required to confirm these implications. Implications of late DLPFC maturation range from whether teenagers should be allowed to drive to whether minors are sufficiently cognitively mature to be subject to the death penalty (Lenroot & Giedd, 2006; Steinberg, Cauffman, Woolard, Graham, & Banich, 2009; Steinberg & Scott, 2003). Lenroot and Giedd pointed to a tendency in these debates to overestimate current knowledge of brain biology, cognition, and behavior, particularly ignoring substantial individual differences. They explained that the 109

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interplay of genetic, epigenetic, and environmental factors means that the relationship between brain development and resultant brain structures is exceedingly complex. As neurons develop, they are encased in a layer of white myelinda lipid and protein sheathdhereafter referred to as “white matter,” which enables a 100-fold increase in transmission speed of electrical impulses between neurons (Blakemore & Choudhury, 2006). Whereas some sensory and motor neurons become fully myelinated in early childhood (e.g., large efferent fibers from the spinal segment), myelination of frontal cortex axons continues well into adolescence. Phylogenetically older brain regions mature earlier than newer regions so that the cortex matures from the back to the front (occipital cortex to frontal cortex) as gray matter (cell bodies and dendrites) volume reduces. Critical changes also occur in the brain’s wiring such that synaptic densitydthe network of connections between neurons or “gray matter”dpeaks during early postnatal development, being higher throughout childhood than in adulthood. Synaptic elimination (pruning), in which frequently used connections (e.g., for the native language) are strengthened and infrequently used connections (e.g., for skills that are no longer practiced) are eliminated, occurs at a faster rate in the first few months of life and again soon after puberty. Neuronal reorganization resulting from synaptic pruning continues throughout adolescence, resulting in a net decrease in synaptic density of the frontal lobes, essential for improving neuronal network efficiency. This dendritic pruning, which results in increased synaptic strength, is particularly important to processing information that is essential to learning as motor information becomes “chunked,” a process that also involves the basal ganglia and cerebellum in feedback that is critical to learning skills such as those involved in driving. Although neuronal death is a natural process, also potentially relevant to driving is the fact that alcohol and other drugs can speed up and distort this process.

2.2. White and Gray Matter 2.2.1. General Changes Increased integration of distributed brain regions, reflected in white matter changes, is associated with greater associative connectivity and more extensive neural networks. Reporting on longitudinal studies of participants aged 3e30 years, Giedd (2008) noted that childhood gray matter peaks were followed by declines during adolescence, with the changing balance between limbic/subcortical and frontal lobe functions extending into young adulthood. Giedd noted the adaptive potential of the neuronal elimination process along with increased connectivity and brain function integration. The changing reward systems and frontal/limbic balance that inter alia serve to increase risk

PART | II

Key Variables to Understand in Traffic Psychology

taking and sensation seeking had been highly adaptive for our ancestors. Although risk taking and sensation seeking might continue to be adaptive in some contexts, such as extending social networks in the search for individual identity, in an unattenuated driving context they would be more likely to be maladaptive. Although brain size does not increase significantly after age 5 years, there is considerable subsequent progressive and regressive growth in different brain regions (Durston et al., 2001; Gogtay et al., 2004; Sowell, Thompson, Holmes, Jernigan, & Toga, 1999; Sowell, Thompson, Tessner, & Toga, 2001). Sowell et al. (2001) reported simultaneous regressive (e.g., synaptic pruning) and progressive (i.e., myelination, synaptic formation, and strengthening of extant synapses) cellular events from childhood to young adulthood. During adolescence, regressive changes dominate progressive changes (Suzuki et al., 2005). Sowell et al. (2001) suggested that improved cognitive task performance through adolescence and into young adulthood could result from regressive changes, such as synaptic pruning, as less efficient or infrequently used connections are discarded. Implications for learning skills associated with complex tasks such as driving lead to the dilemma that whereas this could be an optimum period for learning such skills, it coincides with the time of peak risktaking behavior. Enhanced task performance, such as required in driving, could also result from increased myelination, which improves conduction speed of electrical impulses. Further research is required to identify which cortical regions are associated with particular task types. For example, Casey et al. (1997) found that performance on a decision-making task was only related to activity in the orbitofrontal and anterior cingulate cortices. Baxter, Parker, Linder, Izquierdo, and Murray (2000) found that the ventromedial PFC and amygdala are important in guiding people to use information efficiently concerning positive and negative outcomes required to make good decisions about future behaviors (e.g., driving more safely; Montague & Berns, 2002). A positive relationship has been found between the size of the anterior cingulate gyrus and harm avoidance (Pujol et al., 2002). The cingulate gyrus may be more involved in generating responses than in inhibiting them: The greater the activity in this region, the more likely it is that a motor response will be made (e.g., to avoid harm during driving; Casey et al., 1997). It is also likely that morphological changes could result from practicing certain skills, as in driver training (Draganski et al., 2004).

2.2.2. White Matter Giedd (2008) described myelination as “the wrapping of oligodendrocytes around axons, which acts as an electrical insulator and increases the speed of neuronal

Chapter | 9

Neuroscience and Young Drivers

signal transmission” (p. 336). Myelination also modulates synchronicity of neuronal firing to convey meaning (Fields & Stevens-Graham, 2002), with white matter pathways being particularly important for smooth information flows. Whereas myelination rate varies according to life stage, white matter volumes increase throughout childhood, adolescence, and into the third decade of life (Casey, Galvan, & Hare, 2005; Giedd et al., 1999; Giorgio et al., 2008; Gogtay et al., 2004; Toga, Thompson, & Sowell, 2006), being associated with higher processing speed. White matter volume could continue to increase up to age 60 years (Sowell et al., 2003). Giorgio et al. found that white matter pathways matured at different rates, with the most significant changes in the right body of the corpus callosum, associated descending motor pathways (basal ganglia), and the right superior region of the corona radiata. Although raw processing speed may be important in learning complex tasks such as driving, it is only one component of higher level cognitive functioning. Young drivers’ sometime overreliance on fast reaction time as a buffer against harm is therefore likely to be misplaced.

2.2.3. Corpus Callosum The corpus callosum (CC) is the most prominent white matter structure, comprising approximately 200 million axons connecting equivalent regions of the two cerebral hemispheres. The CC integrates sensory, memory storage and retrieval, attention and arousal, language, and auditory functions (Giedd, 2008; Lenroot & Giedd, 2006). The CC is among the last of the brain’s structures to complete maturation, undergoing rapid growth before and during puberty and lasting through adolescence until the mid-20s (BarneaGoraly et al., 2005; Giedd et al., 1999). CC signal intensity decreases between ages 7 and 32 years, with the most rapid changes during childhood, stabilizing in early adulthood as cerebral functioning becomes more lateralized (Keshavan et al., 2002). The number of connections increases during adolescence, and CC fibers are important in connecting motor and sensory cortices so that increased white matter in this location may be associated with improved motor skills during development (Barnea-Goraly et al., 2005) and in adulthood (Johansen-Berg, Della-Maggiore, Behrens, Smith, & Paus, 2007), such as are required for skilled driving performance. The CC influences handednessdwhether an individual has a strong preference (usually for right-handedness) or is “mixed-handed.” Wolman (2005) reported that rather than left-handers being more prone to have vehicle crashes, as was once thought, it is mixed-handers who are more at risk. Consistent with an interhemispheric model would be the enhanced risk of someone talking on a cell phone (a predominantly left-hemisphere task involving language) while driving with the left hand (a predominantly

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right-hemisphere task for the motor performance component). Further neuroscience evidence is required on potential compromises to the driving task of “cross-talk” between the brain hemispheres that could result from engaging in various secondary tasks during driving.

2.2.4. Gray Matter The childhood peak in gray matter volume and number of synapses is followed by a decline during adolescence, reflecting continuous brain development throughout this period, and a further post-adolescent decrease (De Bellis et al., 2001; Giedd, 2008; Giedd et al., 1999; Paus, 2005; Suzuki et al., 2005). In the nonlinear pattern of gray matter changes, the decrease is most dramatic during adolescence (Sowell et al., 2003). Sowell et al. (2001) found that frontal lobe gray matter loss in the left hemisphere is much greater between adolescence and adulthood than it is between childhood and adolescence. Cortical gray matter loss occurs earliest in the primary sensorimotor areas and latest in the DLPFC and lateral temporal cortex (Gogtay et al., 2004). Whitford et al. (2007) found that gray matter decreased in participants aged 10e30 years in the frontal and parietal cortices, with the greatest change occurring during adolescence. Gray matter changes appear to be region specific, being sometimes progressive and at other times regressive (Blakemore & Choudhury, 2006). For example, gray matter volume in the temporal lobes was found to peak at approximately age 17 years, whereas in the occipital lobes its development was relatively linear (Giedd et al., 1999). In the frontal lobes, gray matter decline was particularly pronounced between adolescence and adulthood. The decline in PFC gray matter volume accelerates during the 20s, although the brain does not reach full maturity until approximately age 30 years (Sowell et al., 2001) and developmental changes continue throughout the adult life span.

2.3. Other Brain Regions Likely to Be Important in Driving Behavior 2.3.1. Amygdala, Hippocampus, and Associated Structures The amygdala and hippocampus, which are concerned with experiencing and expressing emotions, increase in volume with age (Durston et al., 2001; Suzuki et al., 2005). Along with the temporal lobes, the amygdala and hippocampus are involved in emotion, language, and memory for which human capacity changes radically from ages 4 to18 years (Lenroot & Giedd, 2006). The amygdala is critical in assessing the salience of environmental stimuli to survival. The hippocampus, which is involved in memory storage,

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consolidation, and retrieval, has connections with other limbic structures and the neocortex, and it has a role in integrating emotion with cognition (Benes, 1994). Connections between the neocortex and limbic systems are important in memory for stimuli with high salience. Episodic memory operates optimally during adolescence and early adulthood, perhaps due to changing levels of hormones and neurotransmitters (Janssen, Murre, & Meeter, 2007). This feature might have survival value from the perspective of enhanced learning during a developmental stage when sensation seeking and risk taking expose individuals to high levels of environmental stimuli (Giedd, 2008), as during driving. This developmental change would also tend to be positive for early introduction to learning complex skills, such as are required for safe driving performance. The pineal gland produces the hormone melatonin, the levels of which rise in the evening to signal to the body that it is time to sleep. During adolescence, melatonin peaks later in the day than it does in children or adults, which helps to explain why teenagers often prefer to both rise and go to bed later than do adultsdthe “delayed phase preference” (Carskadon, Acebo, Richardson, Tate, & Seifer, 1997; Steinberg, 2008a). Combined with other features of adolescent behavior, this could also help to explain why they may be overrepresented in vehicle crashes at night (Maycock, 2002). Because melatonin levels remain high upon waking for school or work, adolescents tend to be least alert between 8:00 and 9:00 a.m. and may lose up to 2 h of sleep at night as a result of the shift in the timing of the melatonin cycle (Steinberg, 2008a). Chronic sleepiness that could result from this sleepewake cycle, interacting with loss of vigilance in the standard circadian cycle, has the potential to degrade safe driving performance in this age group. Smith, Horswill, Chambers, and Wetton (2009) found that younger inexperienced drivers’ hazard perception skills were significantly impaired by a mild increase in sleepiness. They reviewed research indicating that increased sleepiness impairs a range of cognitive processes vital to safe driving and that many crashes reported as due to “inattention” may be related to sleepiness.

2.3.2. Cerebellum The cerebellum, a component of the oldest part of the brain, governs posture and movement, helping to maintain balance and ensuring that movements are smooth and directed. It also influences other areas of the brain responsible for motor activity and continues to grow until late adolescence. Traditionally associated with balance and motor control, having links with the DLPFC, the medial frontal cortex, and the parietal and superior temporal areas, the cerebellum also has a role in higher cognitive functions, motor learning, and adaptation (Giedd, 2008). All these

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Key Variables to Understand in Traffic Psychology

functions are likely to be critical to driving. Cerebellum volume peaks approximately 2 years later than does cerebral volume, and it is the only brain structure to remain significantly larger in males after co-varying for total cerebral volume (Mackie et al., 2007). Involvement of motor and cerebellar networks in driving was confirmed by Calhoun, Pekar, and Pearlson (2004), who also found that the cerebellar network exhibited a highly significant alcohol dose-related effect for high-speed driving and the number of times the speed limit was exceeded. Compared with drivers aged 30e39 years, male and female drivers aged 17e19 years have approximately twice the proportion of their crashes while negotiating a bend (Maycock, 2002). Maycock also found that 17- to 19-year-old male drivers were more than 50% more likely than were female drivers of the same age group to crash when negotiating a bend.

2.3.3. Temporal Lobes The last parts of the temporal lobes to mature are the superior temporal gyrus and sulcus, which have a role (along with prefrontal and inferior parietal cortices) in integrating memory, audiovisual input, and object recognition. This integration is likely to be critical to the driving task.

2.3.4. Parietal Lobes Brain regions serving primary functions, such as sensory and motor systems, mature earliest; higher level control and integrative functional regions mature later (Casey, Getz, & Galvan, 2008; Gogtay et al., 2004; Sowell, Thompson, & Toga, 2004). Sowell et al. (1999) found that visuospatial functions typically associated with parietal lobes operated at a more mature level earlier than executive functions typically associated with frontal brain regions. Adolescents typically develop their visuospatial abilities prior to the means to fully interpret the meaning of all stimuli. Hence, although young drivers can “see” the same things (including obvious hazards) as adults, they cannot always perceive risks appropriately because they have yet to fully develop higher level cognitive interpretive functions. Critical to risk perception in driving is the ability to appreciate the possibility of hidden hazards and interpret conditional probabilities of the following form: “If that pedestrian has not seen my vehicle and decides to step off the sidewalk . then I had better be prepared to stop quickly.” Differential development of the cerebellum and cortical regions responsible for higher order cognitive processing is consistent with findings from studies revealing younger drivers’ difficulties in perceiving risk accurately. All drivers frequently need to make complex decisions under conditions of uncertainty, which improve with experience. Anticipation and risk avoidance skills develop

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with experience (Simons-Morton, 2002). In gaining this experience, young drivers have the compounding issue of inhibiting greater risk-taking impulses.

2.3.5. Other Subcortical Structures The basal ganglia (caudate nucleus, putamen, globus pallidus, and amygdala) are involved in mediating movement, higher cognitive functions, attention, and emotions (Giedd, 2008; Lenroot & Giedd, 2006), and they have reciprocal connections with the substantia nigra in the midbrain. Large developmental changes occur in the basal ganglia, particularly in males (Casey et al., 2008; Giedd, Snell, et al., 1996). Spear (2000) reported that the balance between dopamine systems begins to shift from subcortical toward cortical levels during adolescenceda change that reflects enhanced acquisition of higher level executive function during this period.

3. CRITICAL ASPECTS OF DRIVING LINKED WITH NEUROLOGICAL DEVELOPMENT 3.1. Response Inhibition Experiments using “go/no-go” tasks require multiple executive functions, including working memory and inhibiting a normal/prepotent response, when participants are shown a certain stimulus (Blakemore & Choudhury, 2006). A driving example would be inhibiting motor responses to start or stop a vehicle at traffic lights when a directional turn (filter) arrow incongruent with the main light is displayed. Studies with children and adults have indicated that inhibiting the normal response (e.g., to proceed or not to proceed when a traffic light shows either green or red, respectively) involves several regions of the frontal cortex, including the anterior cingulate, orbitofrontal cortex, and the inferior and middle frontal gyri (Casey et al., 1997). Individual differences were evident; for example, participants with the lowest error rates showed the greatest orbitofrontal activation and the least DLPFC activation. Casey et al. suggested that during adolescence, the neural network recruited for such tasks matures so that by adulthood a smaller region of the PFC is used to perform this type of task. Continual development of the PFC and parietal cortex during adolescence is reflected in executive functiondcontrolling and coordinating thoughts and behavior. This includes such cognitive features as selective attention, decision making, voluntary response inhibition, and working memory (Blakemore & Choudhury, 2006). During driving, these functions might be involved in filtering out unimportant information (e.g., irrelevant road signage), prospective memory (remembering to carry out an intended action in the future such as a route to

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a destination), and inhibiting impulses (e.g., expressing anger at other road users). These skills rely heavily on the frontal lobes, and studies have shown that adolescents’ task performance continues to develop, for example, in inhibitory control (Leon-Carrion, Garcia-Orza, & Perez-Santamaria, 2004; Luna, Garver, Urban, Lazar, & Sweeney, 2004), processing speed (Luna et al., 2004), and working memory and decision making (Hooper, Luciana, Conklin, & Yarger, 2004; Luciana, Conklin, Cooper, & Yarger, 2005). Speed of switching between tasks, essential as a driving skill, continues to develop during adolescence, which could be another argument for early acquisition of driving competence.

3.2. Controlling Risky Behavior Steinberg (2008b) attributed the increase in risk taking between childhood and adolescence to changes in the brain’s socioemotional system, which tends to increase reward seeking, particularly in the presence of peers. Steinberg attributed the decline in risk taking between adolescence and adulthood to changes in the brain’s cognitive control system, which gradually improves an individual’s capacity for self-regulation. The right ventral striatum, thought to be involved in motivating rewardseeking behavior, is less active in teenagers than in adults, suggesting that adolescents could be more prone to risky but potentially high-reward behaviors (Dicks, 2005). Seeking to account for adolescents’ greater risk-taking propensity, Bjork et al. (2004) suggested that differences in brain activation in mesolimbic regions during incentivemotivated behaviors might be involved. Compared with adults, Bjork et al. found that when anticipating gains, adolescents showed reduced activity in the right ventral striatum and right amygdala, which could be associated with reduced levels of fear. These authors suggested that adolescents’ risk behaviors compensated for low recruitment of networks associated with these brain regions by seeking more extreme incentives. Driving examples include speeding and tailgating, as well as more deviant forms of driving behavior. Steinberg reviewed evidence indicating that during adolescent brain development, peer acceptance may be processed in similar ways as other types of rewards, which could be critical for risk taking during driving. Baird and Fugelsang (2004) studied that aspect of human reasoning concerned with imagining alternative outcomes and the consequences of different courses of actiondessential aspects of risk perception and risk-taking behaviors. Comparing brain activity in teenage and adult samples faced with dangerous and safe scenarios, adults showed greater activity in parts of the brain creating mental imagery and signaling internal distressdboth associated with a rapid and automatic response to danger. Baird,

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Fugelsang, and Bennett (2005) found that compared with adolescents, adults showed greater activation in the insula and right fusiform face area when assessing potentially dangerous actions, indicating that these processes had been automated in adults but not yet in adolescents. Teenagers showed increased PFC activity, with reasoning and judgment activity resulting in extended decision making, revealing a less appropriate response to inherently dangerous situations (e.g., driving at high speed), which do not require extended reasoning for most adults. Confronted with a potentially dangerous scenario, adults were more likely to create a mental and visceral image (“gut feeling”) of possible outcomes (e.g., injuries that could be sustained) and to have a readily available appropriate aversive response based on affect (e.g., “this feels like a very bad idea”). Teenagers, who took significantly longer to decide upon the dangerousness of the scenarios and to produce a correct response, appeared to use reason, involving greater DLPFC activation, to determine whether a scenario was dangerous because they did not have a general mental image and associated visceral response available to guide their decision making. Reyna and Farley (2006) argued that adolescents are able to reason and understand the risks of the behaviors in which they engage, using an intuitive form of decision making. However, actions taken in rewarding and emotional contexts explain why some teenagers are at greater risk for poor decision making and bad outcomes (Galvan et al., 2006; Galvan, Hare, Voss, Glover, & Casey, 2007). Important to successful maturation is developing an ability to suppress inappropriate thoughts and actions in favor of goal-directed ones, particularly within a context of competing and more immediate rewards (Casey, Tottenham, Liston, & Durston, 2005; Casey et al., 2008). All these features may be represented in driving. Risk taking, which is distinct from impulsivity, tends to be higher in adolescence than in either adulthood or childhood. It is associated with subcortical regions, specifically the nucleus accumbens, a region of the basal ganglia involved in making risky choices when evaluating potential rewards (Bjork et al., 2004; Casey et al., 2008; Ernst et al., 2005; Ernst, Pine, & Hardin, 2006; Galvan et al., 2006, 2007; Kuhnen & Knutson, 2005; Matthews, Simmons, Lane, & Paulus, 2004; May et al., 2004; Montague & Berns, 2002). Activity in this region immediately prior to making risky choices is higher in adolescents than in either children or adults (Ernst et al., 2005; Galvan et al., 2006). Casey et al. postulated that adolescents’ increased tendency to higher levels of both impulsivity and risk taking, compared with other life cycle stages, could be explained by their bias to immediate rewards over achieving longer term goals imposed by the combination of changes in the relative rates of development of subcortical regions (e.g., accumbens) to control regions

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(e.g., ventral PFC) and white matter tracts linking these regions. In a driving context, this developmental feature could lead adolescent drivers to favor immediate rewards linked to risk taking over longer term life goals, such as remaining free from injury. Although adolescents in general appear to be more prone to risky decision making (Gardner & Steinberg, 2005), Casey et al. (2008) highlighted the importance of individual differences among adolescents with respect to risk taking, suggesting that genetic variation could underlie such differences, for example, relating to dopamine release to subcortical regions (O’Doherty, 2004; Steinberg, 2008b). Exploring the association between reward-related neural circuit activity and anticipating monetary rewards, Galvan et al. (2007) found a positive association between accumbens activity and the likelihood of engaging in risky activity across participants aged between 7 and 29 years. However, in individuals who perceived risk taking as leading to bad outcomes, the accumbens was less activated to reward. Impulsivity was related to age but not with accumbens activity. Casey et al. interpreted their findings as indicating that adolescent choices and behavior could not be explained by impulsivity or by PFC maturity following that of other brain regions alone. However, they could be accounted for by individual differences in risk taking as well as explaining how this aspect of adolescent behavior differed from that of both children and adults. In addition, social and situational influences such as peer pressure can play an important role in risk taking during driving. One possible mechanism for the transmission of risktaking behavior resulting from peer pressure during driving emerged in a study comparing an adolescent sample (aged 14e19 years) with older and younger groups. In this study, Cohen et al. (2010) determined that the striatum and angular gyrus were the two regions in which only the adolescent group had a hypersensitive response to prediction error. A driving example might involve approaching traffic lights showing green at speed from some distance away and seeing them turn to amber at the critical “go/nogo” decision pointdthe prediction in this case being that the lights would remain green. The ventral striatum was consistently sensitive to unexpected positive feedback, and of the three groups studied, only the adolescents responded more quickly to large rewards compared with small rewards. The “reward” in the traffic lights example would be successfully clearing the junction, irrespective of the color of the lights at the time of crossing. Building on the work of Ernst et al. (2005) and Galvan et al. (2006), which found that adolescents have a hypersensitive response to reward, Cohen et al. found that this was specific to prediction error rather than to valuation signals. Continuing the traffic lights example, valuation signals might involve reflecting on, or being alerted to by a passenger, the possibility that in different circumstances the outcome

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could have been differentdfor example, involving a crash. Cohen et al. surmised that developmental differences in prediction error responses reflected differences in dopamine signaling, which could explain risky reward-seeking behavior typical of adolescents, for example, in driving. Cohen et al. considered that their data were consistent with a greater effect of positive outcomes, resulting in increased motivation for positive outcomes (e.g., peer approval of risky driving) and hence greater risk-taking propensity. They argued that adolescents’ overactive dopaminergenic predictive error response could result in increased reward seeking, particularly when combined with an immature cognitive control system. In a monetary decision-making task involving risk, compared with adults, adolescents showed lower levels of ventral PFC (VPFC) activity (Eshel et al., 2007). Although the strictures of ecological validity mean that the extent to which experimental studies of risk in decision making can be extrapolated to real-world driving risk is uncertain, such findings would be consistent with observed data, for example, of adolescents’ relatively high level of involvement in vehicle crashes. However, it remains to be determined whether the same brain circuitry that appears to guide non-life-threatening risk taking is also critical to the etiology of young drivers’ heightened risk-taking susceptibility. In a review, Ernst et al. (2006) explained the propensity during adolescence for reward/novelty seeking when confronted with uncertainty or potential harm by the combination of a strong reward system (nucleus accumbens), a weak harm-avoidant system (amygdala), and an inefficient supervisory system (medial/ventral PFC). This combination could be reflected in dangerous driving behavior. Notwithstanding individual differences, diminishing impulsivity throughout adolescence is associated with PFC development (Casey, Tottenham, et al., 2005; Casey et al., 2008; Galvan et al., 2007). Liston et al. (2006) showed that although white matter tracts continue to develop into adulthood, only those linking the PFC with the basal ganglia are associated with impulse control. Such findings emphasize the importance of the development of circuits as well as brain regions. Cognitive development throughout adolescence is characterized by increased efficiency of cognitive control and improved emotional regulation, primarily dependent on PFC maturation (Yurgelun-Todd, 2007). Casey et al. (2008) argued that limbic subcortical and prefrontal control regions should be considered as linked components of a developing system, with the limbic component developing before the prefrontal region. They suggested that adolescents’ behaviors are biased by their more functionally mature limbic regions. Developing functional connectivity between these components is critical to enhanced top-down control of subcortical regions. Cortical connections are enhanced as synapses are pruned

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through a combination of development and experience. A key challenge for researchers and practitioners of young driver training is to capitalize on this developmental imperative while simultaneously moderating the inevitable downside of this critical maturational phase. This might be achieved by early and frequent reinforcement of the pathways that would develop naturally during this perioddfor example, by ensuring that adolescents are given opportunities to practice impulse control in simulated driving scenarios or during on-road driver training. This would contrast with the more typical approach to the practical component of driver training, which is to practice behaviors that accord with the relevant jurisdiction’s road rules until a certain standard can be reproduced during the driving examination. Whereas future orientation, impulse control, resistance to peer influence, punishment sensitivity, and planning showed linear increases during early adolescence, Steinberg (2008b) found a curvilinear pattern for sensation seeking, risk preference, and reward sensitivity. These distinct patterns might suggest that although those psychosocial phenomena that develop linearly might be open to “accelerated learning” strategies (e.g., during driver training), context-based approaches are likely to be required for those that develop nonlinearly. For example, because risk taking is more likely to be a group-based phenomenon for adolescents than for adults, restrictions on the number of peer passengers should be mandatory for younger drivers.

3.3. Processing Emotions Adolescence is known to be a time of greater vulnerability to an imbalance between emotion processing and top-down regulation. For example, exposure to both positive and negative information has been shown to result in heightened activity in subcortical limbic regions (e.g., ventral striatum and amygdala) in adolescents compared with adults (Ernst et al., 2005; Eshel et al., 2007; Galvan et al., 2006; Monk et al., 2003). Although later development of subcortical systems relative to top-down control systems generally biases adolescents’ behavior toward immediate over long-term goals (Galvan et al., 2006), this effect is moderated by considerable individual variability in emotional reactivity and its regulation. Basic emotions are generated mainly in the limbic system with neural circuits associated with each emotion, including happiness, fear, and anger (Panksepp, 1998, 2001). Panksepp maintained that each basic state was associated with an action category shaped by evolution to continue an activity, to sense danger and escape, or to prepare for confrontation. Limbic system circuits are relatively fixed and can powerfully affect our cognitions. The amygdala performs a rapid initial appraisal to detect

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whether some external event presents a threat, and it induces feardthe “low road” (LeDoux, 1997). This can result in physiological responses that include blood pressure change, stress hormones release, and the startle reflex, as well as behaviors such as flight, fight, or immobilization. The longer route, via the cortex (the “high road”), results in more detailed analysis of an event. During adolescence, additional demands are placed on executive (cognitive) systems (e.g., memory, perception, decision making, and problem solving) and on the interplay between cognitions and emotional processesdfor example, in processing verbal and nonverbal environmental cues. These include the cognitions and emotions involved in interpersonal peer interactions (Watkins et al., 2002). Although basic aspects of face perception are in place soon after birth, quality and quantity of processing the meaning of facial expressions continue to improve throughout adolescence (Carey, 1992; McGivern, 2002; Taylor, McCarthy, Saliba, & Degiovanni, 1999). Of potential relevance to driving, and other social situations, is that processing the critical emotion of fear in others’ faces appears to be relatively weak in adolescents (Baird et al., 1999; Thomas et al., 2001). In a driving context, this effect could impair interpreting emotional feedback from peersdfor example, fear or anxiety expressed facially by passengers in a vehicle driven by an adolescent (Doherty, Andrey, & MacGregor, 1998). Hare and colleagues (Hare, Tottenham, Davidson, Glover, & Casey, 2005; Hare et al., 2008) showed that mean reaction times for acknowledging fearful facial expressions were positively correlated with amygdala activity. Hare et al. (2008) found elevated amygdala activity in an emotional context (fearful faces) in adolescents compared with children and adults. Individual differences in emotional expression could be accounted for by the strength of connectivity between top-down control regions, especially the VPFC and bottom-up emotional processing regions (amygdala). In adolescents, the strength of coupling between VPFC and amygdala was correlated with greater habituation of amygdala activity, showing that learning could occur. Less trait-anxious adolescents showed greater VPFC and less amygdala activity (habituation) after initial trials on a go/no-go task. Compared to adults, adolescents responded more slowly to fear targets and showed less prefrontal relative to amygdala activity, suggesting that they were likely to be more susceptible to emotional interference in decision making (“heat of the moment”) (Luna & Sweeney, 2004). Suppressing competing emotional responses involved adolescents engaging more prefrontal regions compared with adults (Luna & Sweeney, 2004). Although individual differences should not be ignored, learning in the context of specific stimuli can occur within a relatively short period of time (Hare et al., 2008). This knowledge could be put to

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good use in training young drivers to enhance their level of control when faced with potentially emotionally arousing situations (e.g., perceived threats from other road users).

3.4. Sex Differences On average, the male brain is 10% larger than the female brain, with most structures displaying this difference (Durston et al., 2001). Females have proportionately more gray matter and less white matter; males have a higher ratio of white matter to brain volume (Luders et al., 2005; Nagel et al., 2006). Nagel et al. found a negative relationship between prefrontal white matter volume and age, particularly among an adolescent female subsample. In contrast with previous research, the nonlinear trend in their data suggested that at approximately 15 years of age, prefrontal white matter appeared to decline. Women have a higher concentration of gray matter in the neocortex (the phylogenetically newer part of the cerebral cortex), whereas men have proportionately more gray matter in the “older” entorhinal cortex. During childhood and adolescence, males have more prominent age-related gray matter decreases and white matter increases (De Bellis et al., 2001; Giedd et al., 1999). Male brains consistently show greater hemispheric asymmetry; in female brains, the two hemispheres are much more alike (Good et al., 2001). Total cerebral volume peaks at 11.5 years in girls and 14.5 years in boys (Giedd, 2008; Lenroot & Giedd, 2006; Lenroot et al., 2007). By age 6 years, brain volume is at approximately 95% of this peak (Giedd, 2008; Lenroot, & Giedd, 2006). Gray matter volumes follow inverted Ushaped trajectories, which are distinct for each lobe, and peak 1e3 years earlier in females (Lenroot & Giedd, 2006; Lenroot et al., 2007). Although mean total cerebral volume is 9 or 10% larger in boys (Goldstein et al., 2001; Lenroot & Giedd, 2006), total brain size differences do not imply any functional differentiation, and healthy children of the same age may show up to 50% difference in total brain volume (Giedd, 2008; Lenroot & Giedd, 2006). Brain morphology is highly variable between individuals, and although there are significant sex differences, there is considerable overlap between male and female distributions. Silveri et al. (2006) found that functionality in different brain regions accounted for some sex differences in impulse control. Although gender differences in human brain anatomy are well-established (Goldstein et al., 2001; Gro¨n et al., 2000; Gur, Gunning-Dixon, Bilker, & Gur, 2002; Nopoulos, Flaum, O’Leary, & Andreasen, 2000; Overman, 2004), the development of such differences is less well understood. Evidence from studies of adolescent brains has shed light on differential rates of maturation of the amygdala and higher cortical areas that control impulsive behavior. Although it has been known for some time that

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girls’ brains develop faster than those of boys, research has revealed larger differences than previously suspected. However, the exact “developmental lag” of males vis-a`-vis females remains problematic. For example, Caviness, Kennedy, Richelme, Rademacher, and Filipek (1996) found that the typical brain of a 17-year-old boy resembled that of an 11-year-old girl, and a similar difference was found by Anokhin, Lutzenberger, Nikolaev, and Birbaumer (2000). Measuring brain myelination, Benes, Turtle, Khan, and Farol (1994) found that between ages 7 and 22 years, girls’ brains were 3 or 4 years ahead of boys’ brains, and that the men did not catch up with the women until age 29 years. Blanton et al. (2004) found significant gender differences in white matter development in speech-related brain areas, with boys but not girls showing a linear age-related increase in white matter volume. However, Giedd et al. (1999) found that gray matter development peaked at age 10 years in girls and at age 12 years in boys, after which ages there was a significant decrease in gray matter volume. De Bellis et al. (2001) found that between 6 and 18 years of age, males had a 19% reduction in gray matter compared with a less than 5% reduction in females. They also found that males had a 45% increase in white matter and a greater than 58% increase in CC area compared with females, for whom corresponding increases were 17 and 27%. The caudate nucleus and the hippocampus, which contain predominantly estrogen receptors, are proportionately larger in female brains, whereas the amygdala (containing predominantly androgen receptors) is proportionately smaller (Lenroot & Giedd, 2006). Longitudinal data showed that amygdala volume increased significantly with age only in males, whereas hippocampal volume increased significantly with age only in females (Giedd, Vaituzis, et al., 1996). During childhood and adolescence, myelination in the hippocampus occurred earlier in females than in males (Benes et al., 1994; Suzuki et al., 2005). Gur et al. (2002) reviewed studies on the generation and regulation of emotion. The amygdala is considered to be primarily involved in the excitatory aspects of emotional behavior, including aggression, whereas the orbitofrontal region has a modulating function. Gur et al. found that orbitofrontal brain regions were relatively larger in women than in men, and that compared with men, women had more brain tissue available for modulating amygdala input. One implication is that women have more available brain tissue that can moderate emotions underpinning behavioral displays such as are seen in aggression, which may play a key role in some forms of risk taking, such as when driving. Goldstein et al. (2001) confirmed that women had larger cortex (particularly frontal and medial paralimbic cortices) volume relative to cerebrum size, whereas men had larger volumes relative to cerebrum size in the amygdala and hypothalamus. However, reviewing evidence on

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gender differences in risk taking, Steinberg (2008b) considered that sex differences in risk-taking behavior may be mediated more by context than by biology, and that the gender gap in real-world risk taking could be narrowing independently of sex differences in brain morphology. Studying how emotion is processed in the brains of children aged between 7 and 17 years, Killgore, Oki, and Yurgelun-Todd (2001) found that in young children emotional activity was localized in the amygdala and other older subcortical brain areas, and that at this age connections to higher brain centers (cerebral cortex) had not yet been made. During adolescence, brain activity associated with emotion moves to the cerebral cortex, but by age 17 years this change has occurred only in girls, whereas in 17year-old boys the locus of emotional control remains in the amygdala. Killgore et al. also found differences between male and female children and adolescents’ amygdala versus prefrontal activation while viewing faces expressing fear. With increasing age, females, but not males, progressively increased prefrontal activation relative to amygdala activation. Critical from a driving perspective (and other activities involving risk) would be knowing the extent to which these research findings reflected real differences between males’ and females’ respective PFC regulated responses to fear, for example, as reflected in passengers’ facial expressions or other potentially fearinducing stimuli, and their consequential impact on driving behavior.

4. DISCUSSION AND CONCLUSIONS Understanding the full implications of absolute and differential stages of brain development for driving remains in its early stages. Because behaviors emanate from integrated activity among distributed networks, a research priority is to link brain regions and circuits with relevant cognitions, including those relating to safety and risk perception and behaviors relevant to avoiding danger and managing risk. For example, Calhoun et al. (2002, 2004) identified seven separate brain networks involved in a simulated driving task in small samples of young drivers: (1) bilateral components of the parieto-occipital sulcus, including portions of cuneus, precuneus, and lingual gyrusdinvolved in visual monitoring; (2) mainly occipital areasdfor low-order visual processing; (3) bilateral visual association and parietal areasdfor high-order visual processing and visuomotor integration; (4) motor cortex and (5) cellebellar areasdfor gross motor control and motor planning; (6) orbitofrontal and cingulatedfor error monitoring and inhibition, including motivation, risk assessment, and “internal space”; and (7) medial frontal, parietal, and posterior cingulatedfor vigilance, including spatial attention, visual stream, monitoring, and “external space.” Comparing task performance of adolescent samples aged

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15 and 16 and 18e20 years, Santesso and Segalowitz (2008) found a greater amplitude in anterior cingulate cortex activity among the older group. This brain region, associated with performance errors and self-monitoring, was found to be more mature in the older group. However, response times and accuracy were comparable across the groups, suggesting that the greater error rate in the younger sample was not due to performance differences. This study might suggest an age-related basis for driving-related errors that are not related to basic driving skill, particularly those associated with self-monitoring of performance. Giedd (2008) suggested that differences in developmental trajectories could be more informative than adult differences. For example, Shaw et al. (2006) found that at age 20 years, developmental trajectories were more predictive of IQ than were cortical thickness differences. However, as Steinberg (2008b) noted, current knowledge about neurophysiological changes during adolescence exceeds our understanding of how these relate to specific behaviors. Steinberg’s research indicated that although basic information processing showed no maturation after age 16 years, self-reported future orientation improved up to age 18 years, whereas planning and impulse control continued to increase through the early 20s. Respective contributions of these cognitive functions to driving might be coordinated with the delivery of relevant driving-related information within each of these age ranges. As our understanding of the role of brain function and developmental stages improves, it should be increasingly possible to link these with skill development and training for particular driving tasks (Eby et al., 2007). Anticipating probable intentions and behaviors of other road users is among many driving skills required. Psychologically, this is described as taking the perspective of another. Common brain areas (popularly known as “mirror neurons”) in the superior parietal and right inferior frontal cortices are activated both when an individual performs an activity and when they observe another performing that same activity (e.g., driving). Although social perspective taking develops during puberty, further research is required to establish when the perceptual, motor, and social functions required for mature adult performance become fully integrated (Blakemore & Choudhury, 2006). To assist in identifying links between brain function and development, on the one hand, and cognitive indicators relevant to safety and risk cognition and driving behaviors, on the other hand, triangulated methods are required. Because most research to date has been cross-sectional, more longitudinal studies are needed to enhance understanding of these links. Reflecting the cost of data collectiondfor example, using functional magnetic resonance imaging (fMRI) techniquesdsample sizes in several studies have been small. Resources are required to increase sample sizes so as to enable population norms, including

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variances, for males and females at different ages with respect to brain development to be mapped in more detail. Age and sex differences in brain development are complex, and key individual differences may be difficult to reveal. As population norms for age and gender differences become clearer, it should be possible to identify individual differences. It is well recognized that brain systems controlling arousal, emotional experiences, and social information processing become much more active at puberty, and that these are associated with increased novelty-seeking, sensation-seeking, and risk-taking behaviors. With puberty for many young people beginning at 12 years or younger and the age at which the brain can be considered to be fully mature in both sexes now being assessed as the mid to late 20s, there is a considerable time period during which the brain is being driven by hormonal changes to undergo substantial transformations in structure and function. Improving our understanding of these processes and their implications for safety and risk-related behaviors is a key issue for future research. Young drivers are much more likely than older drivers to be influenced by their peers (Gardner & Steinberg, 2005; Simons-Morton, Lerner, & Singer, 2005; Steinberg, 2008b). Gardner and Steinberg found higher levels of risk taking, greater focus on benefits than potential costs of risk taking, and riskier decisions by adolescents when in peer groups than when alone. Steinberg reported fMRI data indicating that although brain regions activated in a driving task associated with cognitive control and reasoning (e.g., prefrontal and parietal association cortices) were active irrespective of driving condition, additional brain regions were activated (medial frontal cortex and left ventral striatumdprimarily the accumbens, left superior temporal sulcus, and left medial temporal structures) when peers were present. This socioemotional network led to more risky driving behavior, indicating that peer presence enhanced rewards from potentially risky driving behavior. Gardner and Steinberg (2005) found that although selfreported resistance to peer pressure continued to age 18 years, peer presence continued to be evident at age 20 years. According to Engstro¨m (2003), peer presence appears to influence risk taking in young people up to approximately the age of 25 years. Until this age, the brain’s prefrontal lobes, which govern an individual’s ability to plan, control impulses, and weigh risks and benefits, are still maturing. When age and maturation are taken into account, task experience remains important. There is thus a need to provide “safe” learning opportunities during the early period of learning to drive. To partly offset adverse effects of peer influence, alternative role models are important. Within a driving context, parents provide important role models, particularly the young driver’s same-sex parent (Glendon, 2005).

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Kuhn (2006) argued that enhanced executive control is the major feature of cognitive development during adolescence. She focused on adolescents’ increasing ability for self-determinationdfor example, monitoring and managing their learning. During this period, improved information processing is achieved through a combination of faster speed (via increased myelination of nerve fibers), greater capacity (e.g., memory), and more effective inhibitiondboth resisting interfering stimuli and controlling one’s own responses. Luna et al. (2004) suggested that improved neuronal communication and increased myelination not only supported greater processing speed but also benefited response inhibition through more efficient synaptic organization. However, Kuhn noted that although experimental paradigms can provide evidence for the effects of instruction to inhibit responses, there is much less information about situations in which adolescents make their own decisions about inhibiting their cognitions or behaviors and their success in achieving this. In other words, adolescents’ self-regulatory processes under “reallife” conditions, such as driving, have been little studied, but it is particularly important to know about such situations. As Lerner (2002) noted, adolescents are already largely self-developing. Neurological development is partly experience driven, in that neuronal connections are strengthened by activities that are engaged in, whereas connections that are not developed through experience weaken. This presents a paradox with regard to an activity such as driving, given that a central feature of many adolescents’ self-image is their driving ability. The more (and the earlier) experience that an adolescent has of driving, the stronger will become neuronal connections that relate to this activity and the more skillful he or she will become. This means that a critical task for those involved in training young drivers is to ensure that their developing skill and self-confidence are appropriately matched with the maturity to make good decisions and to infuse their perception of their own driving competence with learned appropriate inhibitory responses to as wide a range of situations as possible. This argues for an extended period of learning, involving multiple inputs and testing to take account of large interindividual variability in the range of abilities required to master complex driving tasks (Eby et al., 2007). On the basis of evidence reviewed here, what are likely to be among the most promising strategies within a driving context for promoting harm avoidance and risk reduction for younger drivers? Given that multitasking skills develop over an extended period up to young adulthood, it is important to limit the number of tasks that a driver needs to perform. Banning the use of cell phones, in-vehicle entertainment (perhaps other than radio), and other distractions is one option. A strategy pursued by increasing numbers of jurisdictions worldwide consists of a graduated introduction to the road environment. The success of graduated

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driver training programs in the United States has been extensively reviewed (Branche, Williams, & Feldman, 2002; Foss & Evenson, 1999; Hallmark, Veneziano, Falb, Pawlovich, & Witt, 2008; Hedlund, 2007; Hedlund, Shults, & Compton, 2006; Keating, 2007; Lin & Fearn, 2003; Neyens, Donmez, & Boyle, 2008; Shope & Molnar, 2003). These might include passenger restrictions (to limit peer influence) and limiting nighttime driving (a known high risk). Rather than focusing on purely punitive approaches, Blakemore and Choudhury (2006) recommended allocating more resources to educational and rehabilitation programs to take greater account of normal developmental changes in adolescents’ brains. One option might be a specified number of hours of practice on a controlled road network, which could also provide video feedback on a young driver’s performance, emphasizing safe driving. Because of their effects on brain and behavior, alcohol, drugs, and fatigue add substantially to risks faced by younger drivers. Stringent testing, controls, and sanctions should apply. Although the intellect of a young person might not be in doubt, to address the affective component of brain maturation and to mitigate what could otherwise constitute social pressure to engage in risk-taking behaviors, arrangements could be made to engage young drivers in dialogues with seriously affected victims of road crashes in which young drivers played a key role. These might include seriously injured victims, bereaved close family members, or contrite “perpetrators.” Such encounters need to be managed so that young drivers are able to identify with the “victim” in each case and are encouraged to imagine how they could be involved in an incident similar to that resulting in the victim’s situation (McKenna & Albery, 2001). Furthermore, young drivers should be provided with the necessary competencies to avoid such an outcome happening to them. As noted previously, brain development is partly shaped by experience; therefore, as one component of enhancing risk perception, adolescent drivers need to be exposed to some of the consequences of risk taking. An issue of likely continuing controversy is the age at which a young person should be eligible to obtain a driver’s license. From the neuroscience findings described in this chapter, when could a young person be deemed ready to become a driver? Waiting until all brain regions and neural networks have fully matured in both sexes, which does not occur until the late 20s, is unrealistic. Assuming that the starting age for driving on public roads is 16 years, given the evidence reviewed here, what developmentally defensible options might reasonably be imposed? Although complete evidence for a definitive answer to this question is still unavailable, general guidelines might be suggested, which happen to be broadly consistent with developing practice with respect to graduated licensing programs in several jurisdictions. During the stage at which major

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neuronal reorganization is occurring, from approximately ages 16 to 21 years (identified as adolescence in this chapter), strategies might reasonably be aimed at hastening those aspects of neural development that occur naturally and that are likely to be critical to safe driving performance. Psychologically, these include enhancing risk perception (e.g., by improving scanning techniques through simulator training), teaching impulse control (e.g., using mentoring), and moderating risk-taking behaviors (e.g., through exposure to road injury victims and training in judging probabilities of harm). These strategies, which might either be incorporated within standard novice driver training or delivered as additional modules, would need to be complemented by appropriate regulatory controls. These might include limiting peer passenger numbers, being accompanied by an experienced older driver, zero tolerance for alcohol and other drugs, nighttime driving restrictions, and

Key Variables to Understand in Traffic Psychology

eliminating in-vehicle distractions, particularly cell phones. These restrictions (other than cell phone use, which should be banned during driving) could be gradually relaxed between the ages of 22 and 25 years, after which the emerging adult could be eligible for an unrestricted driver’s license. If the scientific evidence becomes overwhelming in terms of enhanced driving safety for introducing further aspects of graduated driver licensing programs, then gradual introduction of measures based on the research could gain wider social acceptance, as happened with the generic safety benefits of seat belt use and enforcement of blood alcohol limits. Table 9.1 summarizes key neuroscience research findings to date, potential driving behavior outcomes, and possible ameliorative strategies, many of which are already used or under consideration in a number of jurisdictions. The suggested ameliorative strategies are broadly

TABLE 9.1 Summary of Key Developmental Neuroscience Findings, Potential Effects on Driving, and Possible Ameliorative Strategies Neuroscience findings on the adolescent developing brain

Potential cognitive/behavioral consequences for younger drivers

Possible ameliorative/ counter strategies

Shifting balance between limbic and cortical brain areas (e.g., right ventral striatum is less active in teenage brains).

Behaviors likely to be driven by more extreme incentives: reward seeking, novelty seeking, sensation seeking, risk taking, and “recklessness” (e.g., speeding and tailgating).

Provide opportunities for these motivated behaviors in “safe” (e.g., adequately supervised) environments for young drivers. Provide relevant feedback on potential consequences of behaviors to enhance learning about adverse consequences.

Differential development of cortical (e.g., prefrontal) and limbic (e.g., amygdala) systems.

Affects ability to use information to make good (e.g., safe) decisions. Processing critical emotions (e.g., fear) in others to adult levels still developing. Peer influence important up to age 25 years.

Provide opportunities for young drivers to practice using information in typical road environments (e.g., using case studies or simulations). Train young drivers to deal with driving scenarios that they could perceive as threatening. Role play scenarios involving potentially adverse impacts of peer influence.

Cerebellum development.

Some postures/movements could be adversely affected. Although younger drivers may appear to learn many skills rapidly, they remain prone to errors arising from coordination lapses.

Ensure that young drivers can practice in environments that are “forgiving” of postural lapsesdthat is, those that do not result in potentially fatal/other serious injury. Provide adequate feedback on performance.

Pineal gland and melatonin production.

Younger drivers may tend to “eveningness,” displaying a preference for activities late in the daily cycle.

Restricting nighttime driving for younger drivers runs counter to what may be a “natural” preference. Encourage supervised practice under nighttime conditions.

Amygdala and hippocampus development.

Integrating emotions and cognitions occurs over an extended period, which could put young drivers in danger (e.g., arising from an underdeveloped ability to handle stress).

Adopt expectations appropriate to what young drivers are able to undertake, particularly when driving under potentially “stressful” conditions.

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TABLE 9.1 Summary of Key Developmental Neuroscience Findings, Potential Effects on Driving, and Possible Ameliorative Strategiesdcont’d Neuroscience findings on the adolescent developing brain

Potential cognitive/behavioral consequences for younger drivers

Possible ameliorative/ counter strategies

Corpus callosum, which links the right and left brain hemispheres, does not stabilize until early adulthood.

Young drivers who have “mixed handedness” could be particularly liable to adverse effects from hazards involving complex tasks requiring both hands and more than one sense modality.

Limit the range of allowable activities by young drivers, and enforce strict guidelines on what tasks and activities young drivers can safely perform.

Subcortical regions (e.g., nucleus accumbens) mature before and are more active in adolescents prior to decisions involving risk. Lower levels of VPFC activity and white matter linking circuits and later development of these regions.

Bias to immediate rewards over longer term goals. Decisions with possible risk outcomes more likely for younger drivers.

Test for individual differences in risk-taking propensity. Train younger drivers to understand a range of possible negative outcomes of risk taking during driving. Educate young drivers about the benefits of setting and seeking to meet longer term goals, especially relating to the safety of self and significant others.

Prefrontal cortex develops throughout adolescence; specifically, unneeded neuronal pathways are pruned as higher executive areas mature.

Liability to frustration and errorproneness in driving tasks involving decision making, with potential for “irrational” or disorganized thought patterns/behaviors. Response inhibition may be reduced.

Provide adequate support and guidance (e.g., mentoring) for younger drivers, particularly when undertaking maneuvers involving complex decision making.

Balance between white and gray matter in the frontal lobes of the cortex changes, with gray matter reducing in volume and white matter increasing in volume.

White matter is important in the speed and smoothness of information flows.

Young drivers are increasingly able to develop competence in maneuvers and in dealing with road hazards involving information processing. Appropriate cognitive ability and driving skills tests could be used at intervals to assess and provide feedback on this information-processing ability.

Visuospatial functions associated with parietal lobes mature earlier than executive functions of the frontal brain region (cortex).

Hazards may be perceived as in adults, but risk cognition (understanding the nature of hazards and their potential for harm) lags behind.

Training in risk perception is required to enhance young drivers’ understanding of risks associated with particular road hazards. It cannot be assumed that younger drivers have a complete understanding of the risks associated with driving merely because hazards are visible.

Areas of the brain responsible for creating mental imagery (e.g., insula and right fusiform face) are still developing.

Could result in delays in processing critical information about generically dangerous situations; younger drivers take longer to process such information.

Use danger perception scenarios to train younger drivers in particular aspects of driving to develop mental imagery of undesired outcomes. Develop younger drivers’ “metamemory,” for example, by getting them to give a running commentary on road conditions, particularly potential hazards, during supervised practice to enhance alertness and perceptual abilities.

Amygdala develops later in males; males have less brain tissue available to regulate their emotions.

Younger males are more prone to make aggressive responses to a wide range of situations, which could lead to risk taking.

Mentoring and support from older role models, particularly for young male drivers who are prone to aggressive responses, to ensure that they are unlikely to drive when poor control of aggression or risk taking could compromise safety.

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consistent with Steinberg’s (2008b) maxim that efforts to reduce adolescent risk taking should focus on changing the context in which potentially risky behaviors occur rather than seeking to change adolescents’ knowledge and thinking. To provide useful further insights, future neuroscience research should be targeted toward driving behavior, in particular establishing more definitive links between brain development and specific components of driving behavior. To date, approximately 200 neuroscience studies have linked brain function with aspects of driving (Glendon, 2010). Future studies in this field would ideally focus on key implications for driving of adolescent brain development. These should be complemented by a more tailored approach to applications, particularly young driver education and training, that is based on relevant neuroscience research. Of particular relevance would be the study of developmental features in this age group and whether addressing these appropriately could attenuate risk-taking behaviors typically associated with younger drivers.

ACKNOWLEDGMENTS For their perceptive and expert comments on an earlier draft of this chapter, I thank my colleagues Graham Bradley, Trevor Hine, and David Shum as well as the volume editor, Bryan Porter.

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Nagel, B. J., Medina, K. L., Yoshii, J., Schweinsburg, A. D., Moadab, I., & Tapert, S. (2006). Age-related changes in prefrontal white matter volume across adolescence. NeuroReport, 17, 1427e1431. Neyens, D. M., Donmez, B., & Boyle, L. N. (2008). The Iowa graduated driver licensing program: Effectiveness in reducing crashes of teenage drivers. Journal of Safety Research, 39, 383e390. Nopoulos, P., Flaum, M., O’Leary, D., & Andreasen, N. C. (2000). Sexual dimorphism in the human brain: Evaluation of tissue volume, tissue composition and surface anatomy using magnetic resonance imaging. Psychiatry Research: Neuroimaging Section, 98, 1e13. O’Doherty, J. P. (2004). Reward representation and reward-related learning in the human brain: Insights from neuroimaging. Current Opinions in Neurobiology, 14, 769e776. Overman, W. H. (2004). Sex differences in early childhood, adolescence, and adulthood on cognitive tasks that rely on orbital prefrontal cortex. Brain and Cognition, 55, 134e147. Panksepp, J. (1998). Affective neuroscience: The foundation of human and animal emotions. Oxford: Oxford University Press. Panksepp, J. (2001). The neuro-evolutionary cusp between emotions and cognitions: Implications for understanding consciousness and the emergence of a unified mind science. Evolution and Cognition, 7, 141e163. Paus, T. (2005). Mapping brain maturation and cognitive development during adolescence. Trends in Cognitive Sciences, 9, 60e68. Pujol, J., Lo´pez, A., Deus, J., Cardoner, N., Vallejo, J., Capdevila, A., & Paus, T. (2002). Anatomical variability of the anterior cingulate gyrus and basic dimensions of human personality. NeuroImage, 15, 847e855. Reyna, V. F., & Farley, F. (2006). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy. Psychological Science in the Public Interest, 7, 1e44. Santesso, D. L., & Segalowitz, S. J. (2008). Developmental differences in error-related ERPs in middle- to late-adolescent males. Developmental Psychology, 44, 205e217. Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676e679. Shope, J. T., & Molnar, L. J. (2003). Graduated driver licensing in the United States: Evaluation results from the early programs. Journal of Safety Research, 34, 63e69. Silveri, M. M., Rohan, M. L., Pimentel, P. J., Gruber, S. A., Rosso, I. M., & Yurgelun-Todd, D. A. (2006). Sex differences in the relationship between white matter microstructure and impulsivity in adolescents. Magnetic Resonance Imaging, 24, 833e841. Simons-Morton, B. G. (2002). Reducing young driver crash risk. Injury Prevention, 8(Suppl. 2), ii1eii38. Simons-Morton, B. G., Lerner, N., & Singer, J. (2005). The observed effects of teenage passengers on the risky driving behavior of teenage drivers. Accident Analysis and Prevention, 37, 973e982. Smith, S., Horswill, M., Chambers, B., & Wetton, M. (2009). Sleepiness and hazard perception while driving (Road Safety Grant Report No. 2009-001). Canberra: Australian Government, Department of Infrastructure, Transport, Regional Development and Local Government. Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the life span. Nature Neuroscience, 6, 309e315.

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Chapter 10

Neuroscience and Older Drivers Maria T. Schultheis and Kevin J. Manning Drexel University, Philadelphia, PA, USA

1. NEUROSCIENCE AND OLDER DRIVERS Cognitive neuroscience of aging is a multifaceted discipline that encompasses clinical neuropsychology, cognitive neuroscience, and cognitive aging (Cabeza, Nyberg, & Park, 2005; Grady, 2008). Advances in technology, such as structural and functional neuroimaging, have contributed a growing body of literature that has better defined the overall changes in the aging brain. Specifically, research has been extremely informative regarding the age-related differences (i.e., cross-sectional research) and age-related changes (i.e., longitudinal research) in brain structure and function.

1.1. The Effect of Aging on Neuroanatomy and Cognition As individuals age, physiological changes to the brain occur. In general, brain volume is reduced through atrophy and subsequent enlargement of the cerebral ventricles, or ventriculomegaly (Raz, Gunning-Dixon, Head, Dupuis, & Acker, 1998). Specifically, cross-sectional studies of normative cohorts reveal that the majority of brain structures demonstrate reduced volume, including the cerebral gray (i.e., neurons) and white (i.e., axons) matter, as well as subcortical structures, such as the hippocampus and major elements of the basal ganglia (Raz & Rodrigue, 2006). The rate of ventricular expansion and shrinkage of the total brain parenchyma (atrophy) accelerates with age and appears to follow a linear course. The volume of the white matter, especially in the prefrontal regions (Raz et al., 2005), follows a nonlinear longitudinal course, with linear increase until young adulthood, plateau during middle age, and decline in later years. This overall cerebral atrophy of gray and white matter is thought to explain much of the cognitive changes seen in all adults as they age. For example, it is hypothesized that atrophy of the brain’s frontal lobes’ gray matter and its surrounding white matter connections may be the underlying neuropathology of observed mild memory difficulties in aging. Behaviorally, this mild impairment may be most Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10010-4 Copyright Ó 2011 Elsevier Inc. All rights reserved.

apparent on tasks demanding high levels of attention and executive functioning. An overall “slowing” of behavior is a universal description associated with aging (Salthouse, 1996), which has been related to white matter changes (Gunning-Dixon & Raz, 2000) in the brains of healthy adults. In particular, among normal aging adults, atrophy of the frontal lobes has been a robust and consistent finding (Haug & Eggers, 1991; Raz, et al., 1998). Researchers have suggested that this change in frontal lobe physiology may lead to subtle changes in inhibitory control, leading to observed declines in performance on tests of executive function (i.e., problem solving and decision making). In addition to general “executive functioning decline,” working memory, another important cognitive construct associated with the frontal lobes, has also been shown to decline among normal aging adults. Of note, working memory serves as an important link to slowed processing speed (Gunning-Dixon & Raz, 2000). From a broader and simpler perspective, cognitive functions can be conceptualized and categorized into two general aspects: (1) crystallized intelligence, which includes overlearned, familiar skills accumulated through education and practice, and (2) fluid intelligence, which includes nonverbal reasoning, motor tasks, and problemsolving abilities that evolve and change as a result of physiologic maturation. It has been theorized that crystallized abilities, such as knowledge of general facts and vocabulary, sharply increase during the early years of formal education and then stabilize or gradually improve throughout adulthood. By contrast, fluid abilities are theorized to improve throughout childhood and then gradually decline in adult years, with more rapid deterioration in old age due to neuronal loss, changes in physiologic brain function, and increased rates of disease and injury. Research employing both cross-sectional and longitudinal designs has supported the relative stability of verbal abilities with advancing age (i.e., crystallized) and the decline in tasks requiring perceptual speed, selective attention, and complex problem solving (i.e., fluid) (Tucker-Drobb & Salthouse, 2008). 127

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1.2. Age-Related Changes in Cognition Relevant to Driving A clinical priority of research on driving with older adults is the identification of deficits associated with specific diagnosis (e.g., Alzheimer’s disease). However, it can be argued that diagnosis alone does not render an individual unfit to drive. Certainly, the driving of individuals with a neurodegenerative illness should be monitored because their cognitive abilities will change and/or decline to the level that may prohibit driving. However, as evidenced by studies examining healthy older adults, cognitive abilities necessary for safe driving can be disrupted in older adults without dementia as well. In an analysis of moderately cognitively impaired adults with an average age of 76 years (Average Mini-Mental State Exam ¼ 25; range, 14e30), performance on a test of clock drawing highly correlated with total number of driving errors using a driving simulator (r ¼ 0.68) (Freund, Gravenstein, Ferris, Burke, & Shaheen, 2005). The authors hypothesized that this may be “because executive functioning is a critical component of safe driving, in the presence of executive dysfunction, the automatized and procedural skills learned over decades of daily living do not protect the older driver from errors” (p. 243). Hypothetically, subtle executive changes in a more cognitively intact group may show a stronger relationship with cognitively demanding driving tasks. Other authors have reported data suggesting a relationship between executive functioning and driving. Whelihan, DiCarlo, and Paul (2005), using a mixed sample of older adults with questionable dementia and brain injury, found that out of a comprehensive neuropsychological battery, only performance on a maze navigation test, time to complete Trail Making Test Part B, and the Useful Field of View (a measure of visual attention) correlated with driving ability as measured by a road test. Together, these three measures explained 46% of the variance in a total composite of the road test (Whelihan et al., 2005). Ott et al. (2003) found maze performance to be predictive of driving ability; this was the sole measure from a comprehensive battery of tests to be associated with caregiver ratings of driving performance in individuals with Alzheimer’s disease (AD). Finally, Daigneault, Joly, and Frigon (2002) found that older adults with a history of accidents were more impaired on four measures of executive functioning: Trail Making Test, Wisconsin Card Sorting, Stroop Color Word, and Tower of London. Taken together, these findings suggest that normally occurring changes in cognitive status may be relevant to driving performance. In particular, cognitive functions often grouped as “executive functions” consistently appear to be important; these include cognitive domains such as information process speed, working memory, decision

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making, and visual problem solving. Although medically diagnosed older adults may demonstrate more significantly impaired levels of these domains, the fact remains that driving capacity appears to be affected when changes in these domains occur. The increasing amount of evidence supporting the relationship between aging and cognitive deterioration has led to policy discussions about mandatory aged-based and disorder-based assessments. To further explore reception for this, Adler and Rottunda (2010) investigated the attitudes, beliefs, and preferences of older adults, law enforcement officers, and licensing authorities toward reexamination of driving skills for people with AD and Parkinson’s disease (PD) and at varying ages. The results indicated strongest support across all groups for retesting for those with AD but only moderate endorsement of retesting for those with PD. Moderate support was also given for re-testing of 90-year-old drivers, and the least support was given for reassessment of 70-year-old drivers (Adler & Rottunda, 2010).

1.3. What are the Characteristics of Older Drivers? In the literature, driving performances have been defined using a variety of measures, including traffic crashes and the behind-the-wheel (BTW) exam. Population-based studies of driving in older adults have typically used number of traffic crashes as the outcome variable representing driving performance (Langford, 2008). McGwin and Brown (1999) compared the characteristics of crashes among young, middle-aged, and drivers older than age 55 years from all of the police-reported traffic crashes in the state of Alabama during 1996. Compared to young and middle-aged drivers, older drivers were more likely to be involved in crashes at intersections, fail to yield the right of way, and fail to heed stop signs or signals. Crashes occurring while turning and changing lanes were also more common among older drivers. By contrast, older drivers were less likely to have crashes during adverse weather and while traveling at high speeds. Other studies have reported similar crash characteristics among older adults (Cooper, 1990; Hakamies-Blomqvist, 1993; Ryan, Legge, & Rosman, 1998). A plethora of research demonstrates the significant association between crash risk in older adults and cognitive test performance on tests of attention, memory, executive functioning, and processing speed (Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Lafont, Laumon, Helmer, Dartigues, & Fabrigoule, 2008; Staplin, Gish, & Wagner, 2003; Stutts, Stewart, & Martell, 1998). Despite statistical significance, the clinical significance of the association between cognitive test performance and crash risk is poor. For example, Carr, Duchek, and Morris (2000) compared

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63 healthy older adults and 58 individuals with clinically diagnosed AD and found no group difference in the number of accidents 5 years prior to the onset of the study. In other words, despite impairment in various domains of functional capacity severe enough to warrant a diagnosis of AD, adults with the disease would show no differences in functional capacity if functional capacity were measured using only driving accidents. The point is that although the statistical association between cognitive test performance and crash risk may be beyond a chance possibility, it provides little clinical value in determining individuals who should limit or relinquish their driving privileges (Bedard, Weaver, Darzins, & Porter, 2008). This is likely due to the rarity of accidents and the fact that they represent a heterogeneous collection of incidents occurring in a multitude of circumstances (McGwin & Brown, 1999). More sensitive measures of driving capacity are needed to distinguish older adults with obvious gross functional and cognitive impairment from healthy controls. Currently, the clinical “gold standard” of driving ability is the BTW driving evaluation. The BTW consists of route following, or the examiner directing the examinee where to drive next, and is similar to the “road test” that most individuals undergo to receive their driver’s license. However, when considering its utility as an outcome measure, similar to “number of traffic crashes,” the BTW lacks sensitivity to detect subtle changes in driving performance. Kay, Bundy, Clemson, and Jolly (2008) made this point in their investigation of the psychometric properties of a standardized BTW with 100 cognitively intact older drivers aged 60e86 years. Although the authors found total driving errors and overall ratings of performance (i.e., pass/fail) to be valid and reliable indicators of driving safety, these scoring systems were not sensitive enough to determine different levels of driving ability or even discriminate “safe” versus “unsafe” drivers (Kay et al., 2008). Similar findings have been found for older adults with dementia. Ott et al. (2008) conducted a longitudinal study of drivers with AD spanning 3 years using the BTW. Greater severity of dementia, increased age, and lower education were associated with higher rates of BTW failure at follow-up. However, only 22% of individuals with mild AD failed the BTW at followup. The failure rate was even less in the group of individuals considered to have questionable dementia or mild cognitive impairment. On a practical level, in an attempt to harness this research information for clinical applications, the American Medical Association (2010) compiled physician guidelines for the assessment and counseling of older drivers. These guidelines include recommendations for specific tests for the Assessment of Driving-Related Skills (ADReS), including visual measures (i.e., visual acuity and visual fields) and basic cognition (Trail Making A and B Test and Clock Drawing Test).

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2. MEDICAL ISSUES AND OLDER DRIVERS 2.1. Driving and the Dementias Without doubt, the dementing conditions are among the most problematic faced by aging drivers. In addition to affecting cognitive and visuomotor abilities that can impact everyday activities such as driving, the dementing illnesses can also deprive individuals of the judgment and insight needed to accurately assess their own declining abilities and increase risk due to these deficits. At the same time, many dementing conditions are progressive and tend to be insidious, making detection more difficult for patients, families, and health care professionals. Alzheimer’s disease is the most common cause of dementia and the sixth leading cause of death. Women are more likely than men to have AD, and it has been estimated to affect 14% of all people aged 71 years or older. AD is a steadily progressive disorder that is characterized by a variety of cognitive function abnormalities. By definition, diagnosis of dementia requires the objective measurement of impairments in memory and one other cognitive domain that is resulting in a negative impact on occupational or social functioning (American Psychiatric Association, 2000). One of the common activities of daily functioning that is included is driving an automobile. Competence in driving a motor vehicle has implications both for the safety of the individual affected by dementia and for other road users. Therefore, it is not uncommon for health care providers to have to determine whether individuals with dementia are able to continue to drive and/or when they should stop driving. Retrospective surveys concerning driving and dementia suggest that many patients diagnosed with dementia do continue to drive and may be reluctant to give up driving (Friedland et al., 1988; Gilley et al., 1991). In 2000, Dubinsky, Stein, and Lyons reported an eightfold increase in the crash rate for a group with AD, implying a greater risk of crashes for drivers with AD compared to other drivers. It is also noteworthy that two early retrospective studies found that only 50% of drivers with AD had ceased driving within a 3-year period of the onset of dementia (Drachman & Swearer, 1993; Friedland et al., 1988), after which time crash risk increases substantially. Tuokko, Tallman, Beattie, Cooper, and Weir (1995) examined driving records (insurance claims) of 165 drivers with dementia and found that they had an approximately 2.5 times higher crash rate than that of the matched control sample. In contrast, in a study using state records, road crash and violation rates among AD patients did not differ significantly from those of matched controls (Trobe, Waller, Cook-Flannagan, Teshima, & Bieliauskas, 1996). However, this study did not control for mileage driven, and reduced driving exposure of AD patients may be the reason why their crash rate was equal to that of control subjects. Carr and

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colleagues (2000) reported that a sample of 63 drivers with very mild or mild Clinical Dementia Rating (Hughes, Berg, Danziger, Coben, & Martin, 1982) showed no difference in state recorded crash rate for the previous 5-year period compared to nondemented, older control drivers, even after adjusting for exposure. Carr et al. noted that the drivers with dementia in their study may have been only mildly impaired in their driving skills, with little, if any, impairment in driving skills evident in the preceding 5-year period. It has been recommended that limitation of driving privileges should be based on demonstration of impaired driving competence rather than on a clinical diagnosis such as AD. As early as 1988, Drachman and colleagues argued that individuals with an AD diagnosis should not be excluded from driving based on the possibility of minimal functional decline in early AD. They also suggested that the likelihood of the loss of driving privileges may result in many people with mild or potentially treatable cognitive impairments refraining from seeking medical advice about continuing to drive. O’Neill et al. (1992) based their argument on findings of studies that demonstrated that a substantial percentage of patients with AD at the time of driving assessment had suffered no deterioration in driving skills, thus supporting the view that a diagnosis of AD alone is not sufficient to preclude driving. Indeed, researchers continue to attempt to define the rate of AD progression and nature of disease manifestation, particularly in the earlier stages; therefore, using a diagnosis of AD as a basis for a decision regarding driving is not recommended. In 2000, the American Academy of Neurology published a practice parameter regarding AD and driving (Dubinsky et al., 2000). Two recommendations were made. First, drivers with AD who have a Clinical Dementia Rating (CDR) of 1.0 or greater should not drive because of driving performance errors and a substantially increased accident rate. Second, drivers with possible AD with a severity of CDR of 0.5 should be considered for referral for driving performance evaluation. Furthermore, because of the high likelihood of disease progression, it was recommended that dementia severity and appropriateness of continued driving be reassessed every 6 months. This follow-up recommendation was evaluated in a prospective longitudinal study of 58 healthy controls, 21 individuals with very mild AD, and 29 individuals with mild AD. In this study, participants underwent a standardized on-road test approximately every 6 months for a 3-year period. Analysis of the survival curves generated for each group supported the recommendation to conduct driver evaluations every 6 months for people with very mild and mild dementia of the Alzheimer’s type (Duchek et al., 2003). Although helpful, there remains limited follow-up to the application of these initial guidelines, and additional longitudinal studies are needed to better describe the progression of AD and its subsequent effect on driving ability.

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A 2010 update of these practice parameters attempted to provide more specific guidelines for clinicians (Iverson et al., 2010). The authors recommended that for individuals with dementia, (1) consideration of the CDR scale, (2) a caregiver’s rating of a patient’s driving ability as marginal or unsafe, (3) a history of crashes or traffic citations, (4) reduced driving mileage or self-reported situational avoidance, (5) Mini-Mental State Examination scores of 24 or less, and (6) aggressive or impulsive personality characteristics are useful for identifying patients at increased risk for unsafe driving. Although informative, the study excluded much of the work with neuropsychological testing and subsequently did not support or refute the contributions of cognitive testingdan important limitation that minimizes the relevance of cognitive status in this population. Although these studies serve to provide clinicians with some guidelines, potential difficulties with implementation of these parameters have been noted. For example, the AD patient or his or her family may not accept the physician’s recommendation for discontinuation of driving. Thus, although physicians have a significant responsibility to determine “medical” competence to drive, in practice such a clinical decision is difficult because of lack of standards and effective guidelines.

2.2. Other Dementias Other less frequently occurring dementias include illnesses such as PD and Huntington’s disease (HD). Much less is known about the relationship between these disorders and driving capacity. However, given some commonality (particularly in the cognitive domains), the use of similar strategies for evaluating driving that are employed for AD have been recommended.

2.2.1. Parkinson’s Disease This common disease, known since ancient times, was first clinically described by James Parkinson in 1817. The disease generally begins at 40e70 years of age, with the peak age of onset in the sixth decade. It is infrequent before 30 years of age, and most series contain a somewhat larger proportion of men. The core syndrome is one of expressionless face, poverty and slowness of voluntary movement, “resting” tremor, stooped posture, axial instability, rigidity, and festinating gait. Although most are familiar with the motor effects of the disease, cognitive decline may also be seen. Patients therefore not only experience a progressive loss of motor control but also eventually are at risk for cognitive and emotional deterioration. Cognitive symptoms include slowed information processing, executive dysfunction, memory loss, and associated personality changes (Aarsland, Bronnick, Larsen, Tysnes, & Alves, 2009; Rodriguez-Oroz et al., 2009).

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Work by Uc and colleagues has provided a greater understanding of the driving ability of individuals with PD (Rizzo, Uc, Dawson, Anderson, & Rodnitzky, 2010). In 2009, Uc and colleagues compared the performance of licensed drivers with PD with that of an age-matched control group. They found that overall, drivers with PD had poorer road safety compared to controls, but there was considerable variability among the drivers with PD, and some performed normally. Familiarity with the driving environment was a mitigating factor against unsafe driving in PD. Impairments in visual perception and cognition (attention, visuospatial, and visual memory) were associated with road safety errors in drivers with PD (Uc, Rizzo, Johnson, et al., 2009). Another study examining driving ability found that drivers with PD made more safety errors than did neurologically normal drivers during a route-following task. The authors concluded that the PD group driver safety was degraded due to an increase in the cognitive load in patients with limited reserves. Driving errors and lower driver safety were also associated more with impairments in cognitive and visual function than with the motor severity of the disease in drivers with PD (Uc et al., 2006). This group of researchers also employed the use of driving simulation to better delineate the driving errors seen in PD. Using this methodology, they concluded that under low-contrast visibility conditions, drivers with PD had poorer vehicle control and were at higher risk for crashes (Uc, Rizzo, Anderson, et al., 2009), and that the quantitative effect of an auditoryeverbal distracter task on driving performance was not significantly different between PD and control groups. However, a significantly larger subset of drivers with PD had worsening of their driving safety errors during distraction (Uc et al., 2006). Across the various studies, cognitive predictors of driving performance included visual processing speed and attention, motion perception, contrast sensitivity, visuospatial construction, motor speed, and Activities of Daily Living score.

2.2.2. Huntington’s Disease This disease, distinguished by the triad of dominant inheritance, choreoathetosis, and dementia, derives its eponym from George Huntington (1872). Although relatively rare, in university hospital centers this is one of the most frequently observed types of hereditary nervous system disease. The usual age of onset is in the fourth and fifth decades, but 3e5% of cases begin before the 15th year and some even in childhood. In 28% of cases, symptoms become apparent after 50 years. The progression of the disease is slower in older patients. Once begun, the disease progresses relentlessly. The personality and psychiatric changes associated with HD assume several subtle forms long before the deterioration of cognitive functions becomes evident. In approximately

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half of the cases, alterations of character are the first symptoms. Patients begin to find fault with everything; they may be suspicious, irritable, impulsive, eccentric, untidy, or excessively religious; or they may exhibit a false sense of superiority. Poor self-control may be reflected in outbursts of temper, fits of despondency, alcoholism, or sexual promiscuity. Disturbances of mood, particularly depression, are common and may constitute the most prominent symptoms early in the disease. Eventually, other cognitive functions deteriorate, and the patient becomes less communicative and more socially withdrawn. Diminished work performance, inability to manage household responsibilities, disturbances of sleep, difficulty in maintaining attention, impaired concentration, deficits in learning new material, and mental rigidity become apparent, along with loss of fine manual skills. Because memory performance benefits from cues to help with retrieval of information, HD has been characterized as a “subcortical dementia.” Increased deterioration of motor functions and chorea (a relatively ceaseless occurrence of a wide variety of rapid, highly complex, jerky movements that appear to be coordinated but are in fact involuntary) usually follow. To date, only one study has empirically assessed the influence of the neurological and cognitive impairments of HD on automobile driving (Rebok, Bylsma, Keyl, Brandt, & Folstein, 1995). These authors found that HD patients performed significantly worse than control subjects on driving-simulator tasks and were more likely to have been involved in a collision in the preceding 2 years (58% of HD patients vs. 11% of control subjects). Patients with collisions were less functionally impaired but had slower simple reaction time scores than did those without collisions. Although additional research is needed, to date there is a presumption that such patients will eventually cease driving as this terminal disease progresses. It is remarkable that other systematic studies examining driving performance in this population are lacking, despite the fact that this is a progressive disease with known cognitive impairments. In particular, difficulties with divided attention, executive functioning, and awareness have all been identified as potential cognitive contributors to driving difficulties in this population. As is the case with other dementias, there is no uniform national law about driving with HD, but several support organizations directly address the topic of driving and offer recommendations for modifying driving behaviors (http://hopes.stanford.edu/ n3547/managing-hd/lifestyle-and-hd/driving-andhuntingtons-disease).

2.3. Cerebral Vascular Accidents or Stroke Cerebral vascular accidents or strokes are the third leading cause of death in the United States. By definition, a stroke

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occurs as a result of blockage or hemorrhage of a blood vessel leading to the brain. The resulting lack of oxygen supply to the brain results in damage that can manifest in a variety of physical, cognitive, and behavioral deficits for the individual. Common difficulties can include hemiparegia or paralysis of upper and lower extremities, speech difficulties, visual perception and visual spatial difficulties, and changes in cognition (e.g., memory and attention). Not surprisingly, these deficits can have a significant impact on an individual’s activities of daily living and overall quality of life. Given the high value placed on individual transportation in the United States, it is not surprising that many individuals seek to return to driving after experiencing a stroke. In fact, it is estimated that approximately 30e50% of stroke survivors return to driving (Fisk, Owsley, & Mennemeier, 2002; Fisk, Owsley, & Pulley, 1997; Heikkila, Korpelainen, Turkka, Kallanranta, & Summala, 1999). However, it has also been reported that many stroke survivors do not go through any formal evaluation of their driving ability or receive advice before returning to the road. Therefore, the challenge remains in determining how the various sensorimotor and cognitive impairments resulting from stroke may or may not impact the individual’s performance on the road. To date, although no single measurement can be used to definitively calculate an individual’s driving capacity, much has been learned about driving after stroke. It is well documented that stroke can result from different etiologies and can present in significantly varying degrees of severity. As a result, stroke is a major cause of disability, affecting approximately 500,000 individuals annually. Although the highest incidence is reported in older adults, work has identified an increasing number of younger adults who suffer from strokes (Bjorkdahl & Sunnerhagen, 2007). Given this fact, it is not surprising that stroke survivors (both young and older) find that driving cessation interferes with activities related to independent living (e.g., working) and consider the resumption of driving after stroke an important step in their recovery. Long-standing evidence supporting this finding first came from studies that demonstrated that stroke survivors who did not resume driving participated in fewer social activities and were more likely to be depressed (Legh-Smith, Wade, & Hewer, 1986). In addition, one study that focused on driving resumption after mild stroke found that 50% of individuals returned to driving within the first month after experiencing a stroke (Lee, Tracy, Bohannon, & Ahlquist, 2003), further underscoring the need for early assessment. Indeed, accuracy in measuring driving safety after stroke is crucial for ensuring that individuals who are safe drivers are not prevented from maintaining their independent mode of transportation and for preventing individuals who are unsafe drivers from posing a danger to themselves and others. Several studies have documented that of individuals who drove before their stroke, approximately 30e59%

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return to driving after their stroke (Fisk et al., 1997; Heikkila et al., 1999). Of those individuals returning to driving, almost one-third report high driving exposure, driving 6 or 7 days per week and/or 100e200 miles per week (Fisk et al., 1997). However, other findings indicate that stroke survivors drive less compared to a nonstroke cohort (Fisk et al., 2002). Specifically, although no differences in days per week of driving were seen, nonstroke drivers drove to more places, took more trips, and drove more miles (Fisk et al., 2002). Drivers who returned to driving also acknowledged difficulties in varying driving situations, such as making left turns, driving on the interstate, and driving in heavy traffic. Despite this, the stroke drivers did not differ from the nonstroke drivers in occurrences of self-reported crashes or citations (Fisk et al., 2002). Overall, stroke drivers appear to be self-regulating their driving behaviors and exposure.

2.3.1. Right Versus Left One of the most common areas of stroke research is evaluating differences in impairment resulting from strokes in the two hemispheres. Several studies have examined the lesion location and the extent of brain damage incurred to better determine the impact of the resulting impairments on driving performance. Cortical damage in the area of the temporoparietal lobe of the right hemisphere often results in impairments in spatial and perceptual abilities and also attentional and visual skills deficits such as visual neglect. Physically, a right-hemisphere stroke can often lead to paralysis of the left side of the body, known as left hemiplegia. In contrast, cortical damage to the left hemisphere often results in language and speech difficulties and paralysis of the right side of the body, known as right hemiplegia. More global cognitive deficits, such as changes in memory and attention, cannot be exclusively associated with one or the other hemisphere. In relation to driving difficulties, several studies have indicated poorer performance in individuals who have sustained a right-hemisphere stroke (Fisk et al., 2002; Korner-Bitensky et al., 2000; Quigley & DeLisa, 1983). These researchers have noted the impact of visual spatial and perceptual deficits on driving capacity. Although physical impairments can lead to problems with motor reaction time, which can be crucial in driving (e.g., braking) and safe maneuvering (e.g., steering), in many cases, adaptive driving equipment can be used to minimize the impact of physical limitations. For example, an adaptive spinner knob can be attached to the steering wheel to allow controlled steering with the use of only one hand, or a left-foot gas or pedal may be used if the individual is unable to use his or her right foot to push the accelerator or brake. In fact, Smith-Arena, Edelstein, and Rabadi (2006) found that individuals in an acute rehabilitation setting with higher Motricity Index scores and intact

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visual fields were more likely to pass an in-clinic driver evaluation. The researchers concluded that physicians could safely identify post-stroke patients most appropriate for driver evaluation when mild physical impairments, normal visual fields, and mild cognitive impairments were present. In summary, although physical challenges resulting from stroke can impact driving performance, cognitive and visual impairments pose a greater challenge for returning to driving.

2.3.2. Cognition and Perception Stroke can produce a variety of cognitive difficulties that can affect an individual’s ability to return to driving, including slowed information processing speed, visuospatial and perceptual deficits, visual inattention, decreased ability to concentrate, and reasoning difficulties. As a result, cognition and driving after stroke has been extensively studied, with the main goals being to better define this relationship and to identify potential cognitive predictors of driving performance. To date, research has not defined a specific cognitive impairment pattern that is predictive of driving performance, but the results of these studies have identified specific cognitive domains relevant to driving, and some new computerized and noncomputerized driving assessment tasks designed to assess the cognitive domains of driving after stroke have been generated. One of the most common problems associated with stroke and a cognitive domain identified early on as relevant to driving concerns perceptual abilities (Quigley & DeLisa, 1983; Sivak, Olson, Kewman, Won, & Henson, 1981). Early studies examining perceptual/cognitive abilities among right- and left-hemisphere stroke survivors indicated that individuals with right-hemisphere strokes demonstrated the most severe perceptual difficulties. Of those who returned to driving, when self-reported traffic difficulties (e.g., accident involvement) were examined 1 year later, the predictive validity of the perceptual assessment procedure held true for approximately 80% of the sample (Simms, 1985) . Another study used a factor analysis approach to better define the perceptual/cognitive constructs of driving performance by conducting a comprehensive neuropsychological battery on 72 consecutively referred patients who had suffered a stroke (Sundet, Goffeng, & Hofft, 1995). The test battery was factor analyzed into four valid principal components: visual perception, spatial attention, visuospatial processing, and language/praxis. The researchers reported greater visual neglect in right-hemisphere strokes compared to left-hemispheres strokes, but they did not find overall group differences in the number of patients denied driving after a stroke. They concluded that in addition to hemianopia, measures of neglect, speed of mental processing, and emotional disturbances such as denial of illness

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were the most potent subject characteristics in assessing patients for return to driving (Sundet et al., 1995). Mazer, Korner-Bitensky, and Sofer (1998) examined the use of perceptual tests to predict driving performance in individuals with stroke. Driving performance was quantified as pass or fail outcome of an on-road driving evaluation that was conducted by an occupational therapist and was based on observed driving behaviors. Their results indicated that a test of visual perception skills (Motor Free Visual Perception Test (MVPT)) was the most predictive of on-road performance (positive predictive value ¼ 86.1%; negative predictive value ¼ 58.3%), and that the combination of the MVPT and a measure of task switching (Trail Making Test Part B) represented the most predictive and parsimonious model for predicting on-road performance. Other researchers have reported that a neuropsychological assessment including tests measuring dynamic cognitive processing and complex speed can be useful in assessing driving skills after stroke. For example, Lundqvist, Gerdle, and Ronnberg (2000) reported that complex reaction time the Stroop Color and Word Test, the Listening Span task, and a computerized administration of the K-test were most associated with driving skills, as defined by both on-road and simulated driving performance. Similarly, other researchers have found that although the MVPT is believed to be a strong predictor of on-road evaluation failure, its predictive validity is not sufficiently high to warrant its use as the sole screening tool in identifying those who are unfit to undergo an on-road evaluation (Korner-Bitensky et al., 2000). As the challenge of determining driving capacity following a stroke has been acknowledged, it is not uncommon for many settings to rely on a team of clinicians who evaluate varying aspects of an individual’s ability (e.g., medical and cognitive). One retrospective study, which attempted to better define the contributing factors to a team’s decision on driving ability, examined 104 individuals who had suffered a first stroke (Akinwuntan et al., 2002). The researchers administered both a comprehensive predriving assessment and an on-road test. The predriving assessment included specific measures of vision (monocular vision, binocular vision, stereoscopy, and kinetic vision) and a neuropsychological assessment consisting of eight different tests: the Rey Complex Figure Test, UFOV, divide attention, flexibility, visual scanning, incompatibility, visual field, and neglect (Akinwuntan et al., 2002). Using logistic regression, the researchers found that a model including the side of lesion, kinetic vision, visual scanning, and a road test was the predictor of the team decision; within this model, the road test was the most important determinant. A combination of visual acuity and the Rey Figure test was the best subset for predicting on-road test performance (Akinwuntan et al., 2002). In follow-up prospective studies, these researchers found that a combination of visual neglect,

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Rey Figure, and on-road test was the best predictor of fitness to drive (as defined by clinicians’ ratings) (Akinwuntan et al., 2006). The accuracy of this short battery was confirmed in another study demonstrating an 86% predictive value of these three tests, which are both sensitive (77%) and specific (92%) in their prediction (Akinwuntan et al., 2007). Taken together, these studies clearly indicate that the determination of driving after stroke cannot be limited to a single cognitive domain.

2.3.3. Aging and Stroke In addition to coping with residual deficits of a stroke, many older adults must cope with ongoing cognitive and physical changes that are commonly seen in aging adults, such as decreased physical mobility, changes in vision, and changes in cognition (e.g., memory problems). Older individuals are also at risk for other neurological involvement; they may be at risk for additional strokes, other cardiovascular disorders, and/or injuries.

3. OTHER CONSIDERATIONS FOR OLDER DRIVERS 3.1. Medication A peripheral challenge in considering the driving ability of older adults is the issue of polypharmacy or the use of multiple types of medications. Not surprisingly, as individuals age, there are higher risks for multiple types of medical issues, including systemic (e.g., diabetes), focal (e.g., cardiac and stroke), or emotional (e.g., depression). Treatment of these often coexisting diagnoses often results in the individual taking multiple medications. Subsequently, the older adult is at risk for taking concomitant medications, which can lead to serious drug interactions.

3.1.1. Emotional Impairment and SelfAwareness Individuals who are unable to drive may suffer increased isolation, which may contribute to depression (Martolli et al., 2000). In addition to the physical limitations that can affect one’s life after stroke, one’s professional and personal lives are also tested and stressed. Many people who have suffered from stroke are unable to return to work in the same capacity as that prior to their disability. The disability not only affects the inflicted person but also the person’s close inner circle. For example, there is more dependence on spouses/family members/close friends for basic everyday activities, such as eating, personal hygiene, and dressing. It is also well-known that stroke patients may have problems recognizing their own cognitive or psychomotor disorders, and they may have serious impairment of functions

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that are crucial for safe driving. Particularly, damage to the nondominant hemisphere often causes anosognogia and neglect syndrome and, hence, lowered awareness. Heikkila et al. (1999) found that both patients and their spouses demonstrated a clear tendency to overestimate driving ability compared to the estimates of the neurologist and psychologist.

3.1.2. Education (for the Patient and for the Clinician) As reported in even the earliest study by Quigley and DeLisa (1983), part of the rehabilitation team’s efforts is directed toward providing the candidate with information about the policies and restrictions of the department of motor vehicles. However, Kelly, Warke, and Steele (1999), who investigated the awareness of patients and doctors of medical restrictions to driving, found that educating the client may be difficult. In addition to patients having difficulty knowing if they should drive based on their medical condition, Kelly et al. found that doctors had very poor knowledge of the current licensing policy and action to be taken if a patient was not eligible to drive. Medical staff does not seem to be able to provide this guidance. To increase doctors’ awareness of medical restrictions to driving, greater emphasis must be placed on this aspect of patient care during both undergraduate and postgraduate training (Kelly et al., 1999). With the growing need to improve older adult driving safety, different training strategies are beginning to emerge that focus on changing driving behaviors and knowledge. In 2007, Tuokko, McGee, Gabriel, and Rhodes examined the perceptions of risk, beliefs and attitudes, and openness to change of 86 older participants who voluntarily attended a driver education program. The authors reported that most people attending these sessions were not necessarily concerned about their own driving, safety, or abilities but were interested in maintaining mobility. They were conservative and reasonably consistent in their attitudes toward traffic regulations and safe driving practices. Some gender differences emerged, with more men than women being resistant to changing their driving habits and reporting that they drive after consuming alcohol, and more women than men identifying a role for their families in decision making regarding driving cessation. This suggests that educational material may need to be targeted differently for men and women and that psychosocial factors related to driving, such as driver perception, beliefs, and openness to change, will be useful for maximizing the fit between education program content and outcomes (Tuokko et al., 2007).

4. CONCLUSION This chapter provided an introduction to the current literature on older drivers with and without neurological compromise.

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Neuroscience and Older Drivers

A main focus was providing information relevant to common clinical diagnoses in older drivers (e.g., dementia and stroke). Although much has been accomplished in this area, much work is needed. In particular, as new technologies (e.g., functional neuroimaging) provide greater insight into the neuroanatomy of aging and neurological compromises, we will be better able to understand the relationship between behavior and brain changes in older drivers. Given the growing number of older adults (and the anticipated continued growth), the need to increase our knowledge is clear. The ability to drive is often synonymous with autonomy, and older drivers in our fast-paced society will likely continue to have a high reliance on automobiles in order to maintain their independence in their communities. The challenge posed to clinicians and driving experts is the ability to balance between an individual’s autonomy and safety (both for the individual and for others). Given the known consequences of neurological compromise, the challenge that remains before us is to identify and refine the best methods to accurately make driving recommendations. In doing so, we can keep our roads and older drivers safe.

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Rebok, G. W., Bylsma, F. W., Keyl, P. M., Brandt, J., & Folstein, S. E. (1995). Automobile driving in Huntington’s disease. Movement Disorders, 10, 778e787. Rizzo, M., Uc, E. Y., Dawson, J., Anderson, S., & Rodnitzky, R. (2010). Driving difficulties in Parkinson’s disease. Movement Disorders, 25(Suppl. 1), S136eS140. Rodriguez-Oroz, M. C., Jahanshahi, M., Krack, P., Litvan, I., Macias, R., Bezard, E., et al. (2009). Initial clinical manifestations of Parkinson’s disease: Features and pathophysiological mechanisms. Lancet Neurology, 8, 1128e1139. Ryan, G. A., Legge, M., & Rosman, D. (1998). Age related changes in drivers’ crash risk and crash type. Accident Analysis and Prevention, 30, 379e387. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403e428. Simms, B. (1985). The assessment of the disabled for driving: A preliminary report. International Rehabilitation Medicine, 7, 187e192. Sivak, M., Olson, P. L., Kewman, D. G., Won, H., & Henson, D. L. (1981). Driving and perceptual/cognitive skills: Behavioral consequences of brain damage. Archives of Physical Medicine and Rehabilitation, 62, 476e483. Smith-Arena, L., Edelstein, L., & Rabadi, M. H. (2006). Predictors of a successful driver evaluation in stroke patients after discharge based on an acute rehabilitation hospital evaluation. Archives of Physical Medicine and Rehabilitation, 85, 44e52. Staplin, L., Gish, K. W., & Wagner, E. K. (2003). MaryPODS revisited: Updated crash analysis and implications for screening program implementation. Journal of Safety Research, 34, 389e397. Stutts, J. C., Stewart, J. R., & Martell, C. (1998). Cognitive test performance and crash risk in an older driver population. Accident Analysis and Prevention, 30, 337e346. Sundet, K., Goffeng, L., & Hofft, E. (1995). To drive or not to drive: Neuropsychological assessment for driver’s license among stroke patients. Scandinavian Journal of Psychology, 36, 47e58. Trobe, J. D., Waller, P. F., Cook-Flannagan, C. A., Teshima, S. M., & Bieliauskas, L. A. (1996). Crashes and violations among drivers with Alzheimer disease. Archives of Neurology, 53, 411e416. Tucker-Drobb, E. M., & Salthouse, T. (2008). Adult age trends in the relations among cognitive abilities. Psychology of Aging, 23(2), 453e460. Tuokko, H., Tallman, K., Beattie, B. L., Cooper, P., & Weir, J. (1995). An examination of driving records in a dementia clinic. Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 50, S173eS181. Tuokko, H. A., McGee, P., Gabriel, G., & Rhodes, R. E. (2007). Perception, attitudes and beliefs, and openness to change: Implications for older driver education. Accident Analysis and Prevention, 39, 812e817. Uc, E. Y., Rizzo, M., Anderson, S. W., Dastrup, E., Sparks, J. D., & Dawson, J. D. (2009). Driving under low-contrast visibility conditions in Parkinson disease. Neurology, 73, 1103e1110. Uc, E. Y., Rizzo, M., Anderson, S. W., Sparks, J. D., Rodnitzky, R. L., & Dawson, J. D. (2006). Driving with distraction in Parkinson disease. Neurology, 67, 1774e1780. Uc, E. Y., Rizzo, M., Johnson, A. M., Dastrup, E., Anderson, S. W., & Dawson, J. D. (2009). Road safety in drivers with Parkinson disease. Neurology, 73, 2112e2119. Whelihan, W. M., DiCarlo, M. A., & Paul, R. H. (2005). The relationship of neuropsychological functioning to driving competence in older persons with early cognitive decline. Archives of Clinical Neuropsychology, 20, 217e228.

Chapter 11

Visual Attention While Driving Measures of Eye Movements Used in Driving Research David Crundall and Geoffrey Underwood University of Nottingham, Nottingham, UK

1. INTRODUCTION Van Gompel, Fischer, Murray, and Hill (2007) introduced their book on eye movements with a historic perspective on the idea that the eyes provide access to the inner workings of the mind and brain. They quote De Laurens (1596) as referring to the eyes as “windowes (sic) of the mind” (p. 3), which presents an opportunity to indirectly observe what is being processed in the brain on the basis of what the eye is looking at. This link between the location of the eye in the visual world and the concomitant processing in the brain is most formally stated in Just and Carpenter’s (1980) eyeemind assumption, which states that the eye remains fixated on an object until the brain has finished processing it. Various ancillary assumptions can be appended to this, such as the argument that the brain should not be processing any visual information that the eye is not looking at, and that whenever the eye is fixating something, that particular object must be being processed. If these assumptions are met, it is easy to see how valuable it would be for a psychologist to monitor the eye movements of individuals engaging in various tasks, including driving, which is predominantly dependent on the processing of visual information. Unfortunately, the story is not so simple. Numerous studies demonstrate the ability of readers to process words parafoveallydthat is, to process them without looking at them directly (Underwood & Everatt, 1992). Conversely, there is evidence that looking directly at an object or area of a scene does not guarantee that the viewer will process the information. Studies of change blindness have demonstrated that viewers may not notice a change made to an object in a visual scene even though they are looking at the object when the change occurs (Caplovitz, Fendrich, & Hughes, 2008). Indeed, whenever the mind wanders while reading a book, it is common to feel that one has read a sentence without actually processing what it meant, and drivers sometimes report not being aware of familiar sections of roadway that they have successfully negotiated. Despite the refutation of the strong version of the Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10011-6 Copyright Ó 2011 Elsevier Inc. All rights reserved.

eyeemind assumption, the link between what the eye is looking at and what the viewer is thinking about is still very robust. Although Underwood and Everatt (1992) have presented several challenges to the eyeemind assumption, they acknowledge that these are all special cases in which the assumption can be shown to fail, and in general it is a safe working assumption that if someone is looking at something, then he or she is processing it. Modern reviews of eye tracking (Van Gompel et al., 2007) have demonstrated the appeal of this methodology as a means of better understanding how people approach and engage in a variety of tasks and situations. We believe that the driving task is eminently suited to the application of eye tracking methodologies. The information that a driver uses is predominantly visual (Sivak, 1996), and a wide range of specific driving behaviors, from navigation to anticipation of hazardous events, are primarily dependent on the optimum deployment of attention through overt eye movements. Classic studies of road collision statistics have identified perceptual problems to be a leading cause of traffic crashes (Lestina & Miller, 1994; Sabey & Staughton, 1975; Treat et al.,1979), and in-car observation of driver behavior preceding an actual crash supports the causal role of distraction and inattention (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). Several reviews of driving research have all reached the same conclusion: When and where drivers look is of vital importance to driver safety (Lee, 2008; Underwood, 2007), and we need to record and interpret these eye movements in order to decrease death and injury on our roads (Shinar, 2008). Recording and interpreting eye movements has indeed been a valuable tool in driving research during the past 40 years, and we review much of the findings in this chapter. This methodology is likely to become even more important in the future with the development of transport simulators that allow the recording of eye movements in near-naturalistic situations while maintaining a high degree of experimental control over the environment. Considering 137

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the interest that has been and will be shown in recording drivers’ eye movements, we believe that it is important to provide a review of the various measures that can be and have been captured in previous studies and to discuss how these different measures can be used to address different hypotheses. This chapter is not concerned with the benefits of one eye tracker over another (for a review of eye tracking methods, see Duchowski (2007)) but is restricted to measures we might record. Essentially, eye movements consist of two primary events: fixations and saccades. Fixations are periods of relative stability, during which the eyes focus on something in the visual scene. Such fixations most often reflect the fact that the brain is processing the fixated information. Saccades are rapid, ballistic jumps of the eye that separate the fixations and serve to orient the focus of the eyes from one point of interest to another. No visual information is taken in during these rapid movements. Although we can reduce eye movements to these two components, there are numerous exceptions and different methods of capturing, combining, averaging, and analyzing these processes. This chapter provides an overview of some of these methods and also reviews the various studies that have employed these methods in their search for greater insight into the task of driving. We start with the most obvious of measuresd assessing whether drivers actually look at elements of the road scene that might help prevent a collision.

2. DO DRIVERS LOOK AT CRITICAL INFORMATION? Lee (2008) reviewed 50 years of research and concluded that collisions occur because drivers “fail to look at the right thing at the right time” (p. 525). We first consider how we should measure whether drivers “look at the right thing” before later considering how to measure whether they look “at the right time.” The simplest conception of whether an individual has looked at a certain object in a scene is whether the eye coordinates recorded by an eye tracker are coincident with the world coordinates of the object. This can be calculated automatically for eye movements to static images where the precise coordinates of an object are easily defined and related to the eye position coordinates. Many eye tracking software packages allow areas of interest (AOIs) to be generated for particular pictures, which allow automatic calculation of when individuals look at specific objects. These AOIs are regions of a visual image that are defined by two-dimensional (2-D) coordinates in the viewing plane and thus allow software to identify fixations that fall within their boundaries. However, there are two particular problems with this approach. First, the AOI is typically a symmetric shape drawn on top of the image or stimulus

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(most often a rectangle). Unfortunately, real-world objects rarely fit into such shapes. Objects may have irregular outlines, or their 2-D shape may be distorted by the 3-D representation. Objects in real scenes also tend to be partially obscured or may themselves partially obscure other interesting stimuli. It is impossible to determine from 2-D data whether the participant is genuinely looking at the car ahead or whether he or she is looking through the windows of the car to identify any further traffic that might be obscured. The second, more pertinent, problem is that AOIs cannot be practically defined for unpredictable interactions on the road or in a driving simulator. Thus, if eye movements are recorded from an on-road vehicle, it is impractical to define the coordinates of a particular vehicle in the road ahead because the position of both the target car and the participant’s car would require the AOI coordinates to be updated constantly. Improvements in the software of at least one eye tracking system have extended the application of AOIs to video-based stimuli (where one can specify an AOI at several points during a video, and the software will interpolate the coordinates in between, creating a dynamic AOI); however, this procedure is limited by the predictability of the dynamic object that one wishes to track. For instance, it is relatively easy to interpolate the coordinates of an approaching car if the vehicle maintains its heading and speed: Drawing an AOI around the car when it first appears in the video and when it is last visible will allow the software to estimate the rate and direction of the AOI expansion as the vehicle approaches. Less predictable patterns require more experimenterdefined AOIs to allow more accurate prediction of how the AOIs change. At least with video-based stimuli, even if a large number of experimenter-defined AOIs were required to identify a single object, the calculations could then be applied across all participants watching the same video clips. With simulation and on-road eye tracking, however, this benefit is absent because the position and dynamic nature of visual targets will vary across participants. A requirement to define a large number of AOIs for every participant simply to assess whether drivers tend to look at a particular object would rapidly become impractical. Although it is theoretically feasible that a simulator could record coordinates of objects as they move through the virtual world, providing a personalized dynamic AOI (several research groups are pursuing this goal), we are unaware of any published articles that have used this methodology. Instead, researchers who are faced with eye movement data from dynamic stimuli (especially on-road eye tracking) must often perform a frame-by-frame analysis of video footage containing the dynamic stimuli (e.g., the external world in on-road tests, often recorded through a windscreen-mounted or head-mounted camera) and an overlaid cursor depicting where the eye tracker thinks they were looking.

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Visual Attention While Driving

A study that employed this methodology was conducted by Pradhan et al. (2005). Three groups of drivers, of varying age and experience, drove through a series of simulated scenarios in which potential hazards might (but did not) occur. For instance, one scenario contained a line of bushes obscuring the entry to a pedestrian crossing. It was feasible that a pedestrian could have emerged from behind the hedge and entered the crossing in front of the participant’s vehicle. Participants were assessed in regard to whether they looked at this hedge on approach to the pedestrian crossing, with the associated assumption that a glance at the hedge reflected the driver’s concern that it might conceal a pedestrian. The results of the study demonstrated that novices “often completely fail to look at elements of a scenario that clearly need to be scanned in order to acquire information relevant to the assessment of a potential risk” (p. 851). In one particular scenario, only 10% of novices looked in the appropriate direction to check whether pedestrians might emerge from behind a parked truck onto a crossing. The more experienced drivers, by contrast, were significantly more likely to look at these a priori areas of the visual scene, which was considered indicative of safe behavior by the experimenters. Similar results were found by Borowsky, Shinar, and Oron-Gilad (2010), who compared young, inexperienced drivers to more experienced drivers. They noted instances in which the experienced drivers fixated areas in the scene that they considered important to hazard detection (e.g., vehicles merging from an adjoining road). In this particular instance, the young, inexperienced drivers tended to focus on the road ahead, apparently disregarding the hazard posed by merging vehicles. These results are important because they offer a suggestion as to why novice drivers are consistently overrepresented in crash statistics (Clarke, Ward, Bartle, & Truman, 2006; Organisation for Economic Co-operation and Development/European Conference of Ministers of Transport, 2006; Underwood, 2007; Underwood, Chapman, & Crundall, 2009; Underwood, Crundall, & Chapman, 2007). Several researchers have argued that novices have poor hazard perception skills (Horswill & McKenna, 2004), and the results of Pradhan et al. (2005) and Borrowsky et al. (2010) ostensibly demonstrate that a failure to anticipate locations from where hazards may emerge and then prioritize these locations for visual search may be a major contributor to failures in hazard perception. There is, however, a potential confound in the interpretation of simple glance measures that record only where the driver has looked. Often, they do not take into account any measure of duration, simply assuming that to look at an object is to process that object. However, extremely short fixations may not reflect sufficient processing time for a particular fixated object to be identified. It is common practice in eye movement research to filter out extremely short fixations from subsequent analyses because they are

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unlikely to reflect object processing (often researchers define a fixation as at least 100 ms of eye stability). Fixations shorter than 100 ms do occur, but they are more likely to reflect attempts to reorient visual search on the basis of global features in the whole scene rather that processing what is at the point of fixation. However, the 100-ms cutoff is not a psychophysical threshold, after which any fixations must have accessed the identity of the fixated object, but is instead a heuristic for accepting and rejecting data. We know that some objects take longer to process than others because they have a higher threshold for identification. This is most obvious in the research literature on eye movements during reading. Results have consistently shown that lower frequency words receive longer fixations (Liversedge & Findlay, 2000; Rayner, 1998), and this appears to translate to objects in scenes, with unexpected or inconsistent objects receiving longer fixations (Henderson & Hollingworth, 1999; Underwood, Templeman, Lamming, & Foulsham, 2008). Even task-irrelevant objects may evoke longer fixation durations due to their novel or unexpected nature (Brockmole & Boot, 2009). This, in itself, does not pose a problem for the typical glance analyses seen in many driving studies (Pradhan et al., 2005), providing that all glances to target objects meet the required threshold duration. The problem arises when fixations are curtailed before the threshold is met. This issue is detailed in the E-Z Reader model of reading (Reichle, Rayner, & Pollatsek, 2003). Reichle et al. presume two levels of lexical access when looking at a word in a sentence. The first level is the familiarity check, which is undertaken when the eye first lands on the word. If the word is identified as familiar (and therefore to have a low threshold for complete identification), then the oculomotor system begins to plan the next saccade in parallel with the second level of word processing, secure in the knowledge that the full identity of the word will be accessed before the saccade is triggered. If, however, the word does not pass the familiarity check, then the subsequent plan for the next saccade may be delayed. Even if the system has already begun to plan the next saccade, it can be canceled if the order is made quickly enough (during the labile stage of the saccadic planning process). If the reader realizes that the word is a difficult one to process only once the saccadic plan has reached the nonlabile stage, the eye movement will go ahead even though the reader has realized that more attention needs to be devoted to the troublesome word. Thus, the eye will move away from the word before it is identified. This will often lead to a regressive saccade, where the eye jumps back in the text in order to reprocess a tricky word. It is highly probable that something similar occurs with drivers’ glances when on the road. Consider, for instance, the most common cause of motorcycle collisions in the United Kingdomdwhen a car driver pulls out from a side road into the path on an oncoming motorcycle (Clarke,

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Ward, Bartle, & Truman, 2007). The driver will check down the road to see if there is any conflicting traffic. If the driver’s eyes land directly upon an approaching car, the familiarity check will probably be completed successfully and allow the driver to plan the next saccade during level 2 processing. Information about this approaching vehicle (trajectory, speed, etc.) could then be integrated with information about traffic coming from the opposite direction, which may then identify a suitable gap in which to pull out. Even if the approaching car was displaying behavior that required closer attention, this should be flagged at least in the labile stage of saccadic programming, allowing the subsequent saccade to be canceled and more attention to be given to the car. The situation becomes more precarious, however, if the approaching vehicle is a motorcycle. Because motorcycles comprise only 1% of UK traffic (Department for Transport, 2010a, 2010b), they are novel, low-frequency items that should therefore be associated with higher thresholds for target identification (and thus, just as with low-frequency words, would require longer fixations before identification is achieved). Even assuming that the driver looks directly at the approaching motorcycle, the low cognitive and physical conspicuity associated with the target may result in the familiarity check wrongly assuming that the road is empty. Thus, the eye may move away from the motorcycle before the perceptual system has had time to process and identify the threat. Even if this happens, in fortunate cases the driver may at least realize that there is an approaching motorcycle in the nonlabile stage of the saccade program. Although the eyes may move off the motorcycle before fully processing it, the driver may then have enough information to realize that this was an error and then re-fixate on the motorcycle, which may be enough to enable a second look. In particularly unfortunate cases, however, the driver may have no contradictory information to challenge the initial assumption that the road is empty, and the maneuver may proceed with dire consequences. This is a classic case of a “look but fail to see” accident, in which drivers often report that they had looked in the appropriate direction but had completely failed to see the approaching motorcycle (Brown, 2002). There is even evidence that initial gaze durations upon approaching motorcycles at T-junctions can, in certain situations, be shorter than the corresponding gazes devoted to approaching cars. Considering that cars and motorcycles are the equivalent of high- and low-frequency words, respectively, we would expect this significant effect to be reversed. The short initial gazes on motorcycles were therefore argued to be indicative of initial fixations that were not long enough for drivers to realize exactly what they were looking at (Crundall, Crundall, Clarke, & Shahar, in press). To further pursue the analogy of driving and reading, a study of eye movements during reading found

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the equivalent of “look but fail to see” events (Ehrlich & Rayner, 1981). Readers were set a task of detecting misspellings in a paragraph of text, and quite often they would look at a misspelling but not report it. Interestingly, this was more likely to happen if the word was predictable: The readers saw what they expected to see, and perhaps drivers sometimes do the same. In light of the possibility of “look but fail to see” errors, one can understand how perilous it might be to rely on a binary measure of whether a glance occurred or not. Instead, we argue that glance probability or frequency needs to be paired with a measure of glance duration. The following section considers the different measures of duration that can be used and what previous studies have shown regarding the sensitivity of these durations to different driving conditions.

3. MEASURES OF GLANCE DURATION Several measures of glance duration are typically used in studies of reading, scene perception, and driving. The smallest unit of duration is the first fixation duration (FFD). This represents the time that the eye dwells in one place for the first time. This is only applicable in relation to specific objects. For instance, it makes sense to consider the FFD upon a pedestrian who steps out from behind a truck, but it is less useful to record the FFD on more general categories such as the “road ahead.” This initial fixation and subsequent fixations are often averaged to create a mean fixation duration (MDF), which can apply to both specific objects and general categories. Gazes differ slightly from fixations in that they are concerned with multiple fixations on specific objects. Depending on how large an object of interest is, it may be possible to have two separate fixations within the same object without having saccaded away (i.e., the eye moves to another location within the object). Only when a fixation occurs outside the boundary of the object does the gaze end. Total dwell time (TDT) is simply the summation of all fixations on a specific object. Figure 11.1 shows a pictorial representation of how these measures are calculated. Typically, all of these measures reflect various levels of processing of the stimuli, although there are arguments for and against different measures. For instance, TDT represents the most stable measure of overt attention, presented either in absolute terms (providing all participants had the same duration of opportunity to fixate the object) or in percentage or ratio terms. Individual fixations, however, represent the most sensitive measure of processing demand, although they are more susceptible to slight variations in a magnitude of potentially confounding factors. Henderson (1992) argues that the more global measures of fixation time, such as TDT, on an object will include not just the time spent processing the target but also many other

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FIGURE 11.1 Hypothetical fixations while driving a car. There are three fixations on the rearview mirror (b, c, and e). There are two gazes on the rearview mirror (b þ c and e). The total dwell time on the rearview mirror is b þ c þ e. Mean fixation duration on the rearview mirror is the total dwell time divided by the number of fixations [(b þ c þ e)/3]. Mean gaze duration is ((b þ c) þ e)/2.

post-identification processes, such as integrating the object into a situational model. On this basis, Henderson argues in favor of the FFD as the cleanest measure of initial processing demands, although within the driving domain this could give undue prominence to “look but fail to see” fixations, potentially leading researchers to underestimate the processing time required to successfully identify an object in the first fixation. Some researchers have gone further than Henderson: Kotowicz, Rutishauser, and Kock (2010) suggest that in simple visual search experiments, a fixation on the target might not be necessary to identify it. They found that participants could accurately report a target location with as little as 10 ms of fixation on it before it offset. They argue that the target is actually identified extrafoveally during the visual search, which then results in the saccade to the target location. The amount of time spent fixating the target does not increase target identification accuracy, but it does increase confidence in reporting the item as the target. We argue, however, that Kotowicz et al.’s data are unlikely to transfer from a simple visual search task for a target among distracters to a complex driving situation. In simple visual search tasks, the targets are usually highly constrained in both appearance and location, the task tends not to require any secondary response, and the distracters are welldefined. In driving, however, there are multiple task goals that need to be monitored, with a nonuniform background to interrogate. Furthermore, it might be understandable why participants in some simple visual search experiments attempt to maximize their use of extrafoveal vision: Without any peripheral cues, participants are faced with using a random search strategy (or at least a strategy that is unrelated to the likely location of subsequent targets). When driving, however, people often use specific search strategies to scan the road ahead in anticipation of potential hazards that might occur. As discussed later, these strategies are built up from experience and from learning where in the driving scene certain information is available or where certain hazards might be likely to appear. Thus, drivers are unlikely to mobilize as many extrafoveal resources to direct their saccades as were

participants in Kotowicz et al.’s (2010) simple visual search experiments. The use of fixation and gaze durations in driving research has revealed some very consistent patterns. For instance, Chapman and Underwood (1998) and Underwood, Phelps, Wright, van Loon, and Galpin (2005) demonstrated that fixation durations tend to increase in the presence of specific hazards (e.g., the car ahead suddenly braking). We interpret this in the same way that lowfrequency words or incongruent objects in pictures evoke longer fixation durations: Because hazards are relatively novel events, which are more difficult to predict and require additional processing, they require longer fixation durations. This is not to say that all long fixation durations on a hazard are indicative of high initial processing demands, but (as Henderson (1992) would argue) it is likely that longer fixations also reflect ongoing monitoring of the hazard, attempts to integrate it into a situational model, and concomitant memory processes. Regardless of the precise reason for the increased fixation lengths during hazards, it is clear that this is a form of attentional capture, in which the saccade to the next fixation location is delayed for longer than usual due to the additional processing tasks that are inherent with hazards. Interestingly, there is an additional parallel to the reading literature: Not only do low-frequency, novel, and complex hazards (or words) demand longer fixations but also those individuals who are considered better at the primary task tend to have overall shorter fixations. In studies of eye movements in reading, it has been shown that reading age correlates with a reduction in fixation length on words (Rayner, 1998), and greater exposure to typically low-frequency words will lower the thresholds for those words relative to those of other readers. For instance, lawyers are likely to have shorter fixations on certain Latin phrases compared to readers from other professions. In the same way, it appears that greater experience in driving tends to lead to a reduction in the duration of certain types of fixations. Chapman and Underwood (1998) noted this when recording the eye movements of novice and experienced drivers while watching hazard perception video clips.

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Although the appearance of a hazard tended to increase the fixation durations of all participants, the experienced drivers had consistently shorter fixations than the novice drivers in all conditions, especially in the presence of a hazard. They argued that this was because the hazardous events are less novel to the experienced drivers: If they have previously been exposed to similar situations, their threshold for understanding the threat posed by a particular object should be lower. This experiential effect has been replicated in a simulator. Konstantopoulos, Crundall, and Chapman (2010) measured the eye movements of driving instructors and learner drivers while navigating a hazardous route through a virtual city in a medium fidelity simulator. They found the driving instructors to have shorter, more frequent fixations than the learner drivers, which supports the suggestion that experience and expertise improve one’s ability to extract relevant driving information from a single fixation. A second finding of interest from the Konstantopoulos study was an increase in fixation durations during nighttime driving and driving through rain in the simulator. They argue that the nighttime and rain conditions decreased visibility, thereby increasing the difficulty of extracting information during individual fixations. Again, this parallels the reading research, which suggests that words that are more difficult to perceive will require longer fixations (Reingold & Rayner, 2006). However, there are certain paradoxes in the fixation duration literature on driving that echo Henderson’s (1992) concerns that anything beyond the FFD is likely to reflect a host of post-identification processes. For instance, whereas hazards tend to produce longer fixations, more complex driving scenes tend to reduce overall fixation length. For instance, Chapman and Underwood (1998) noted that video clips of complex urban settings tended to evoke significantly shorter fixations than did clips of rural settings. Similarly, on-road data suggest that more visually complex roadways tend to produce shorter, more frequent fixations than more sparsely populated roads, such as dual carriageways or single-carriageway rural roads (Crundall & Underwood, 1998). Typically, the urban and suburban roads that produce the shortest fixation durations have a greater number of potential distracters and potential hazards: Shop fronts, pedestrians, parked vehicles, road and informational signs, and roadside advertising all vie for the attention of drivers. This increase in visual complexity requires a higher sampling rate of visual search. In contrast, empty undulating rural roads provide little to distract or interest the attention of the driver beyond the immediate road ahead. Although participants might occasionally search hedgerows for gates and emerging vehicles, the majority of their time will be spent looking as far down the road as possible (although how far one can comfortably look down the road varies from person to person for a variety of reasons, including driving experience and the

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extent of extrafoveal region from which ambient information may be processed) (Underwood et al., 2007). Thus, the long fixation durations that are seen during rural driving are not predominantly due to object processing or identification but instead related to vigilance and monitoring. In summary, the length of fixation durations provides an important addition to a simple analysis of binary or frequency-based glances analyses. Unfortunately, the length of a fixation is affected by many factors (Henderson, 1992), and without a careful understanding of what is reflected in these duration measures, it is possible to draw erroneous conclusions. However, a number of clear patterns have emerged from a decade or more of studies. First, it seems that experience in the driving domain does tend to reduce fixation durations on average, most likely through a mixture of reduced thresholds for object or event identification and through an increase in processing speed. Second, localized increases in demand, such as the appearance of a hazardous pedestrian stepping out from behind a parked vehicle, tend to increase fixation durations. Considering the priority given to these stimuli, this may be understandable. However, the fact that novice driver fixations are proportionally more affected by the appearance of a hazard raises the potential problem of attentional capture beyond what is required to process the hazard. This may have a negative impact on the driver’s ability to process other objects or events that appear soon after the hazard. Third, a dispersed increase in general demand (in regard to more visually complex road scenes) provokes the opposite reaction to a localized increased in demand. Whereas hazards capture attention, urban and suburban scenes promote shorter and more frequent fixations in order to cope with the greater number of points of potential interest.

4. MEASURES OF SPREAD Although the process of identifying what drivers look at, and for how long, has resulted in a number of insights into driver vision and behavior, these measures provide no indication of whether drivers adopt general scanning strategies when driving. Certainly, a number of experts in the field of driver training expound the view that wide and constant scanning is important for safe driving (Coyne, 1997; Mills, 2005) and warn against the “disastrous habit of fixating [in one place for too long]” (Haley, 2006, p. 112). Some of the earliest research also noted that novice drivers scan a smaller area of the visual scene (Mourant & Rockwell, 1972). Later research confirmed that new drivers scan the road in a curiously maladaptive way, tending to look straight ahead of them and not showing any sensitivity to changes in driving conditions (Crundall & Underwood, 1998; Konstantopoulos et al., 2010). The measure of greatest interest in the study by Crundall and Underwood was the variance of fixation locations, with high variance

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indicating greater scanning. This measure is calculated from the location of fixations in one axis (in the eye tracking system’s reference frame). For instance, a sample of 100 fixations will provide a sequence of xy coordinates. The variance, or standard deviation, of the x coordinates reflects the extent of search activity in the horizontal axis. Likewise, using the y coordinates would produce a measure of the spread of search in the vertical axis. In the Crundall and Underwood (1998) study, new and experienced drivers traveled along a range of roads through countryside, suburban housing areas, and along a demanding section of a multilane highway. Their eye movements were monitored and recorded during this drive. On simple rural roads with few hazards, all drivers tended to look at the roadway ahead, but on a tricky multilane highway with traffic joining from both left and right, the experienced drivers increased their scanning, whereas the novices continued to look straight ahead. This is maladaptive, or insensitive, because merging traffic requires an adjustment to the driver’s own speed and a preparedness to take avoiding acting (and is in accordance with the work of Borowsky et al., 2010). The experienced drivers showed situation awareness in that they looked around them to determine the trajectories of the proximal traffic. Why should the novice drivers fail to scan for hazards on the roads most likely to present them with dangers? Three explanations considered here are that (1) novices need to look at markings on the road (white lines, curbs, and barriers) in order to steer their vehicles, (2) novices have not yet automatized the steering and speed controldthe subskills required for the coordination of the vehicledand are thus are unable to allocate mental resources to the task of monitoring other traffic, or (3) novices may opt not to look around them because they have a poor idea of the dangers present on these roadsdthey have inadequate situation awareness (Gugerty, 1997; Horswill & McKenna, 2004; Underwood, 2007). Of course, these three explanations are not mutually exclusive. It is plausible that new drivers have difficulties in maintaining their lane position, in thinking about much else other than controlling the vehicle, and in forming a mental model of what the other road users are doing and what they are likely to do next. As drivers become more skilled in handling their vehicles, cognitive resources are released and can be allocated to other tasks such as hazard surveillance. With increased experience, novice drivers no longer need to concentrate on their engine speed when deciding on the moment to change gear or on the coordinated sequence of accelerator pedal release and clutch pedal depression when doing so. They are increasingly able to think about the behavior of the traffic around them while maintaining vehicle speed and position seemingly without thinking about these relatively low-level actions. The three hypotheses emphasize steering control demands, vehicle control demands, and the driver’s situation

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awareness. The first two hypotheses are closely related, with steering control being a special case of the demands of vehicle control. Mourant and Rockwell (1972) demonstrated that novices tend to look at the roadway closer to the vehicle than do experienced drivers, perhaps suggesting that they have not yet learned to use peripheral vision for steering control (Land & Horwood, 1995) or that the dynamics of perceptual-motor coordination are still being learned. If they need to look at road markers in order to keep their vehicle in the center of their lane, then they will have limited scope for looking at other objects in the roadway. The second hypothesis extends this view of the demands of vehicle control and sees central cognitive resources being occupied more generally, to the extent that the novice driver does not have the resources available for scanning the road scene and thereby acquiring new information about potential hazards. It has been established that varying the demand of the driving task will cause variations in the acquisition of information. Recarte and Nunes (2000) reported that mirror checking is reduced as the mental load on the driver is increased, and Underwood, Crundall, and Chapman (2002) also found that mirror checking varied with driving experience, with greater selectivity of the choice of mirror used by experienced drivers during lane changing. Similarly, as driving demands increase, fewer fixations on mirrors and other nonessential objects are reported (Schweigert & Bubb, 2001). This evidence from studies of the inspection of the information available in the mirrors suggests that as driving demands increase, experienced drivers re-allocate their cognitive resources and modify their intake of information about traffic in the roadway. The third suggestion is that perhaps novices stereotypically look straight ahead when driving because they have inadequate situation awareness. Differences in search patterns associated with driving experiencedspecifically, increased variance of fixation locations in more experienced driversdwould then be explained as a product of the knowledge base developed through previous traffic encounters. As drivers interact with other drivers and observe the behavior of other road users, they accumulate memories of events that happen on different kinds of roads, and they develop an awareness of their probability of happening. These situation-specific probabilities can help guide drivers through newly encountered environments if they are sufficiently similar to earlier circumstances for drivers to generalize their behavior (Shinoda, Hayhoe, & Shrivastava, 2001). Because of his or her limited exposure to varying roadway conditions, a new driver necessarily has an impoverished catalog compared to an experienced driver. Perhaps, when novices scanned a multilane highway to a lesser extent than the experienced drivers in Crundall and Underwood’s (1998) eye tracking study, they behaved like this because they were unaware of the special dangers associated with this particular type of road. They perhaps

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had insufficient exposure to this kind of road with which to build a mental model of the behavior that might be shown by other vehicles. They would then be unable to predict where other vehicles would be a few seconds later, and they would not recognize the demands of negotiating interweaving lanes of traffic and the need to monitor not only the traffic ahead but also the lane-changing activity of traffic immediately to the rear. We sought evidence to discriminate between the vehicle control and situation awareness hypotheses by recording the scanning behavior of drivers in a laboratory task that eliminated the need for vehicle control (Underwood, Chapman, Bowden, & Crundall, 2002). Drivers sat in the laboratory and watched film clips recorded from a car as it traveled along the roads used by Crundall and Underwood (1998). One advantage of this approach is that each driver saw the same traffic conditions, whereas in the original study traffic conditions inevitably varied from moment to moment and from driver to driver. The laboratory task was essentially one of observation and prediction, with the task being to make a key-press response if they saw an event that would cause a driver to take evasive actiondessentially a hazard detection task that gave a reason for monitoring the video recordings carefully. The scanning behavior of new and experienced drivers was the principal interest, and while they watched the films, their eye movements were recorded. If new drivers have restricted search patterns because their resources are allocated to vehicle control, then eliminating the vehicle-control element of driving should result in a visual search pattern in the laboratory that is similar to that of an experienced driver. Take away the demands of maintaining vehicle speed and lane position and resources should become available for scanning, but only if the need for scanning is understood. If the search patterns of new drivers result from a mental model that does not inform them of the particular hazards associated with multilane highways, then they would continue to restrict their scanning while watching the roadway video recordings in the viewing-only task. The results indicated that the two groups of drivers were thinking about the scene differently, even when their resources were not occupied by the demands of vehicle control. Experienced drivers exhibited more extensive scanning when they watched more demanding sections of the roadway, whereas new drivers showed less sensitivity to changing traffic conditions. The eye tracking data indicated differences between new and experienced drivers that support the hypothesis that their inspection of the roadway varies not because they have differences in their mental resources residual from the task of vehicle control but, rather, because the novice drivers have an impoverished mental model of what other drivers might do on demanding roadways. Other research supports the hypothesis of limited situational awareness in novice

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drivers. Jackson, Chapman, and Crundall (2009) asked drivers to predict events from driving video clips that were stopped at critical moments and obscured. They found that more experienced drivers were better able to predict the subsequent events, which they argued was akin to greater level 3 situational awareness (Endsley, 1995, 1999). Although these measures of the spread of search have provided some consistent and replicable findings, they need to be used with caution. For instance, in Figure 11.2, panels a and b reflect the typical scan path that one might expect from an experienced driver and novice driver, respectively. Calculating the variance or standard deviation of the x-axis fixation coordinates would reveal a clear difference between the two. However, the two scan paths shown in panels c and d would be indistinguishable on the basis of this simple calculation of spread. Imagine two pedestrians on either side of the roadway: In panel c, the driver extensively scans one of the pedestrians before shifting to the other, whereas in panel d the driver constantly switches between the two pedestrians. From a commonsense viewpoint, we might consider panel d to reflect greater spread of search. Certainly, most driver-training experts would consider panel d to represent a safer search strategy than panel c (Mills, 2005). However, the calculation of the variance of locations will not discriminate between these two strategies because it cannot take into account the sequential nature of the fixations. A further issue with this measure is that it will not discriminate between a decrease in the eccentricity of fixations to the left and right of the road ahead and a decrease in the frequency of these eccentric fixations. Thus, a reduction in the actual area of the visual scene that is scanned is indistinguishable from a reduction in the number of fixations away from the road ahead. In essence, a measure of spread is still informative about the extent to which drivers sample the visual scene, but it cannot be used to separate out the more subtle differences in visual strategies. However, these spread measures can be used in conjunction with range measures (e.g., mean saccade length, or how far the visual search extends in the scene) (Crundall et al., in press) to provide a more detailed picture. The failure of measures of spread to take into account the sequential nature of the fixations can also be overcome through scan path analysis. This type of analysis searches for statistical regularity in sequences of fixation locations using a transition matrix. Underwood, Chapman, Brocklehurst, Underwood, and Crundall (2003) recorded where drivers looked as a function of where they had looked immediately beforehand. Video recordings of fixations made in the Crundall and Underwood (1998) study were used as the input to the process of identifying fixation scan paths, and driver differences in the inspection of different roadways were again the focus of interest. The most interesting differences between drivers were again seen when

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FIGURE 11.2 Hypothetical scan patterns while driving a car. Panel a reflects a typical experienced driver, panel b reflects a typical novice, and panels c and d represent two scan paths that pose problems for simple calculations of spread of search.

they traveled along the multilane highway with merging trafficdthe most demanding section of the drive. The increased variance of experienced drivers from the earlier analysis was reflected in the scan paths, which showed very little consistency. There were few two-fixation scan paths that appeared regularly for these drivers, indicating that their fixation behavior was unpredictable statistically, and this can be explained by variations in traffic conditions from moment to moment prompting changes in fixations. As other vehicles appeared in the roadway or in the vehicle’s mirrors, the experienced drivers inspected them and evaluated their trajectories. There was no consistency in the location of one fixation according to where they had looked previously. The new drivers, on the other hand, showed a remarkable consistency that can be summarized by

a simple generalization: Wherever they had looked previously, the next place they looked was at the roadway straight ahead of them. Their fixation behavior was stereotyped and not sensitive to the variations in traffic behavior seen on a highway with fast-moving vehicles that are regularly changing lanes, both ahead and to the rear. The low variance of fixations recorded by Crundall and Underwood was a product of the new drivers repeatedly moving their eyes to inspect the roadway directly before them.

5. CONCLUSIONS The use of eye tracking measures has greatly increased our understanding of how driving skills develop and what strategies drivers employ to ensure a safe journey. Eye

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movement analyses are now being applied to help understand specific accident types (e.g., spotting overtaking motorcycles; Shahar, van Loon, Clarke, & Crundall, in press) and are forming the basis of training interventions to decrease accident rates (Chapman, Underwood, & Roberts, 2002; Pradhan, Pollatsek, Knodler, & Fisher, 2009). It is in these areas that the most exciting advances are being made. However, there are caveats that need to be mentioned. First, we have noted in this chapter that eye movement measures, when taken in isolation, are open to errors of interpretation. Simply inferring safe driving on the basis of whether one has looked at a particular area of the scene (Pradhan et al., 2005) may not be sufficient because extremely short fixations may not be indicative of full processing (as with “look but fail to see” errors; Crundall et al., in press). Similarly, we noted that measures of spread may be unable to identify certain visual strategies. Thus, it seems that multiple measures of visual behavior should be taken to ensure that the potential confounds associated with one particular measure do not dominate the conclusions. Second, we need to relate all measures back to the context in which they were collected. It is useful to relate measures of vision in driving to other fields of research such as reading, although it must always be borne in mind that the complex context of real driving is unlikely to be paralleled in an analogous laboratory. Thus, although reading research provides us with a framework with which to interpret increased fixation durations in terms of processing difficulty, this analogy does not necessarily transfer to the rural road, where fixations on the focus of expansion can be extremely large. The number of variables that can influence the patterns of eye movements during driving seem too many to document, but that has not stopped attempts to do so. Indeed, during approximately the past 10 years, great strides have been made in our understanding of how various factors interact, although we must remember that there are other potential aspects of the context that we have not accounted for when drawing conclusions. Finally, when considering the potential for designing training interventions to encourage eye movements, particularly in young and novice drivers, we must be aware of the developmental limitations on eye movement strategies. For instance, Mourant and Rockwell (1972) found that novice drivers look more at lane markings than do more experienced drivers; Land and Horwood (1995) demonstrated that experienced drivers still use the information provided by lane markers but do so through peripheral vision; and Crundall, Underwood, and Chapman (1999, 2002) demonstrated that inexperienced drivers have fewer resources devoted to peripheral vision than do more experienced drivers. Taken together, this body of work suggests that although lane markings are of vital importance to maintaining the lateral position of the vehicle,

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inexperienced drivers do not necessarily have the available resources to devote to peripheral vision in order to extract lane marker information without foveating them. Thus, an intervention strategy that directly or indirectly trains inexperienced drivers to focus less on lane markings may have the unintended effect of impairing lane maintenance. Despite these caveats, it is clear that studies of eye movements have provided considerable insights into the driving process and have achieved moderate success in rudimentary training situations (Chapman et al., 2002; Pradhan et al., 2005). As eye tracking technology continues to improve and costs are reduced, the use of these systems in future research will increase, and we hope that this brief discussion provides some ideas on how to best employ this technology for current and future uses.

REFERENCES Borowsky, A., Shinar, D., & Oron-Gilad, T. (2010). Age, skill and hazard perception in driving. Accident Analysis and Prevention, 42, 1240e1249. Brockmole, J. R., & Boot, W. R. (2009). Should I stay or should I go? Attentional disengagement from visually unique and unexpected items at fixation. Journal of Experimental Psychology: Human Perception and Performance, 35, 808e815. Brown, I. D. (2002). A review of the “look but failed to see” accident causation factor. In Behavioural Research in Road Safety XI. London: Department for Transport, Local Government and the Regions. Caplovitz, G. P., Fendrich, R., & Hughes, H. C. (2008). Failures to see: Attentive blank stares revealed by change blindness. Consciousness and Cognition, 17, 877e886. Chapman, P., & Underwood, G. (1998). Visual search of driving situations: Danger and experience. Perception, 27, 951e964. Chapman, P., Underwood, G., & Roberts, K. (2002). Visual search patterns in trained and untrained novice drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 5, 157e167. Clarke, D. D., Ward, P., Bartle, C., & Truman, W. (2006). Young driver accidents in the UK: The influence of age, experience, and time of day. Accident Analysis and Prevention, 38, 871e878. Clarke, D. D., Ward, P., Bartle, C., & Truman, W. (2007). The role of motorcyclist and other driver behaviour in two types of serious accident in the UK. Accident Analysis and Prevention, 39, 974e981. Coyne, P. (1997). Roadcraft: The essential police driver’s handbook. London: The Stationary Office. Crundall, D., Crundall, E., Clarke, D., and Shahar, A. (in press). Why do car drivers violate the right-of-way of motorcycles at t-junctions? Accident Analysis and Prevention. Crundall, D., & Underwood, G. (1998). Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics, 41, 448e458. Crundall, D., Underwood, G., & Chapman, P. (1999). Driving experience and the functional field of view. Perception, 28, 1075e1087. Crundall, D., Underwood, G., & Chapman, P. (2002). Attending to the peripheral world while driving. Applied Cognitive Psychology, 16, 459e475.

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Department for Transport. (2010a). Road traffic by vehicle type, Great Britain: 1950e2009 (miles). http://www.dft.gov.uk/pgr/statistics/ datatablespublications/roads/traffic/annual-volm/tra0101.xls. Accessed November 11, 2010. Department for Transport. (2010b). Road traffic and speed statisticsd2009. http://www.dft.gov.uk/pgr/statistics/datatablespublications/roads/traffic. Accessed November 11, 2010. Duchowski, A. T. (2007). Eye tracking methodology: Theory and practice (2nd ed.). New York: Springer-Verlag. Ehrlich, S. E., & Rayner, K. (1981). Contextual effects on word perception and eye movements during reading. Journal of Verbal Learning and Verbal Behavior, 20, 641e655. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32e64. Endsley, M. R. (1999). Situation awareness and human error: Designing to support human performance. In Proceedings of the high consequences system surety conference, Albuquerque, NM. Gugerty, L. (1997). Situation awareness during driving: Explicit and implicit knowledge in dynamic spatial memory. Journal of Experimental Psychology: Applied, 3, 42e66. Haley, S. (2006). Mind driving. Croydon, UK: Safety House. Henderson, J. M. (1992). Identifying objects across saccades: Effects of extrafoveal preview and flanker object context. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 521e530. Henderson, J. M., & Hollingworth, A. (1999). The role of fixation position in detecting scene changes across saccades. Psychological Science, 10, 438e443. Horswill, M. S., & McKenna, F. P. (2004). Drivers’ hazard perception ability: Situation awareness on the road. In S. Banbury, & S. Tremblay (Eds.), A cognitive approach to situation awareness. Aldershot, UK: Ashgate. Jackson, A. L., Chapman, P., & Crundall, D. (2009). What happens next? Predicting other road users’ behaviour as a function of driving experience and processing time. Ergonomics, 52(2), 154e164. Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329e354. Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-Car Naturalistic Driving Study data. Washington, DC: National Highway Traffic Safety Administration. Konstantopoulos, P., Crundall, D., & Chapman, P. (2010). Driver’s visual attention as a function of driving experience and visibility. Using a driving simulator to explore visual search in day, night and rain driving. Accident Analysis and Prevention, 42(Special issue), 827e834. Kotowicz, A., Rutishauser, U., & Kock, C. (2010). Time course of target recognition in visual search. Frontier in Human Neuroscience, 4, 31. Land, M. F., & Horwood, J. (1995). Which parts of the road guide steering? Nature, 377, 339e340. Lee, J. D. (2008). Fifty years of driving safety research. Human Factors, 50, 521e528. Lestina, D. C., & Miller, T. R. (1994). Characteristics of crash-involved younger drivers. In 38th Annual proceedings of the Association for the Advancement of Automotive Medicine (pp. 425e437). Des Plaines, IL: Association for the Advancement of Automotive Medicine. Liversedge, S. P., & Findlay, J. M. (2000). Saccadic eye movements and cognition. Trends in Cognitive Sciences, 4, 6e14.

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Mills, K. C. (2005). Disciplined attention: How to improve your visual attention when you drive. Chapel Hill, NC: Profile Press. Mourant, R. R., & Rockwell, T. H. (1972). Strategies of visual search by novice and experienced drivers. Human Factors, 14, 325e335. Organisation for Economic Co-operation and Development/European Conference of Ministers of Transport. (2006). Young drivers: The road to safety. Paris: Organisation for Economic Co-operation and Development. Pradhan, A. K., Hammel, K. R., DeRamus, R., Pollatsek, A., Noyce, D. A., & Fisher, D. L. (2005). Using eye movements to evaluate effects of driver age on risk perception in a driving simulator. Human Factors, 47, 840e852. Pradhan, A. K., Pollatsek, A., Knodler, M., & Fisher, D. L. (2009). Can younger drivers be trained to scan for information that will reduce their risk in roadway traffic scenarios that are hard to identify as hazardous? Ergonomics, 52, 657e673. Rayner, K. (1998). Eye movements in reading and information processing: Twenty years of research. Psychological Bulletin, 124, 372e422. Recarte, M. A., & Nunes, L. M. (2000). Effects of verbal and spatialimagery tasks on eye fixations while driving. Journal of Experimental Psychology: Applied, 6, 31e43. Reichle, E. D., Rayner, K., & Pollatsek, A. (2003). The E-Z Reader model of eye movement control in reading: Comparisons to other models. Behavioral and Brain Sciences, 26, 445e476. Reingold, E. M., & Rayner, K. (2006). Examining the word identification stages hypothesized by the E-Z Reader model. Psychological Science, 17, 742e746. Sabey, B.E., & Staughton, G. C. (1975). Interacting roles of road environment, vehicle and road user in accidents. In Proceedings of the fifth international conference of the International Association for Accident and Traffic Medicine. Schweigert, M., & Bubb, H. (2001, August). Eye movements, performance and interference when driving a car and performing secondary tasks. Brisbane, Australia: Paper presented at the Vision in Vehicles 9 conference. Shahar, A., van Loon, E., Clarke, D., & Crundall, D. (in press). Attending overtaking cars and motorcycles through the mirrors before changing lanes. Accident Analysis and Prevention. Shinar, D. (2008). Looks are (almost) everything: Where drivers look to get information. Human Factors, 50, 380e384. Shinoda, H., Hayhoe, M. M., & Shrivastava, A. (2001). What controls attention in natural environments? Vision Research, 41, 3535e3545. Sivak, M. (1996). The information that drivers use: Is it indeed 90% visual? Perception, 25, 1081e1089. Treat, J. R., Tumbas, N. S., McDonald, S. T., Shinar, D., Hume, R. D., Mayer, R. E., Stansifer, R. L., & Castellan, N. J. (1979). TRI-level study of the causes of traffic accidents: Final report. (Report No. DOT HS 805-085). Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration. Underwood, G. (2007). Visual attention and the transition from novice to advanced driver. Ergonomics, 50, 1235e1249. Underwood, G., Chapman, P., Bowden, K., & Crundall, D. (2002). Visual search while driving: Skill and awareness during inspection of the scene. Transportation Research Part F: Traffic Psychology and Behaviour, 5, 87e97. Underwood, G., Chapman, P., Brocklehurst, N., Underwood, J., & Crundall, D. (2003). Visual attention while driving: Sequences of eye

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fixations made by experienced and novice drivers. Ergonomics, 46, 629e646. Underwood, G., Chapman, P., & Crundall, D. (2009). Experience and visual attention in driving. In C. Castro (Ed.), Human factors of visual and cognitive performance in driving (pp. 89e116). Boca Raton, FL: CRC Press. Underwood, G., Crundall, D., & Chapman, P. (2002). Selective searching while driving: The role of experience in hazard detection and general surveillance. Ergonomics, 45, 1e12. Underwood, G., Crundall, D., & Chapman, P. (2007). Cognition and driving. In F. Durso (Ed.), Handbook of applied cognition (2nd ed). (pp. 391e414). New York: Wiley. Underwood, G., & Everatt, J. (1992). The role of eye movements in reading: Some limitations of the eyeemind assumption. In

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E. Chekaluk, & K. R. Llewellyn (Eds.), The role of eye movements in perception. Amsterdam: North-Holland. Underwood, G., Phelps, N., Wright, C., van Loon, E., & Galpin, A. (2005). Eye fixation scanpaths of younger and older drivers in a hazard perception task. Ophthalmic and Physiological Optics, 25, 346e356. Underwood, G., Templeman, E., Lamming, L., & Foulsham, T. (2008). Is attention necessary for object identification? Evidence from eye movements during the inspection of real-world scenes. Consciousness & Cognition, 17, 159e170. Van Gompel, R. P. G., Fischer, M. H., Murray, W. S., & Hill, R. L. (2007). Eye-movement research: An overview of current and past developments. In R. P. G. van Gompel, M. H. Fischer, W. S. Murray, & R. L. Hill (Eds.), Eye movements: A window on mind and brain (pp. 1e28). Oxford: Elsevier.

Chapter 12

Social, Personality, and Affective Constructs in Driving Dwight Hennessy Buffalo State College, Buffalo, NY, USA

1. INTRODUCTION Driving is more than the mechanical operation of a vehicle, as a means of movement between destinations. Rather, it is a complex process involving individual factors expressed within a social exchange among drivers, passengers, and pedestrians, which is ultimately impacted by contextual and environmental stimuli found inside and outside the vehicle. Rotton, Gregory, and Van Rooy (2005) argued that, traditionally, the focal point of most traffic research has been the individual in this system, often at the expense of situational determinants. This is perhaps due to the fundamental attribution error, which is the tendency to explain the actions of others in terms of personal causes even when situational factors are evident. The driver is a central component of this system, but only one component, whose thoughts, feelings, and actions are shaped and directed by the micro and macro context. In many respects, the traffic environment is a distinct and intriguing setting. There is a degree of speed and anonymity not found in other contexts, with huge discrepancies in history, experience, or skill level among drivers; subtle forms and means of communication that often have multiple interpretations; a unique blend of written and unwritten rules that can vary across locations; and ultimately a high degree of danger. This uniqueness, in addition to the widespread application potential and relevance to the general public, has made the traffic environment an attractive context for social research. This chapter focuses on the components of this personesituation system and how they have been shown, individually and in combination, to impact driving behavior (e.g., rule violations and collision), personal outcomes (e.g., health, mood, stress, and fatigue), and interpersonal interactions (e.g., aggression and judgments of others).

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10012-8 Copyright Ó 2011 Elsevier Inc. All rights reserved.

2. PERSONAL FACTORS AND DRIVING OUTCOMES Given that it is “people” who drive, it would seem rational that any discussion of factors that impact driving outcomes would include personal factors. It would also seem logical to assume that drivers are all ultimately unique, and that at any given moment on the roadway, there is immense variability in driving styles, learning experiences, collision history, and expectations/judgments. However, this does not preclude the search for patterns that identify categories of drivers who might be more or less at risk for negative outcomes on the roadway over time. Although there are numerous categories of individual differences that can impact driving, traffic psychology has often focused on personality variables. In fact, personality has been used to qualify many demographic and affective factors, such as gender (often associated with a masculinity trait) and driver anger (often examined as a trait disposition). Although there are many definitions of personality, most share the notion that it involves the consistent pattern of thoughts, feelings, and actions that emerge with some level of stability across time and context. Traffic psychology has typically relied on the “trait” approach, in which the focus has been on individual characteristics that combine or cluster to determine the overall expression of personality. As a result, certain traits are believed to be inherently more dangerous than others in the traffic environment. Those who possess more of these or in a more dangerous balance are believed to be a greater risk to self and others. However, it is also a matter of “degree” in that each trait is identified along a continuum of its “strength,” where some characteristics may have a much stronger impact on the overall personality, and those that possess dangerous traits to a higher degree are most problematic. 149

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2.1. A Case for Personality in Understanding Negative Driving Outcomes A number of researchers have attempted to link personality to negative driving outcomes, particularly collisions, with mixed results. One reason for such discrepancies may be the selection and number of personality factors used in prior traffic research. Personality and its behavioral outcomes may be most accurately reflected by combinations or clusters of traits rather than by individual components. In this respect, personality and its expression are multidimensional. Thus, selection of unitary constructs or perhaps the “wrong” blend of personality characteristics may give the impression that it is not predictive of an outcome as rare and complex as collisions. Another reason may be that personality represents an indirect rather than direct link with collisions. Beirness (1993) argued that personality on its own is a poor predictor of collisions but instead interacts with more “proximal” factors that often involve current, state, or pressing drivingrelated factors. Su¨mer (2003) provided an excellent review of this research and proposed a model in which personality represents one of several possible distal factors (in addition to other enduring personal, cognitive, and situational factors, such as culture, vehicle condition, and attributions) that impact more immediate proximal factors composed of driving style and transitory factors (e.g., violations, errors, and safety skills) that then influence collisions. In this respect, personality is an important focal point in traffic research given its impact on the more macro, persistent, or ambient distal level (e.g., the approach to driving, personal tendencies, and beliefs about other drivers), as well as on the immediate and transitory proximal influences (e.g., altering state interpretations of actual driving behavior, state emotional experiences, and negative driving behaviors). The following sections provide a brief examination of personality factors that have been linked to negative driving outcomes. Although this list is far from comprehensive, it represents factors commonly observed in traffic psychology.

2.2. Sensation Seeking One of the most widely studied personality predictors of negative driving behavior is sensation seeking, which is defined as a trait characterized by the pursuit of novel, diverse, and extreme experiences. To achieve these goals, high sensation seekers often display a willingness to take disproportionate physical and social risks (Zuckerman, 1994). Driving provides an excellent opportunity for high sensation seekers to satisfy the desire for sensation given the inherent potential for arousal, excitement, danger, speed, and competition. Jonah (1997) reviewed 40 studies on sensation seeking in drivers and concluded that it can

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increase dangerous driving in a number of areas, including impaired driving, speeding, and seat belt use. In fact, a relationship in the range of 0.3 to 0.4 was found in all but 4 studies he examined. Sensation seeking is typically measured using Zuckerman’s Sensation Seeking Scale (SSS) Form V, which is further divided into four subscales identifying boredom susceptibility (BS), thrill and adventure seeking (TAS), disinhibition (DIS), and experience seeking (ES). However, comparisons across studies applying sensation seeking to driving behavior are difficult due to the fact that researchers often do not use the entire SSS, instead opting for selective subscales (thrill seeking being the most typical), truncation of items from subscales, combinations of items with selfgenerated or other unrelated items, or a total sensation seeking score that is undifferentiated across subscales. Another tool that is gaining in popularity is the Arnett Inventory of Sensation Seeking (AISS), which was designed in response to perceived limitations in Zuckerman’s SSS. Most notably, Arnett (1994) believed that several items were culturally irrelevant and also contained risky behavior-based items that were confounded with many of the activities that were typically used as outcome variables in research, including drinking and taking drugs. The AISS contains two subscales based on a need for novelty and intensity of stimulation. Previous research has established that the AISS does measure a similar dimension of sensation seeking as that measured by the SSS and predicts dangerous and risky behavior (Andrew & Cronin, 1997; Arnett, 1994). Despite their differences, both the AISS and the SSS have been linked to negative driving outcomes. According to Zuckerman (2007), the SSS predicts reduced risk appraisal and elevated risk taking, including reckless driving. Jonah, Thiessen, and Au-Yeung (2001) argued that sensation seeking does appear to increase dangerous driving patterns, including aggressive (e.g., swearing, yelling, and horn honking) and high-risk activities (e.g., weaving and speeding). Interestingly, this trend is evident even among predrivers (Waylen & McKenna, 2008). Specifically, using eight items from the AISS, high sensation seeking among boys aged 11e16 years was related to favorable attitudes toward risky road use. Other negative driving activities that have been linked to sensation seeking include tailgating and speeding (using TAS & BS subscales; Harris & Houston, 2010), lack of seat belt use and unsafe passing (using TAS & DIS subscales; Dahlen & White, 2006), and ignorance of traffic rules (using newly generated items; Iversen & Rundmo, 2002). Sensation seeking has also been found to predict elevated convictions (using TAS & BS subscales; Matthews, Tsuda, Xin, & Ozeki, 1999), self-report traffic violations (using total SSS score; Schwerdtfeger, Heims, & Heer, 2010), drinking and driving (using all four SSS factors; Greene,

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Krcmar, Walters, Rubin, & Hale, 2000), and driving under the influence of cannabis (using total SSS; Richer & Bergeron, 2009). Furthermore, using TAS and DIS ˚ berg (1999) determined that the subscales, Rimmo¨ and A violations subscale of the DBQ (which measures a dispositional tendency to willfully commit violations) mediated the relationship between sensation seeking and self-report violations and collisions. Interestingly, Wong, Chung, and Huang (2010) advocated a positive effect of sensation seeking in that high sensation seekers (using self-derived items) were involved in fewer motorcycle collisions due to the regulating effect of confidence in their riding ability. However, the collisions they experienced were more severe than those of low sensation seekers. Some critics have argued that caution must be exercised when interpreting reported associations between sensation seeking and risky behaviors due to a high degree of conceptual overlap between the two constructs (Arnett & Balle-Jensen, 1993). A risk-taking lifestyle may naturally predispose one to seek stimulating activities; hence, the psychological phenomenon of sensation seeking may overlap with the behavioral predictor of risk taking. However, Burns and Wilde (1995) contended that sensation seekers may, in fact, seek activities that are not inherently risky. For example, sensation seekers may drive fast and experience heightened arousal yet remain within the bounds of safety by not taking excessive risks (e.g., driving on higher speed highways). Another consideration is the fact that sensation seeking is strongly associated with age and developmental processes, which peak in late adolescence and begin to decline thereafter (Arnett & Balle-Jensen, 1993). This is especially important in traffic research because most studies that focus on dangerous or risky outcomes of sensation seeking tend to concentrate on younger drivers, for whom sensation seeking, risky tendencies, and dangerous driving are concurrently heightened. In addition, younger drivers are typically the least experienced and may engage in more dangerous activity not because they are sensation seekers but, rather, because they lack the practical knowledge, speed of processing, or sensitivity to potential danger that come with practice. In this sense, the link between sensation seeking and risky or dangerous driving may be overstated. However, the fact that sensation seeking is still linked to negative driving outcomes among older drivers may provide impetus for its continued use in identifying a category of at-risk drivers.

2.3. Trait Aggression The issue of aggressive driving has received a great deal of attention from both scientific and public communities. However, there are differing conceptualizations of the term “aggressive,” which has made it difficult to interpret and

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compare conclusions in this area. Following the motivational approach to aggression (Berkowitz, 1993), many have treated traffic aggression as actions intended to physically, psychologically, or emotionally harm another within the driving environment, including drivers, passengers, and pedestrians (Hennessy & Wiesenthal, 1999; Shinar, 1998). Examples include yelling, swearing, purposely tailgating, leaning on the horn, and roadside confrontations. In contrast, others have approached aggressive driving as any dangerous driving behavior regardless of intent, such as speeding, weaving through traffic, and using the shoulder to pass. However, several studies have found that these latter actions, which more closely resemble highway code violations or assertiveness, are distinct compared to intentionally harmful “aggressive violations.” Drivers who intentionally cause other motorists harm are different in important ways from those who break traffic rules, even though these latter actions are sometimes selfish, illegal, or dangerous to others (Hennessy & Wiesenthal, 2005; Lawton, Parker, Manstead, & Stradling, 1997). For the purpose of this chapter, discussions of driver aggression focus on the motivational definition emphasizing intentional harm. One important limitation to this approach is that intention is often difficult to determine, especially when self-report measures are used. This is exaggerated by the fact that several actions that are often considered as “aggressive” can also have different meanings between cultures or could be initiated for reasons other than harm. For example, Lajunen, Parker, and Summala (2004) noted that horn honking is typically considered aggressive in Scandinavia but as a message in southern Europe. Nonetheless, research has found that aggressive drivers typically show patterns of increased frequency and duration, as well as decreased latency of such actions (e.g., honk more often, for longer, and quicker in response instigation), and that they concurrently engage in a pattern of multiple forms of aggression, all of which would be more indicative of aggression than signaling behavior (Doob & Gross, 1968; Gulian, Matthews, Glendon, & Davies, 1989; Hennessy & Wiesenthal, 2005). Although aggression is predicted by numerous personal, social, cognitive, and environmental factors (Berkowitz, 1993), there are some individuals who demonstrate a “trait”-like proclivity toward more frequent and severe acts of aggression. From this perspective, some drivers develop trait driver aggression tendencies (Hennessy & Wiesenthal, 1999), which represents a unique personal quality from those who seek to take risks or gain excitement from driving. Rather, trait driver aggression appears to involve an elevated tendency to misperceive the actions and intentions of other drivers as hostile and threatening; to become frustrated or irritated by the actions of others; and to be willing to “pay back” offending drivers

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through physical, emotional, and psychological harm (Gulian, Matthews, et al., 1989; Hennessy & Wiesenthal, 2001a). One explanation for the development of trait driver aggression is grounded in learning theories. Specifically, acting in an aggressive manner, when viewed as “successful,” can be negatively reinforcing due to the removal of the source of irritation, frustration, and conflict and/or positively reinforcing through the addition of feelings of control, power, and dominance. Over time, these actions are then considered as more acceptable, beneficial, and potentially successful during similar subsequent situations. As a result, aggression becomes more customary for that driver as such responses increase in his or her response hierarchy. With repeated reinforcement, and concurrent lack of negative repercussions, driver aggression becomes acceptable or normative to that driver’s behavioral repertoire. Trait aggression has been linked to a number of dangerous outcomes, including state aggressiveness and violations. Britt and Garrity (2006) had participants recall past driving events and indicate how they would react, and the authors found that dispositional aggression predicted angry and aggressive responses. Using selfreport measures, Hennessy and Wiesenthal (2002) found that trait driver aggression was related to traffic (highway code) violations, which is consistent with the results of King and Parker (2008), who reported that trait aggressive drivers were more likely to commit traffic violations and to falsely underestimate the frequency of their violations in comparison to others. Similarly, Maxwell, Grant, and Lipkin (2005) found a relationship between trait driver aggression and traffic violations in a sample of UK drivers, whereas Kontogiannis, Kossiavelou, and Marmaras (2002) also noted that speeding convictions and general law breaking were predicted by a tendency to commit aggressive violations among Greek drivers. The link between trait aggression and collisions may be more complicated, perhaps due to the relative rarity of collisions. Li, Li, Long, Zhan, and Hennessy (2004) found that self-reported trait driver aggression was related to active collisions, as well as traffic violations, among Chinese drivers. However, they cautioned that their sample was predominantly males, which may overestimate this ¨ zkan, and Lajunen (2008) also relationship. Bener, O revealed that driver aggression predicted collisions in Qatar, although their aggressive construct also contained elements of speeding competition. Gulliver and Begg (2007) utilized face-to-face interviews with drivers and identified a relationship between trait aggression and crashes, but only among men and after controlling for exposure. According to Su¨mer (2003), the link between trait aggression and collisions may, in fact, be indirect

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because it is mediated by an aberrant driving style (i.e., elevated errors and violations).

2.4. Negative Emotions and Trait Anger Negative emotions experienced while driving may alter cognitions of traffic-related stimuli, narrow attentional focus or processing, and modify interpretations of other drivers and their activities, thus eventually increasing the potential for risky, harmful, or unsafe driving (Mesken, Haganzieker, Rothengatter, & de Waard, 2007). Shahar (2009) found that anxiety can have a negative impact on the number of errors, lapses, and ordinary violations, largely due to distraction and attention deficits. Similarly, Oltedal and Rundmo (2006) demonstrated that anxiety may have an indirect relationship to collisions through its link with risky driving behavior. However, others have argued that anxiety can serve a positive role in driving through increased cautiousness and alertness (Stephens & Groeger, 2009). Other emotions, such as sadness, may have an indirect impact on driving outcomes as well. According to Peˆcher, Lemercier, and Cellier (2009), drivers who listened to sad music in a simulator felt calmer but were unable to focus as much attention on the driving task. Similarly, Bulmash et al. (2006) found slower steering reaction times and a higher number of accidents among depressed participants in a simulator. The most commonly examined emotion in driving research has been anger. Using a diary methodology, Underwood, Chapman, Wright, and Crundall (1999) revealed that 85% of their sample of UK drivers (104 participants recruited from the general driving population through media advertisements and from driving test centers) reported experiencing anger at least once during their routine daily driving excursions during a 2-week period. According to Speilberger (1988), there is a clear distinction between state and trait anger. State anger represents an emotional experience, from mild annoyance to extreme fury, in a given context. Trait anger characterizes an enduring tendency to experience state anger more frequently, intensely, and with greater duration. Deffenbacher and colleagues have advocated a similar distinction in the driving environment where trait driver anger represents a special context-specific form of trait anger that predicts more frequent and intense anger in actual driving conditions (Deffenbacher, Huff, Lynch, Oetting, & Salvatore, 2000). The most widely used measurement tool to date has been the Driving Anger Scale (DAS; Deffenbacher, Oetting, & Lynch, 1994), in which participants are asked to rate their likely degree of anger to common anger-provoking driving situations, leading to an average score believed to differentiate drivers on a continuum of trait driver anger.

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The DAS has been translated into several languages and used in a number of different countries, with a general degree of success. Despite variability in factor structure and unique predictive ability, there has been general acceptance in its ability to measure a “trait” anger characteristic. For example, Deffenbacher et al. (1994) and Yasak and Esiyok (2009) found six factors using a U.S. and Turkish sample, respectively, whereas Lajunen, Corry, Summala, and Hartley (1998) identified three factors in a UK sample, Bjo¨rklund (2008) found a similar three-factor version using a Swedish translation, and Sullman (2006) uncovered four factors in a New Zealand sample. Also, Sullman found age and gender differences in DAS scores, whereas Yasak and Esiyok did not. Despite discrepancies, trait driver anger has been linked to a number of dangerous driving behaviors, including violations (Lajunen et al., 1998), lost concentration, poor driving control, and close calls (Dahlen, Martin, Ragan, & Kuhlman, 2005). Some studies have linked driving anger to collision, but others have not. Deffenbacher et al. (2000) found that driving anger was related to greater self-reported lifetime collisions and minor collisions during the past year, as well as greater moving violations. In contrast, according to Deffenbacher, Lynch, Oetting, and Yingling (2001), although trait anger was related to low levels of concentration, poor vehicle control, and close calls, it did not predict collisions or violations. Similarly, Sullman (2006) found that the DAS was unrelated to self-reported collisions during the past 5 years. This incongruity may be due to the relative rarity of collisions as an outcome measure, difficulties in collecting collision data accurately (particularly self-reports of active or at-fault collisions), as well as numerous indirect and contextual influences on collisions. Deffenbacher, Deffenbacher, Lynch, and Richards (2003) also identified aggression as a consequence of elevated driving anger independent of demographic variables. However, others would contend that the link between anger and state aggression is not always strong or direct. Considering the total volume of drivers and distance driven per day, and numerous potential moderating and mediating factors in the traffic environment, aggression may not be an overly common event (Parker, Lajunen, & Summala, 2002). In this respect, Neighbors, Vietor, and Knee (2002) advocated only a modest link between anger and aggression. It could be maintained that the strength and nature of such a relationship ultimately depend on a number of interacting personal and situational factors, such as the type and nature of interactions, the degree of frustration, motivation, and public self-consciousness (Lajunen & Parker, 2001; Millar, 2007). One consideration concerning driving anger research is the fact that some researchers neglect to differentiate state from trait anger, to the extent that some treat their trait

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measurement as a state outcome. Although trait anger may predict greater state anger in driving situations, it is not deterministic. Another concern is the fact that a metaanalytic review of the angereaggression link by Bettencourt, Talley, Benjamin, and Valentine (2006) showed that trait anger predicts aggression only in hostile situations. Similarly, Wilkowski and Robinson (2010) argued that anger predicts reactive forms of aggression. This might account for the contention from Van Rooy, Rotton, and Burns (2006) that the DAS and Driving Vengeance Questionnaire (DVQ) are indistinguishable from one another based on their extremely high correlation. The DVQ was designed as a measure of attitudes toward vengeance in the driving environment, where those who have been wronged or provoked by other drivers would seek revenge through reactive types of aggression (Wiesenthal, Hennessy, & Gibson, 2000). Hence, the high correlation found by Van Rooy et al. (2006) may be due to confounding by reactive aggression. However, it could just as easily be argued that trait vengeful drivers become more easily and intensely angered and as a result are more inclined to respond to provocation from others through aggression.

2.5. Trait Driver Stress Susceptibility Most traffic research has treated driver stress as the outcome of a negative cognitive appraisal of driving situations (Glendon et al., 1993; Hennessy & Wiesenthal, 1997). It is only when driving is interpreted as demanding or overly taxing that stress manifests itself in psychological (e.g., anxiety and negative mood; Gulian, Matthews, et al., 1989; Wiesenthal, Hennessy, & Totten, 2000), cognitive (e.g., task-relevant cognitive interference and loss of attention; Desmond & Matthews, 2009; Matthews, 2002), or physical symptoms (e.g., increased heart rate and blood pressure; Stokols, Novaco, Stokols, & Campbell, 1978). In this respect, trait driver stress has immediate and long-term consequences to drivers. According to the transactional model of driver stress (Matthews, 2002), trait stress susceptibility is an important personal factor that can potentially interact with the situation to impact driving outcomes. This model holds that driver stress is the product of the dynamic interaction between personal and environmental factors that are mediated by cognitive processes (e.g., interpretation of events and selection of coping resources). Repeated stressful experiences or ineffective coping strategies can lead to feedback that dynamically alters the transactional process and eventual behavior of that driver. For example, repeated stressful outcomes may lead to a generalized trait driver stress susceptibility, which may then impact the driving activities and resulting situational factors that might be encountered, subsequently altering perceptions and

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interpretations of those events and ultimately impacting the state emotional experience in actual conditions. Based on this model, Gulian, Matthews, et al. (1989) developed the Driving Behaviour Inventory (DBI) as a tool to measure the factors, or characteristics, associated with appraisal styles and coping behaviors reflective of driver stress. It consists of both a five-factor solution and a single “general” stress factor solution. The five-factor solution includes three main factors labeled as Dislike of Driving, Driving Aggression, and Alertness, as well as two minor factors identified as Irritation When Overtaken and Frustration When Overtaking. The General factor is composed of a much smaller set of items from the five-factor solution. Although the General factor has been found to predict state driver stress and negative emotion in actual driving conditions (Hennessy & Wiesenthal, 1997), and has been linked to speeding convictions and minor accidents (Matthews, Dorn, & Glendon, 1991), the three main factors from the five-factor solution are most commonly used in research. Dislike of Driving (DIS) represents items related to anxiety, unhappiness, and lack of confidence while driving, particularly when driving conditions are difficult. DIS has been found to predict negative mood in a simulated driving task (Dorn & Matthews, 1995) as well as postcommute tension and depression (Matthews et al., 1991). High DIS drivers show greater impaired lateral control (Matthews, Sparkes, & Bygrave, 1996) and mistakes and lapses (Kontogiannis, 2006), but they also perceive themselves as less skilled, showing reduced self-driving confidence leading to greater cautiousness, slower speeds, and fewer speeding tickets (Matthews et al., 1991, 1998). Driving Aggression (AG) items focus on reactions to irritation in the driving context and impatience shown as a result of impedance from others. Those high on the AG factor demonstrate more dangerous driving patterns, including control errors when overtaking (Matthews et al., 1998); mistakes, lapses, and speeding (Kontogiannis, 2006); and tailgating, confrontation, negative evaluation of other drivers, and minor collisions (Matthews et al., 1991). The Alertness (AL) factor predominantly measures a tendency to monitor the driving situation for hazards. Previous research has found that AL is considerably lower in reliability than the DIS or AG factors (Glendon et al., 1993; Lajunen & Summala, 1995) and has weak predictive relationships with behavioral and emotional outcomes (Matthews et al., 1998). This may partially account for conflicting results in research attempting to link AL with negative driving outcomes. For example, although AL has been associated with reduced collisions (Matthews, Desmond, Joyner, Carcardy, & Gilliland, 1997) and fewer speeding convictions (Matthews et al., 1991), Kontogiannis (2006) failed to find a direct relationship with either speeding convictions or collisions.

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As an alternative, Matthews et al. (1997) revised and extended the item pool of the DBI to develop the Driver Stress Inventory (DSI), which consists of five factors or “dimensions” of stress vulnerability. According to Matthews (2002), Dislike of Driving (DIS), Driver Aggression (AG), and Fatigue Proneness (FAT) are reflective of subjective states of disturbance while driving. Thrill Seeking (TS) represents an enjoyment of danger, and Hazard Monitoring (HM) is consistent with a vigilance to danger while driving. The DSI is believed to be more predictive of negative driving outcomes consistent with ¨ z, O ¨ zkan, and stress than general personality constructs. O Lajunen (2010) confirmed the five-factor solution of the DSI among Turkish drivers, and regression analysis showed that DIS and TS predicted speeding, whereas AG, DIS, and low HM were related to accident involvement. Matthews (2001) also reported that AG, TS, and low DIS were related to speeding and self-reported violations, and that AG, TS, DIS, FAT, and low HM were linked to higher rates of unintentional errors. However, some inconsistencies have ¨ z et al. did find negative outcomes from DIS, been noted. O AG, and FAT that would be expected in comparison to the work of Matthews and colleagues, but they found that TS was related to less “dangerous” activity in the sense of slower driving and that HM was related to greater collisions. They argued that other factors, such as unique sample makeup, risk perception, and hazard detection, might account for these differences, which would be consistent with the transactional model on which the DSI is based. One issue with using the DBQ and DSI is the fact that the factors overlap with other driving personality constructs, most notably aggression and thrill seeking of sensation seeking scales. Although these legitimately appear to represent components of driver stress within a transactional model, caution needs to be exercised when examining trait driver stress in combination with tools more narrowly designed to measure related constructs to avoid inflated relationships due to item overlap. Another issue is that age differences have been found in both total and subfactor scores of driver stress, with older drivers typically showing lower levels of stress (Gulian, Matthews, et al., 1989; Langford & Glendon, 2002). Given that age has also been negatively linked to several driving outcomes, including aggression, speeding, riskiness, and collisions (Hennessy & Wiesenthal, 2002; Waylen & McKenna, 2008), oversampling of young participants or failure to control for age may exaggerate the negative impact of stress on driving. A similar case could be made for gender due to the fact that men are also highly represented in dangerous driving activities and have been concurrently found to show higher levels of some components of driver stress, such as driving aggression and irritation when overtaking (Matthews et al., 1999). Hence, the

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disproportionate inclusion of male drivers in research could overstate the link between components of drivers stress, such as aggression, and negative outcomes, such as collisions.

2.6. Locus of Control Locus of control (LOC) represents an enduring belief about the source of cause for, or control over, personal behavior and is typically polarized as internal (I) and external (E) (Rotter, 1966). Externals are more likely to find responsibility for their actions in other individuals, luck, chance, or situational factors beyond their control, which may contribute to lack of caution or fewer precautions to prevent negative outcomes in life. According to Montag and Comrey (1987), externals represent a danger in the traffic environment due to their passive tendencies leading to fewer personal precautions. In contrast, internals tend to attribute their activities more often to stable, internal attributes (skill or effort) and as such also take more active responsibility for their actions and steps to alter negative future outcomes. However, Arthur and Doverspike (1992) argued that internals may represent a greater danger in the driving environment due to an overconfidence and overestimation of their skills or abilities while driving. Overall, LOC has been linked to various driving attitudes and behaviors, although there have been mixed findings. An external locus has been linked with elevated collisions (Lajunen & Summala, 1995), drinking and driving offenses (Cavaiola & DeSordi, 2000), errors (Breckenridge & Dodd, 1991), and intention to commit violations (Yagil, 2001), whereas an internal locus has been associated with increased seat belt use (Hoyt, 1973) and cautious behavior (Montag & Comrey, 1987). However, others have failed to find a link between LOC and collisions (Guastello & Guastello, 1986; Iversen & Rundmo, 2002) or seat belt use (Riccio-Howe, 1991) and determined that different measurement approaches can lead to contrasting results (Cavaiola & DeSordi, 2000). One reason for such inconsistency could be that Rotter’s original I/E scale may be too general to predict situationspecific LOC. In response, Montag and Comrey (1987) developed two separate scales to measure driving-specific internality (DI) and externality (DE) (MDIE), with which they identified a positive relationship between DE, as well as a negative relationship between DI, and collisions. However, using the MDIE, Arthur and Doverspike (1992) and Iversen and Rundmo (2002) failed to find any significant association with collisions. Holland, Geraghty, and Shah (2010) noted that such variation could be due to an overuse of male drivers, who are also more likely to possess an elevated self-bias and, as such, may give responses that make themselves appear less internally responsible for

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collisions, hence masking possible relationships between DI and collisions. Another reason may be that the link between LOC and driving behavior is more indirect than direct. For example, although Guastello and Guastello (1986) did not find a link between Rotter’s I/E and collisions, they did note that a belief about the causes of accidents, or attributional style, may translate to specific activities such as collisions. Furthermore, Holland et al. (2010) found that externals are more likely to possess a negative driving style (e.g., either a “dissociative style” that includes distractibility or an “anxious style” that involves lack of confidence and distress) that may then indirectly impact collisions. Yagil (2001) similarly argued that externality can indirectly influence intentions to commit violations through positive attitudes toward violations. Others have noted that LOC acts as the moderator. According to Gidron, Gal, and Desevilya (2003), the impact of hostility on collisions is moderated by increased DI. In another study, Miller and Mulligan (2002) found that driving LOC altered the outcome of mortality salience on subsequent risky driving behavior. After receiving a mortality salience treatment in which participants were given 15 true/false items designed to stimulate thoughts about their own death, internals indicated decreased, whereas externals reported increased, future risky driving behavior. This suggests that the LOC orientation was accountable for changes in the perception and personalization of mortality-related information in the driving context, in advance of future risk-taking driving behavior. A third reason for the discrepancies could be that the concept of LOC is more varied and multidimensional than originally proposed. In this respect, a single bipolar distinction of control may be insufficient to examine the complexities of driving behavior. Levenson (1981) extended the original dimension of I/E to represent independent orientations of internal, chance, and powerful others. In a similar respect, Lajunen developed a multidi¨ zkan & mensional traffic-specific LOC scale (T-LOC; O Lajunen, 2005a), which asks questions about the source of control for driving outcomes. The four subscales of the TLOC include the Self (similar to Internal), the Vehicle/ Environment, Other Drivers, and Fate (similar to External). ¨ zkan and Lajunen (2005a) Using a Turkish translation, O found that those with an elevated “self” LOC reported elevated ordinary and aggressive violations, errors, and active accidents, which they interpreted within the framework of an elevated self-bias where self-confident drivers downplay the likelihood of negative outcomes for more dangerous driving behavior due to their own ability or skill. ¨ zkan, Using a Swedish translation of the T-LOC, Warner, O and Lajunen (2010) found a five-factor structure with similar external factors (Vehicle/Environment, Other Drivers, and Fate) and two internal factors related to the

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Self (Own Skill and Own Behavior). Own Behavior predicted self-reported speed on 90 km/h roads, supporting the notion that internal aspects of the T-LOC may be a useful alternative in understanding riskier driving activities.

3. THE CONTEXT IN DRIVING OUTCOMES The context has a complex and ongoing impact on the thoughts, feelings, and actions of drivers. For the purpose of this chapter, the driving context includes a combination of physical (e.g., temperature), social (e.g., culture), and temporal (e.g., time urgency) factors on both distal/macro (laws and norms) and proximal/micro (weather and traffic congestion) levels. Part of the challenge in considering context factors in traffic research, particularly those on the proximal level, is the fact that they are fluid and part of an ongoing, and often changing, process. As mentioned previously, context factors are important to understand on their own, but they are best appreciated in interaction with personal factors. It is also important to note that context factors interact with one another, and that issues, events, and experiences from one context can carry over from one environment to impact subsequent environments, often outside of conscious awareness (Hennessy, 2008). Events from the traffic environment can influence drivers in subsequent nondriving settings (Hennessy & Jakubowski, 2007), and drivers can be changed by factors from outside the driving environment (Wickens & Wiesenthal, 2005). In this respect, combinations of contextual variables may be conceptually limitless, although the following represent a small subset of context factors and their recognized impact on driving.

3.1. Traffic Congestion: Impedance, Time Urgency, and “Other” Drivers Perhaps the most recognizable contextual factor in the driving environment is traffic congestion, which extends beyond the number of vehicles to represent the perception and interpretation of that volume. In line with the distinction between density and crowding in environmental psychology, the volume of traffic embodies the physical number of vehicles (or drivers) per unit of space, whereas congestion occurs when drivers believe that there are too many vehicles or too little space at that given time and location. Also, although a greater number of vehicles are likely to lead to perceptions of congestion, it is not necessary nor sufficient. For example, regular commuters, who have adjusted their expectations to fit the predictably slower moving areas during “rush hour,” may not necessarily view a specific commute as congested, especially if traffic flows faster than usual. In contrast, during periods that are typically low in volume, drivers who are forced to

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slow their pace or drive closer to other vehicles than anticipated for some unexpected reason may experience a sense of congestion at that particular time and location. In this respect, negative outcomes are not dependent solely on how many vehicles are present on the roadways, which would explain why congestion, and related outcomes such as stress, can be experienced in areas that have relatively small driving populations rather than solely in large city centers. According to Stokols et al. (1978), one factor that may facilitate the perception of congestion is impedance, which represents the behavioral constraints that occur in traffic due to the distance a driver must travel and the time spent in transit during a given trip. They found that high impedance (longer distances at a slower pace) leads to greater perceptions of congestion, greater physiological arousal, and more negative evaluations of other drivers. Schaeffer, Street, Singer, and Baum (1988) subsequently argued that distance and time are so highly correlated that the average speed of a commute was a better predictor of impedance, which they also found to be related to physiological arousal and decreased performance on postcommute proofreading tasks (identifying errors in spelling, grammar, and punctuation in a written passage). Thus, travel impedance appears to create an aversive condition resulting from blocked goals (i.e., expectations of traveling at a certain speed over a certain distance in a specified time frame) due to constrained movements from other drivers (and their vehicles), which can lead to negative physical, cognitive, and behavioral outcomes. A major consequence of traffic congestion is heightened stress and anxiety. Several studies have found that drivers report greater stress on days when levels of congestion are highest (Gulian, Matthews, et al., 1989; Hennessy & Wiesenthal, 1999). Using predetermined routes that were naturally either high or low in congestion, Van Rooy (2006) found that participants randomly assigned to the highly congested route reported greater stress and negative mood. Similarly, Hennessy and Wiesenthal (1997) had commuters drive their regular daily routes and administered state measures of driver stress while they drove in areas of both high and low congestion during the same trip. They found that state stress was significantly greater in high-congestion than in low-congestion conditions, but it was moderated by trait driver stress susceptibility. In studies examining bus drivers who routinely experience a wide spectrum of traffic conditions, Evans and colleagues determined that higher congestion was associated with elevated stress but was exaggerated by personal factors of lost control (Evans & Carre`re, 1991) and learned helplessness (Evans & Stecker, 2004) in such conditions. Traffic congestion can also increase anger and aggression in drivers. Deffenbacher et al. (2003) found that anger predicted aggression in simulated driving situations in which participants were impeded by other traffic. Using

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audio recorders, Underwood et al. (1999) had participants document various experiences from their commute and found that congested encounters were linked to elevated anger during that commute. Gulian, Glendon, Davies, Matthews, and Debney (1989) determined that a large proportion of highway drivers in the United Kingdom frequently experienced irritation in traffic congestion, and that the source of frustration and aggression they reported was typically “other drivers.” Hennessy and Wiesenthal (1999) confirmed this in Canadian drivers interviewed via cellular telephones while engaged in actual low- and highcongestion conditions, where state aggression toward other drivers occurred more frequently in high congestion. This is in line with the notion by Shinar (1998) that roadway aggression may be explained by the frustration aggression hypothesis, in which frustration is more typical when the actions of other drivers block or impede an intended goal. This frustration in turn increases the likelihood of aggression (depending on personal factors, past experience, attributions of others’ actions, and anticipation of outcomes). Several studies have supported the proposition that frustration and irritation from other drivers can lead to driver aggression (Bjo¨rklund, 2008; Hennessy & Wiesenthal, 2001b; Lajunen & Parker, 2001; McGarva, Ramsey, & Shear, 2006). Interestingly, traffic congestion has also been shown to have an enduring impact outside the actual driving environment. Spillover effects have been detected, where congestion-induced elevations in driver stress subsequently impact postdriving outcomes. For example, traffic congestion has been linked to performance decrements in workplace tasks, such as greater errors in proofreading (Schaeffer et al., 1988) and greater time needed to complete simple visual spatial assignments (Hennessy & Jakubowski, 2007), as well as disturbances in interpersonal factors, including increased levels of workplace aggression during that workday (specifically, obstructionism and expressed hostility among men; Hennessy, 2008) and negative evaluations of unqualified job candidates (Van Rooy, 2006). Hennessy (2008) proposed that unresolved stress from the traffic environment likely continues to unconsciously influence and intensify subsequent reactions to stressors experienced after a commute ends, even though the immediate emotional effects from driving dissipate quickly postcommute. An important consideration in interpreting the impact of congested conditions is the confounding issue of time urgency. For many, the delays caused by other drivers can alter the economic or social experience of time, particularly during “rush hour.” Time pressure or time urgency can modify the perception of traffic events, flow, and actions of other drivers, subsequently increasing stress, irritation, frustration, negative affect, and aggression (Evans & Carre`re, 1991; Hennessy, Wiesenthal, & Kohn, 2000;

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Koslowsky, 1997; Lucas & Heady, 2002; Neighbors et al., 2002). Everyday hassles, such as time pressure and adverse driving conditions, typically have an additive effect, in which the influence of one event can add to the severity of another. According to Shinar and Compton (2004), driver aggression increases in frequency during periods of high time value (rush hour) even after controlling for traffic volume. In addition, Lucas and Heady (2002) discovered that regular commuters with greater flextime in their work schedule showed less perceived time urgency and resulting driver stress. Clearly, a hurried pace and lifestyle, a focus on time issues, and the escalated potential for blocked goals have the capacity to exaggerate the impact of congestion on negative driving outcomes. Interpretations of research on traffic congestion, however, must also be qualified by methodological considerations. Like many contextual factors, it is difficult to accurately measure effects of traffic congestion outside of the actual driving experience. The use of hypothetical situations or participant recall of previous trips (even those recently completed) may be problematic because of memory issues, social desirability, experimenter bias, and expectancy effects. However, there are still concerns of control and predictability of real-time traffic events that can alter in situ evaluations of traffic congestion, such as police presence, weather effects, or construction. Also, simulated driving events are limited by the degree they are considered or experienced as “real” by participants, particularly in light of the perceptual nature of congestion in comparison to volume. Nonetheless, there is a degree of consistency across various methods that support the relevance of traffic congestion in negative driving outcomes.

3.2. Physical Environment Previous research has established a greater risk of collisions due to adverse weather, including rain, snow, and fog (Andrey, Mills, Leahy, & Suggett, 2003), but many drivers attempt to compensate for such conditions by driving more cautiously, such as reducing speed and increasing distance between vehicles (de Waard, Kruizinga, & Brookhuis, 2008; Harris & Houston, 2010). This is in line with the zero risk theory (Na¨a¨ta¨nen & Summala, 1976), which holds that drivers adapt to risk (i.e., driving more slowly) to the point that their subjective risk approaches zero. However, Kilpela¨inen and Summala (2007) argued that many are not accurate in predicting risks during poor weather and subsequently do not adjust their driving behavior properly, particularly on slippery roads. One possible problem is that although individual motivation might be geared toward increasing personal safety, perception may be impacted by the conditions (e.g., inaccurate interpretations of distances, speed of traffic, and severity of conditions), leading to unintended risky

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behavior, such as speeds that are too fast for the conditions and reduced headway between vehicles (Caro, Cavallo, Marendaz, Boer, & Vienne, 2009). Cognitive workload may also be impacted during adverse weather conditions because drivers must focus on slower moving traffic and deal with reduced visibility, thus decreasing their capacity to estimate safety margins and increasing strain (Cavallo, Mestre, & Berthelon, 1997). Hill and Boyle (2007) also found that adverse weather and limited visibility can lead to elevated stress levels, which have been independently connected to increased riskiness and collisions. Heat is another environmental factor that can impact drivers. Heat may be a source of psychological stress and physiological arousal that alters cognitions, emotions, and behavior. Using a traffic simulator, Wyon, Wyon, and Norin (1996) discovered that heat impacted vigilance, where drivers in hot conditions missed a greater number of signals to press a pedal. Heat has also been linked to increased aggressive behavior in drivers. Kenrick and MacFarlane (1986) had a confederate vehicle cause delays at a green light and found increased horn honking among those with open driver-side windows (implying the lack of airconditioning and “hot” drivers) during periods of higher temperatures. However, it should be noted that the impact of high temperatures may be qualified by confounding factors of humidity, wind, and air pressure, which can alter the experience of “heat.” Similarly, duration of temperature needs to be considered given that prolonged heat will have different outcomes than those resulting from temporary or fluctuating heat. This is highlighted in research on the heateaggression hypothesis that suggests a curvilinear relationship, where escape may be a more preferable behavior selection than aggression at prolonged extreme temperatures (Bell & Fusco, 1989). It is also important to note that negative outcomes from heat are typically instigated through some social contact or interaction. The target of heat-related aggression is typically others perceived as a source of irritation, frustration, or wrongdoing. The physical environment may also have beneficial effects on driving outcomes in that pleasant sceneries may favorably impact cognitions, attitudes, affect, and behavior. Although evaluations and perceptions of scenery are subjective, consistent patterns have been found, including a greater general preference for natural as opposed to built urban scenery (Kaplan & Kaplan, 1982). This has been confirmed in the traffic environment, in which drivers report that undeveloped landscapes are less cluttered, as well as more pleasant, useful, and attractive (Evans & Wood, 1980). Antonson, Ma˚rdh, Wiklund, and Blomqvist (2009) reviewed existing literature related to the effect of roadside scenery on driving behavior and noted that varied and vegetated landscapes can have a beneficial impact. Such landscapes may increase curiosity, decrease boredom, and

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help maintain focus through anticipation of upcoming threats or challenges. Alternatively, common, predictable, or homogeneous landscapes may lead to monotony, fatigue, and risk. They found that those who traveled a vegetated route in a simulator drove slower and closer to the center of the road than those who traveled an open landscape. However, they also found greater stress, which may be explained by a greater need for vigilance due to a more rapidly changing scenery on an unfamiliar roadway. Alternatively, Parsons, Tassinary, Ulrich, Hebl, and Grossman-Alexander (1998) measured autonomic stress in participants who experienced mild stressors before and after viewing driving scenarios that varied in degree of roadside vegetation. They found that viewing the more vegetated environments did lead to decreased stress responses and increased recovery from that stress. Similarly, Cackowski and Nasar (2003) found that those who viewed routes with more roadside vegetation showed greater subsequent frustration tolerance by spending greater time attempting to solve an insolvable puzzle task. These findings highlight the relevance of the physical environment in understanding the personesituation interaction of drivers.

3.3. Driving Culture and Norms Culture represents shared norms, values, traditions, and customs of a group that typically define and guide appropriate and inappropriate attitudes and behaviors. These can occur on a macro level (e.g., national customs and religious holidays) or a more micro level (e.g., family traditions and peer activities). Driving behavior and driving style should be influenced by these cultural processes, given that the driving environment is a social context with very distinct rules and norms that are transmitted between road users across time and generations. Also, although driving laws, licensing procedures, road types, driving styles, and actual driving behaviors will vary regionally and internationally, dangerous and risky driving practices occur universally. Attitudes about driving and personal driving styles are largely learned, which includes influence from parents, peers, media, and other drivers regarding the overall riskiness of driving, as well as the probability of experiencing negative outcomes (Hennessy, Hemingway, & Howard, 2007; Shope & Bingham, 2008). Using Ajzen’s (1985) theory of planned behavior as a framework, Elliott, Armitage, and Baughan (2007) found that subjective norms (the perceived pressure or acceptance of others toward a behavior) were associated with elevated speeding intentions, which subsequently predicted both self-reported and observed speeding behavior in a simulator. In essence, the normative belief that there is a consensus or commonality to unsafe driving behavior may serve as a justification for its personal adoption (Forward, 2009).

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Parents represent one potential source of cultural and normative influence on driving behavior, particularly for young drivers. Parental influence on driving begins early in life, well before formal “training years,” through modeling of driving styles, attitudes about safety, reactions to other drivers, and respect for traffic laws (Summala, 1987). Familial models that validate recklessness often encourage riskiness of young drivers (Taubman-Ben-Ari, 2008). Bianchi and Summala (2004) further proposed that parental influence may impart from genetic dispositions that guide personal tendencies of parent drivers, such as sensation seeking or attention, which are then demonstrated to their children. Research has consistently shown that parental attitudes and activities outside the driving environment, particularly lenience of restrictions and control, can impact driving behavior and style of young drivers (Hartos, Eitel, Haynie, & Simons-Morton, 2000). Shope, Waller, Raghunathan, and Patil (2001) found that parental monitoring, nurturing, and connectedness in 10th grade of high school were subsequently linked to lower rates of serious offenses (alcohol related, speeding, and reckless driving) and crashes (single vehicle, at fault, and alcohol related), whereas lower monitoring, nurturance, connectedness, and a greater lenience toward drinking had the opposite effect. Similarly, Prato, Toledo, Lotan, and Taubman-Ben-Ari (2010) determined that risk indexes for young drivers were lower for those whose parents were actively involved in monitoring their child’s driving behavior, whereas lack of supervision exaggerated existing dangerous driving tendencies in their child, such as sensation seeking, increasing overall risk. Another important source of cultural and normative driving influence is the media, which is prone to promote danger and risk over safety as a “normal” part of driving. Although the media has the potential to help promote a culture of driving safety, in many cultures, television and movies glorify and promote speeding, risk taking, and dangerous driving practices as acceptable or even admirable (Hennessy et al., 2007), particularly for young male drivers. Consistent with social and cognitive learning theories, one primary mechanism by which behavior is acquired is through observing and imitating others. By placing emphasis on the consequences of others’ actions, observation serves as a vicarious learning experience (Bandura, 2001). Hennessy et al. found that speeding, lane violations, and near collisions were elevated in a simulator among drivers previously exposed to a short movie scene of dangerous driving. It is possible that watching media portrayals that endorse driving that is competitive, performed at excessive speeds, or otherwise unsafe increases viewers’ perceived acceptability of such actions and reduces the expectancy of a tragic outcome, which then increases the likelihood they will engage in dangerous driving themselves.

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4. CONCLUSIONS AND FUTURE DIRECTIONS This chapter highlighted the importance of personal and social influences on driving outcomes. Each driver brings a unique and rich experience base, skill set, expectation, interpretation, and reaction to the distinctive state driving context, yet research has been successful in identifying consistent patterns that can heighten the likelihood of negative driving outcomes, only a fraction of which were presented here. The purpose of such research is to concurrently understand problematic aspects within the person and the context to ultimately improve traffic safety. However, there are still many more factors in this process to be discovered. One future focus could be on understanding aspects that influence more positive or safe driving emotions and actions. For example, Peˆcher et al. (2009) found that listening to happy music decreased mean speed and hard shoulder deviations, suggesting that positive emotions could ultimately distract drivers from dangerous activi¨ zkan and Lajunen (2005b) argued ties. Furthermore, O that promotion of polite driving through media sources could be a means of reducing aggressive driving. Likewise, psychology often emphasizes negative outcomes, sometimes at the expense of characteristics that can improve or increase more appropriate consequences. Anger, aggression, and revenge have been examined more frequently than their constructive counterpart, forgiveness. It is possible that understanding the process by which drivers forgive transgressions of others in the traffic context (rather than become agitated, ruminate, and harm others) may ultimately hold greater long-term benefit in helping to improve driver interactions (Moore & Dahlen, 2008). Another direction could be increased expansion of the personesituation interaction to include the vehicle. Whereas safety issues surrounding the vehicle have been the focus of engineering and human factors, traffic psychology as a whole has been less inclined to incorporate such aspects into research. The vehicle is more than just the means of transporting individuals; rather, it is part of a driver’s life space. Issues such as comfort, layout of instruments, safety features, overall condition of the vehicle, or technology can alter the driving experience variably across situations. In many instances, the vehicle becomes an extension of the driver and his or her space, with personal meaning that can lead to protection and defense (Marsh & Collett, 1987). It can also provide a degree of anonymity from others that allows expression of emotions and actions not typical in other situations (Li et al., 2004). In this respect, a full understanding of the transactional aspects of driving may not be possible without inclusion of the vehicle.

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Finally, there is a need for continued globalization of traffic research. Although current research has begun to identify those aspects that are unique and those that are consistent across cultures, this is only a start. This is particularly pressing in light of the recent emphasis on the role of traffic psychology in helping create a driving safety culture, with a concentration on establishing values, attitudes, and behaviors that permeate across a society that favors safe over unsafe driving practices as the expectation and norm for all. For example, as laws have changed and seat belt use has become an expectation, seat belt use has increased dramatically during the past few decades in many countries (European Traffic Safety Council, 2006). In such instances, successive generations come to view seat belt use as normative, and non-users are explicitly and implicitly pressured to comply more frequently. As has been highlighted in this chapter, within the personesituation interaction, an enduring alteration in cultural or normative expectations for safety should have widespread national and international impact on individual driving outcomes; hence, pervasive changes hold great potential for improved global driver safety.

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Matthews, G., Tsuda, A., Xin, G., & Ozeki, Y. (1999). Individual differences in driver stress vulnerability in a Japanese sample. Ergonomics, 42, 401e415. Maxwell, J. P., Grant, S., & Lipkin, S. (2005). Further validation of the Propensity for Angry Driving Scale in British drivers. Personality and Individual Differences, 38, 213e224. McGarva, A. R., Ramsey, M., & Shear, S. A. (2006). Effects of driver cell-phone use on driver aggression. Journal of Social Psychology, 146, 133e146. Mesken, J., Hagenzieker, M. P., Rothengatter, T., & de Waard, D. (2007). Frequency, determinants, and consequences of different drivers’ emotions: An on-the-road study using self reports, (observed) behaviour, and physiology. Transportation Research Part F: Traffic Psychology and Behaviour, 10, 458e475. Millar, M. (2007). The influence of public self-consciousness and anger on aggressive driving. Personality and Individual Differences, 43, 2116e2126. Miller, R. L., & Mulligan, R. D. (2002). Terror management: The effects of mortality salience and locus of control on risk-taking behaviors. Personality and Individual Differences, 33, 1203e1214. Montag, I., & Comrey, A. L. (1987). Internality and externality as correlates of involvement in fatal driving accidents. Journal of Applied Psychology, 72, 339e343. Moore, M., & Dahlen, E. R. (2008). Forgiveness and consideration of future consequences in aggressive driving. Accident Analysis and Prevention, 40, 1661e1666. Na¨a¨ta¨nen, R., & Summala, H. (1976). Road user behavior and traffic accidents. Amsterdam: North Holland. Neighbors, C., Vietor, N. A., & Knee, C. R. (2002). A motivational model of driving anger and aggression. Personality and Social Psychology Bulletin, 28, 324e335. Oltedal, S., & Rundmo, T. (2006). The effects of personality and gender on risky driving behaviour and accident involvement. Safety Science, 44, 621e628. ¨ zkan, T., & Lajunen, T. (2010). Professional and non-profes¨ z, B., O O sional drivers’ stress reactions and risky driving. Transportation Research Part F: Traffic Psychology and Behaviour, 13, 32e40. ¨ zkan, T., & Lajunen, T. (2005a). Multidimensional Traffic Locus of O Control Scale (T-LOC): Factor structure and relationship to risky driving. Personality and Individual Differences, 38, 533e545. ¨ zkan, T., & Lajunen, T. (2005b). A new addition to DBQ: Positive O Driver Behaviours Scale. Transportation Research Part F: Traffic Psychology and Behaviour, 8, 355e368. Parker, D., Lajunen, T., & Summala, H. (2002). Anger and aggression among drivers in three European countries. Accident Analysis and Prevention, 34, 229e235. Parsons, R., Tassinary, L. G., Ulrich, R. S., Hebl, M. R., & GrossmanAlexander, M. (1998). The view from the road: Implications for stress recovery and immunization. Journal of Environmental Psychology, 18, 113e140. Peˆcher, C., Lemercier, C., & Cellier, J. M. (2009). Emotions drive attention: Effects on driver’s behaviour. Safety Science, 47, 1254e1259. Prato, C. G., Toledo, T., Lotan, T., & Taubman-Ben-Ari, O. (2010). Modeling the behavior of novice young drivers during the first year after licensure. Accident Analysis and Prevention, 42, 480e486. Riccio-Howe, L. A. (1991). Health values, locus of control, and cues to action as predictors of adolescent safety belt use. Journal of Adolescent Health, 12, 256e262.

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Social, Personality, and Affective Constructs in Driving

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Chapter 13

Mental Health and Driving Joanne E. Taylor Massey University, Palmerston North, New Zealand

1. MENTAL HEALTH IMPACTS On November 6, 2009, a high court jury in New Zealand found a 50-year-old man guilty of murdering a young newlywed on June 3, 2008, after he allegedly crashed into her car in an attempt to end his life. He had tried to kill himself several times before, including one occasion when he drank aftershave and detergent. On the night of the crash, he left his house after an argument with his wife. He had visited an alcohol store three times that afternoon and was purportedly swaying on his feet before he got into his car. He called his wife at approximately 6 pm, saying he was looking for a large semitrailer truck. Later in the evening, he drove directly at four separate cars, crossing the center line and making no attempt to swerve. He subsequently collided with a car carrying a man and his two young children on their way home from soccer practice, and then he hit the newlywed’s car head-on. She did not regain consciousness and died of significant internal injuries. Emergency service personnel discovered empty beer bottles in his car and a full can of beer propped on his lap after the crash. Suicide by intentional car crash is an extreme example of the effects of mental health on driving. There are many other ways in which mental health can impact driving and, conversely, driving can affect mental health, especially following a motor vehicle crash (MVC). The dynamic nature of the driving environment makes this a challenging area for researchers. Currently, there are two very separate bodies of literature that examine this area: traffic psychology research, which examines the effects of mental health on driving, mostly in an attempt to identify factors that might increase the likelihood of MVCs, and mental health research, which investigates the various psychological consequences of MVCs.

2. THE EFFECTS OF MENTAL HEALTH ON DRIVING Driving is a highly complex process. As information processors in the driving system, drivers must constantly Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10013-X Copyright Ó 2011 Elsevier Inc. All rights reserved.

receive, process, and respond to information derived from a constantly changing environment as well as modulate their internal states. Therefore, they require efficient cognitive and intrapersonal function. Several factors can influence the efficiency of cognitive function and in different ways, which may place drivers and other road users at increased risk of involvement in an MVC. In an attempt to understand the human error causes of MVCs, researchers have studied an exhaustive array of human factors, including mood, aggression, risk-taking behavior, fatigue, stress, age, gender, brain injury, drug-taking behavior, and psychiatric symptoms (Little, 1970; McDonald & Davey, 1996; Shinar, 1978; Taylor & Dorn, 2006). Some of these factors are considered in detail elsewhere in this volume (e.g., see Chapter 12 on social, personality, and affective constructs in driving; Chapter 17 on impaired driving; and Chapter 21 on fatigued driving), so the present material focuses on the relationships between mental health and driving. Much of the research in this area has retrospectively examined the prevalence of psychopathology in MVC victims or conducted laboratory-based investigations of driving with groups identified on the basis of particular mental health characteristics, examining factors that may impair and affect driving ability, such as attention, concentration, memory, vigilance, impulse control, judgment, problem solving, reaction time, and psychomotor control. It has long been suggested that mental health might contribute to road safety, and specifically that those who experience mental health problems, such as psychotic, mood, anxiety, or substance use difficulties, are more likely to be involved in MVCs. Several reviews of this area have been published, although they are somewhat dated (McDonald & Davey, 1996; Metzner et al., 1993; Noyes, 1985; Silverstone, 1988; Tsuang, Boor, & Fleming, 1985). Early studies examined what were then called “accidentprone” drivers and reported on their socially deviant characteristics (Tillman & Hobbs, 1949), as well as noting high levels of alcoholism in this group (Selling, 1940). Crancer and Quiring (1969) found that people with personality 165

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disorders had an accident rate 144% higher than that of a matched control group, whereas it was 49% higher in a group with psychoneurotic disorders, and there was no increased rate compared with controls for a group with schizophrenia. Since these early studies, other researchers have reported higher traffic accident rates among people with alcohol use and personality disorders, particularly antisocial personality disorder (Armstrong & Whitlock, 1980; Dumais et al., 2005; Elkema, Brosseau, Koshick, & McGee, 1970; Selzer, Payne, Westervelt, & Quinn, 1967; Waller & Turkel, 1966; see also the review by Tsuang et al., 1985), as well as higher mortality rates in MVCs for these groups (Rorsman, Hagnell, & Lanke, 1982; Schuckit & Gunderson, 1977). Others have found no such increased accident likelihood for those with psychiatric histories, although these studies are limited by methodological issues such as excluding people with substance-related problems (Cushman, Good, & States, 1990; Kastrup, Dunpont, Bille, & Lund, 1977). Laboratory- or field-based studies have reported mixed findings, such as slowed driving speed and more errors and collisions on a simulator for a sample with schizophrenia compared with a matched control group (St. Germain, Kurtz, Pearlson, & Astur, 2004) and marked problems with psychomotor performance in a sample of psychiatric outpatients (de la Cuevas Castresana & Alvarez, 2009). However, the relationship between mental health and driving is complex, and simply examining the differential rates of MVC in those with or without various forms of psychopathology does not provide evidence that psychopathology plays a causal role in accidents. Accidents are often preceded by stressful life events, such as problems in interpersonal relationships, as well as driving under the influence of alcohol and other substances (Noyes, 1985). Various mechanisms have been proposed to explain the relationship between various types of mental health difficulty and MVCs. McDonald and Davey (1996) provide a detailed review of these factors, which are briefly outlined here.

2.1. Alcohol and Substance Use Problems The physiological, cognitive, and behavioral effects of alcohol, such as slowed reaction time, problems with coordination and attention, and lowered behavioral inhibition, clearly increase MVC risk, and this risk is greater for those who have a pathological problem with alcohol, such as alcohol abuse or dependence (which may also be comorbid with other psychopathology, such as antisocial personality and conduct disorder traits, depression, and post-traumatic stress disorder; del Rio & Alvarez, 2001; del Rio, GonzalezLuque, & Alvarez, 2001; McDonald & Davey, 1996; McMillan et al., 2008; Stoduto et al., 2008). Several epidemiological studies have examined psychopathology

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in those convicted of driving under the influence of alcohol and have reported lifetime rates of alcohol use disorder of 41e91% and other drug use disorder of 26e40% (de Baca, Lapham, Skipper, & Hunt, 2004; Lapham, de Baca, McMillan, & Lapidus, 2006; Lapham et al., 2001; McCutcheon et al., 2009; Palmer, Ball, Rounsaville, & O’Malley, 2007; Shaffer et al., 2007). Psychotropic medication use is also relevant for drivers with mental health problems, although most of these medications are generally considered not to interfere with driving performance unless alcohol is also taken or the drugs are being abused (Hole, 2007, 2008).

2.2. Personality Traits and Disorders In some cases, such as alcohol abuse, the symptoms of the problem can directly influence vehicle control and therefore contribute to risky or unsafe driving. In others, however, such as personality disorders, the link between the mental health problem and driving behavior is less clear, and is probably influenced by a combination of factors, including personality traits such as aggression, hostility, impulsivity, and sensation-seeking. These latter traits might have indirect effects on MVCs through driving behaviors such as errors and violations (Donovan & Marlatt, 1982; Shaw & Sichel, 1971; Wilson & Jonah, 1988; Zuckerman & Neeb, 1980). Several studies have attempted to predict accident involvement from a variety of personality factors and driving behaviors (Kim, Nitz, Richardson, & Li, 1995; ˚ berg, 1999; Norris, Matthews, & Riad, 2000; Rimmo¨ & A Ulleberg & Rundmo, 2003; West, Elander, & French, 1993). Su¨mer (2003) developed a fairly sophisticated contextual mediated model of personality and behavioral predictors of MVCs that distinguished distal and proximal factors in 295 professional drivers in Turkey. Psychological symptoms, including anxiety, depression, hostility, and psychoticism, had direct effects on aberrant driving behaviors, and dysfunctional drinking had an indirect effect (via aberrant driving behaviors) on the number of accidents (Su¨mer, 2003). Other researchers have also supported the use of multiple personality predictors of unsafe driving that are related in different ways to different aspects of driving behavior (Dahlen & White, 2006).

2.3. Anger In addition to being investigated in research on personality traits and driving, anger in the driving situation has increasingly been studied during approximately the past decade, fuelled by interest in the phenomenon of road rage (Galovski, Malta, & Blanchard, 2006; Hole, 2007). Research has focused on developing instruments to measure driving anger that can distinguish aggressive driving behavior and

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Mental Health and Driving

thoughts as well as risky, nonaggressive behavior (Deffenbacher, Lynch, Oetting, & Swaim, 2002; Deffenbacher, Oetting, & Lynch, 1994; Deffenbacher, White, & Lynch, 2004; DePasquale, Geller, Clarke, & Littleton, 2001; Lajunen, Parker, & Stradling, 1998). Others have explored associations of driving anger and aggression with psychopathology and have found that aggressive drivers are more likely to meet criteria for various mental health problems, particularly intermittent explosive disorder, current or past alcohol or substance abuse or dependence, antisocial and borderline personality disorders, conduct disorder, and attention-deficit/hyperactivity disorder (Fong, Frost, & Stansfeld, 2001; Galovski, Blanchard, & Veazey, 2002; Malta, Blanchard, & Freidenberg, 2005). Victims of road rage are also at risk of developing mental health problems (Smart, Ashbridge, Mann, & Adlaf, 2003). Despite the tendency for angry and aggressive drivers to report more risky driving behaviors, most research has found no significant correlations between measures of driving anger and accident involvement (Sullman, 2006; Van Rooy, Rotton, & Burns, 2006), although a few have found overall anger, in addition to other variables, to significantly predict crash involvement (Sullman, Gras, Cunill, Planes, & Font-Mayolas, 2007). Driving anger and aggression is generally considered to be a complex problem that depends on the characteristics of the driver and the situation (Lajunen & Parker, 2001; Shinar, 1998).

2.4. Depression Rates of depression in accident samples have not been clearly determined given the overlap in some cases of low mood and depression with self-harm and suicide (McDonald & Davey, 1996) and the use of self-report questionnaires instead of diagnostic methods to determine depression (Hilton, Staddon, Sheridan, & Whiteford, 2009). Other research on depression and driving has taken place in the broader context of evaluating the influence of emotions on driving, which may be related to various factors, including prejourney emotions and circumstances, the traffic situation while driving, and thoughts that arise during travel (Ban˜uls Egeda, Carbonell Vaya, Casanoves, & Chisvert, 1997; Groeger, 1997; Levelt, 2003). For example, Mesken, Hagenzieker, Rothengatter, and de Waard (2007) found that emotions while driving were related to emotional traits as well as traffic events such that anger was associated with other-blame and events affecting progress, and anxiety was associated with situation-blame and events affecting safety.

2.5. Anxiety Discussions in the general driving literature that have related anxiety to driving have come from broader studies

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of personality typologies and disorders (Evans, 1991; Foot & Chapman, 1982; Heimstra, Ellingstad, & DeKock, 1967; Little, 1970; Loo, 1979; Shinar, 1978; Shoham, Rahav, Markovski, Chard, & Baruch, 1984; Silverstone, 1988; Wilson & Greensmith, 1983), and stress (Gulian, Glendon, Matthews, Davies, & Debney, 1988, 1990; Heimstra, 1970; Hentschel, Bijleveld, Kiessling, & Hosemann, 1993). Some research suggests that anxiety necessarily impairs driving. Shoham et al. (1984) used a combination of personality variables to predict the likelihood of recidivist traffic accident involvement, and they reported that drivers characterized as anxious manifested high internalization of traffic norms and high levels of anxiety and “were found to have lowered bio-psychogenic [sic] control over the basic mechanisms required for driving” (p. 184). Other authors have considered that anxiety affects driving in a more complex manner and may also have some facilitative or positive effects that are specific to driver behavior and driving skills (Kottenhoff, 1961; O’Hanlon, Vermeeren, Uiterwijk, van Veggel, & Swijgman, 1995; Payne & Corley, 1994; Silverstone, 1988). For example, a moderate amount of anxiety may enable the driver to carry out all of the basic skills required for driving, as well as to pay sufficient attention to potential hazards so that the appropriate action can be taken if required (Walklin, 1993), whereas high levels of anxiety could interfere with driving performance and increase the risk of an MVC through errors, indecision, and hesitation (Carbonell, Banuls, Chisvert, Monteagudo, & Pastor, 1997; Silverstone, 1988; Walklin, 1993). Yinon and Levian (1988) found that anxiety about being in the presence of other drivers leads to the division of attention between self- and task-relevant stimuli, although the focus on threat appraisals in anxiety disorders may actually explain why the MVC rate is no higher than population norms for this group (McDonald & Davey, 1996). Indeed, Taylor, Deane, and Podd (2007) found that anxious drivers made more errors than controls in an on-road driving test, but there were no differences in MVC history.

2.6. Attention-Deficit/Hyperactivity Disorder During approximately the past decade, researchers have started to investigate the influence of adult attention-deficit/ hyperactivity disorder (ADHD) on driving behavior, particularly because of the features of difficulties with attention and impulsivity that characterize this condition. ADHD has been found to present risks to safe driving in terms of traffic violations, license suspensions, less safe driving practices, more driving errors, and crashes in simulator performance as well as on the road (Fischer, Barkley, Smallish, & Fletcher, 2007; Nada-Raja et al., 1997). These risks may be partly accounted for by the fact that ADHD can also be associated with other risk factors, such as tendencies to frustration and aggression (Richards, Deffenbacher,

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Rose´n, Barkley, & Rodricks, 2006), as well as substance use and conduct problems (Jerome, Segal, & Habinski, 2006). Treatment with long-acting methylphenidate improves the driving performance of adolescents and adults with ADHD (Barkley, Murphy, O’Connell, & Connor, 2005), although it is unknown whether this also reduces their risk of MVCs or traffic violations (Barkley, 2010).

2.7. Stress Although much research has examined the relationship between various types of mental health difficulty and driving performance as well as MVCs, another strand of research has investigated the role of stress as a more general problem linked to mental health that might make people with (or without) psychopathology more vulnerable to MVCs. One difficulty in this research is that stress can act both as a cause and as a consequence of mental health problems, so studies that do not control for psychopathology are limited (McDonald & Davey, 1996). Matthews (2001) attempted to identify the informationprocessing functions that mediate the effects of stress on driving performance impairment in developing a transactional model of driver stress (Matthews et al., 1998). Stress variables used have been based on factor analyses of the Driving Behaviour Inventory (Glendon et al., 1993; Gulian, Matthews, Glendon, Davies, & Debney, 1989) and its revision, the Driver Stress Inventory (Matthews, Desmond, Joyner, Carcary, & Gilliland, 1997), which are considered to represent vulnerabilities to different types of stress outcome, including aggression and anxiety. Effects of driver stress on driving performance can depend on the nature of the driver’s stress reactions (e.g., appraisal of the demands of the traffic environment, including other drivers; appraisal of personal competence; and coping strategy), the traffic environment, and the demands of the driving task (Matthews, 2001; Matthews, Emo, & Funke, 2005; Matthews et al., 1997, 1998, 1999). The approach used to cope with stress has been identified as an important factor influencing the perception of stress and whether it affects driving behavior, and maladaptive coping strategies such as alcohol abuse may increase accident risk (McDonald & Davey, 1996).

3. EFFECTS OF DRIVING ON MENTAL HEALTH Various aspects of driving can also impact mental health. Most notably, involvement in MVCs can have varied mental health effects, ranging from little or no impact to significant and marked difficulties. Several books have been dedicated to this topic (Blanchard & Hickling, 2004; Duckworth, Iezzi, & O’Donohue, 2008; Hickling & Blanchard, 1999; Mitchell, 1997). It is also important to

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note that, in addition to those directly involved and injured in the crash as primary victims, MVCs can also affect (1) those who are uninjured but are still involved in the crash as either participants or witnesses; (2) friends and family who hear descriptions of the event from someone involved; and (3) those involved in dealing with the after effects of MVCs, including police, fire, ambulance, and hospital personnel, as well as those who are charged with preparing medical or legal reports (Mayou, 1997; Mitchell, 1999; Taylor & Koch, 1995).

3.1. Mental Health Consequences of Motor Vehicle Crashes The nature and sheer frequency of MVCs suggests that at least some people are likely to suffer psychological repercussions. Research has demonstrated that people who have been involved in MVCs and other common accidents may manifest chronic psychological dysfunction, even in the context of minimal physical injury and good recovery (Horne, 1993; Pilowsky, 1985). Longitudinal research has demonstrated a variety of psychosocial sequelae in injured MVC survivors, including worsened family or spouse relationships as well as reduced social contact, pleasure from leisure activities, and work capacity (Malt, Hfivik, & Blikra, 1993). Research on the psychiatric consequences of MVCs has documented widespread implications for psychological functioning, including depressive, anxious, and phobic symptoms, as well as multiple disturbances. However, some of these studies have combined MVC victims with victims from a range of industrial and workrelated accidents, making it impossible to clearly determine psychiatric morbidity after MVCs (Culpan & Taylor, 1973; Jones & Riley, 1987; Shalev et al., 1998). Post-MVC research has ranged from examining specific effects such as depersonalization responses (Noyes, Hoenk, Kuperman, & Slymen, 1977) to comprehensive studies of MVC-induced psychopathology. The most common psychological sequelae of MVCs include driving-related fears and avoidance, post-traumatic stress disorder (PTSD), depression, and pain-related syndromes (Blanchard, Hickling, Taylor, Loos, & Gerardi, 1994; Goldberg & Gara, 1990; Koch & Taylor, 1995; Kuch, Cox, Evans, & Shulman, 1994; Malt, 1988; Mayou, 1992; Mayou, Bryant, & Duthie, 1993). For victims with multiple injuries, depression and anxiety are particularly common (Mayou et al., 1993). Approximately 40% of MVC victims suffer comorbid conditions, such as major depression, panic disorder, specific phobia, eating disorder, substance abuse, and personality disorder (Blanchard et al., 1994). Furthermore, blaming others for the crash has been found to be associated with higher levels of psychological distress and lower psychological well-being for both passengers and

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Mental Health and Driving

drivers (Ho, Davidson, Van Dyke, & Agar-Wilson, 2000). However, consistent prevalence data for the various psychological outcomes have been elusive, likely due to the impact of several methodological issues, such as varying definitions of terms, the use of samples that are not generalizable to MVC victims (e.g., medicolegal samples, victims referred for treatment, and hospital or primary care attendees), and lack of consideration of injury severity (Blaszczynski et al., 1998). Significant psychological problems have been identified even among victims of minor traffic crashes. For example, one longitudinal study reported that 4 months after a minor MVC (i.e., outpatient treatment only, with no hospitalization or head injury), 13% of 39 participants scored above the cutoff for a positive PTSD diagnosis on a self-report measure, 36% reported symptoms of anxiety, and 16% described avoiding using their car, motorcycle, or bicycle (Smith, MacKenzie-Ross, & Scragg, 2007).

3.1.1. Post-Traumatic Stress Reactions The most commonly studied psychological outcomes of MVCs are those involving some kind of trauma response, either in the immediate weeks following the crash or in the longer term, and that may range from subthreshold-level symptoms to full-blown clinical syndromes, such as acute and post-traumatic stress disorder. Both are characterized by problematic experiences of anxiety that can occur following a traumatic event involving “actual or threatened death or serious injury, or a threat to the physical integrity of self or others” and in which the person responds to the event with horror, fear, or helplessness (American Psychiatric Association, 2000, pp. 467 and 471). The symptoms of these trauma responses can include psychologically re-experiencing the trauma (e.g., intrusive thoughts and nightmares), increased physical arousal (e.g., exaggerated startle response and irritability), and persistent avoidance related to the crash (e.g., avoidance of or reluctance to drive and avoiding thoughts or conversations about the crash). A body of research exists that indicates the frequent occurrence of PTSD in MVC victims and its impact on quality of life (Gudmundsdottir, Beck, Coffey, Miller, & Palyo, 2004), and it has been suggested that PTSD thoroughly captures the psychological consequences of MVCs (Burstein, 1989b; Davis & Breslau, 1994; Hickling, Blanchard, Silverman, & Schwarz, 1992; Kuch, Swinson, & Kirby, 1985; Platt & Husband, 1987). Studies have investigated the treatment of post-traumatic responses after MVCs (Blanchard, Hickling, Taylor, et al., 1996; Brom, Kleber, & Hofmann, 1993; Fairbank, DeGood, & Jenkins, 1981; Green, McFarlane, Hunter, & Griggs, 1993; McCaffrey & Fairbank, 1985; Walker, 1981) as well as the complicating nature of PTSD in post-traumatic headache (Davis & Breslau, 1994; Hickling, Blanchard, Schwarz, &

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Silverman, 1992; Hickling, Blanchard, Silverman, et al., 1992) and the nature of psychophysiological responding in MVC-related PTSD (Blanchard, Hickling, & Taylor, 1991). However, it has been noted that avoidant symptoms may obscure the identification of PTSD reactions in MVC victims, and PTSD may go unrecognized for some time after the accident (Burstein, 1989a, 1989b; Epstein, 1993). Reported incidence rates of MVC-related PTSD vary considerably across studies, largely due to methodological differences, especially in terms of the sample included and approach used to ascertain PTSD. Table 13.1 provides an overview of studies that have examined adult MVC samples using well-validated structured diagnostic interviews or self-report measures that represent diagnostic criteria. Investigations have differentiated between PTSD among respondents involved in “serious” MVCs, in which there was some degree of physical injury requiring hospitalization, and “non-serious” MVCs, or those not resulting in bodily injury or involving the subjective experience of psychological injury. However, because victims of both serious and non-serious MVCs have been found to experience PTSD, these distinctions may have little utility. Table 13.1 demonstrates this phenomenon, whereby research using seriously injured victims has found a range of rates of PTSD (from 1 to 100%), as have studies that have utilized victims sustaining relatively minor injuries (15e50%). Although there appears to be a larger range for serious MVCs, the overlap in incidence rates is substantial and may be due to definitional (e.g., different criteria for severity of injury, diagnosis, and time since the MVC) and methodological differences (e.g., whether the sample was seeking treatment or not). The various studies shown in Table 13.1 indicate that the rate of PTSD for seriously injured victims 1 month following an MVC is approximately 25e56% (Blanchard et al., 1994; Blanchard, Hickling, & Barton, 1996; Blanchard, Hickling, & Taylor, et al., 1996; Chubb & Bisson, 1999; Feinstein & Dolan, 1991; Ursano et al., 1999). Rates at 3e6 months decrease to 7e30% (Bryant, Harvey, Guthrie, & Moulds, 2000; Ehlers, Mayou, & Bryant, 1998; Hamanaka et al., 2006; Harvey & Bryant, 1998; Mayou et al., 1993; Ursano et al., 1999; Yasan, Gu¨zel, Tamam, & Ozkan, 2009), and they decrease to 5e32% at 12 months (Blanchard, Hickling, Barton, et al., 1996; Blanchard, Hickling, Taylor, et al., 1996; Ehlers et al., 1998; Green et al., 1993; Koren, Arnon, & Klein, 1999; Mayou et al., 1993). Increasingly, studies have documented MVC-related PTSD in children, some of which have reported cases with onset as young as 2 years (Jaworowski, 1992; Jones & Peterson, 1993; McDermott & Cvitanovich, 2000; Thompson & McArdle, 1993). Symptomatology in such cases has included reliving the MVC through nightmares, conduct difficulties, separation anxiety, enuresis, fear of the dark, trauma-specific fears, sleep disturbance, violent play,

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reluctance to cross roads or travel by car, and a preoccupation with road safety (Canterbury & Yule, 1997; Jones & Peterson, 1993; Taylor & Koch, 1995; Thompson & McArdle, 1993). Studies using approaches that permit diagnostic assessment have typically found higher rates of PTSD in

Key Variables to Understand in Traffic Psychology

children than have studies using other criteria to determine the presence of PTSD, such as cutoff scores on a measure of PTSD-type symptoms (e.g., 12e18% from 3 to 12 months post-MVC; Landolt, Vollrath, Gnehm, & Sennhauser, 2009; Landolt, Vollrath, Timm, Gnehm, & Sennhauser, 2005; Sturms et al., 2005).

TABLE 13.1 Prevalence of PTSD in Adults Following MVCs Using DSM-Based Assessment References

N

Injury Severity Criteria

% PTSD

Time since MVC

Kuch et al. (1985)

30

Medical attention sought

100a

NR

Malt (1988)

107

Hospitalized

1

6 months

Feinstein and Dolan (1991)

48

Accidentally injured

25 15

6 weeks 6 months

Hickling, Blanchard, Silverman, et al. (1992)

20

Post-traumatic headache

75b

NR

b

NR

b

Serious Injury

Epstein (1993)

15

Serious injury

40

Green et al. (1993)

24

Severe injury

25

18 months

Mayou et al. (1993)

188

Multiple injury or whiplash neck injury

7e9 5e11

3 months 12 months

Blanchard et al. (1994)

50

Medical attention sought

46

1e4 months

Blanchard, Hickling, Barton, et al. (1996); Blanchard, Hickling, Taylor, et al. (1996)

158

Medical attention sought

39b 12b

1e4 months 12 months

Ehlers et al. (1998)

967

Emergency department attendees

23.1c c

Mayou et al. (2002) Harvey and Bryant (1998)

71

Hospitalized

3 months

16.5 11c

12 months 3 years

25.4c

6 months

c

Chubb and Bisson (1999)

24

Many physically injured

56.3 37.5c

5 weeks 9 months

Koren et al. (1999)

74

Hospitalized

32b

12 months b

Ursano et al. (1999)

122

Most hospitalized

34.4 25.3b 18.2b 17.4b 14b

1 month 3 months 6 months 9 months 12 months

Bryant et al. (2000)

113

Hospitalized

21c

6 months

Hamanaka et al. (2006) Matsuoka et al. (2008) Yasan et al. (2009)

100 100

Severe injuries In intensive care

c

6 months

8.5 c

1 month

8

c

95

Emergency department attendees

29.8 23.1c 17.9c

3 months 6 months 12 months

Goldberg and Gara (1990)

55

Not resulting in death or major bodily injury

15

M ¼ 15 months

Kuch et al. (1994)

21

Minimal injury and chronic pain

38b

NR

Non-Serious Injury

Kupchik et al. (2007)

60

General health outpatient

c

50

M ¼ 44 months

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171

TABLE 13.1 Prevalence of PTSD in Adults Following MVCs Using DSM-Based Assessmentdcont’d References

N

Injury Severity Criteria

% PTSD

Time since MVC

20

NR

50b

NR

a

Injury Criteria Not Given Hickling and Blanchard (1992) Horne (1993)

7

NR

43

Dalal and Harrison (1993)

86

NR

32

Kuch, Cox, and Direnfeld (1995)

54

NR

22

Chan, Air, and McFarlane (2003)

391

NR

29

M ¼ 2 years M ¼ 2.7 years M ¼ 3.6 years

c

9 months

NR, not reported. a DSM-III. b DSM-III-R. c DSM-IV.

Diagnostic studies have reported higher child PTSD rates both in the short term and in the longer term following an MVC, including up to 4e6 weeks (23e35%; MeiserStedman, Smith, Glucksman, Yule, & Dalgleish, 2008; Stallard, Velleman, & Baldwin, 1998), 3 months (22e25%; McDermott & Cvitanovich, 2000; Scha¨fer, Berkmann, Riedasser, & Schulte-Markwort, 2006), and 6 months (13e19%; Meiser-Stedman et al., 2008; Mirza, Bhadrinath, Goodyer, & Gilmour, 1998). Various predictors of PTSD have been identified in the literature, related to both the early development of the trauma response and whether that response becomes chronic. Predictors of early stage development of PTSD include the presence of acute stress disorder, persistent physical disability, severity of physical injury, a sense of threat to life, dissociation during the crash, low perceptions of coping self-efficacy, and lower perceived social support (Benight, Cieslak, Molton, & Johnson, 2008; Hamanaka et al., 2006; Koren et al., 1999; Matsuoka et al., 2008; Yasan et al., 2009). Factors that have been shown to predict chronic PTSD (symptoms experienced for 1 year or more) include some of the previously mentioned factors, such as early trauma symptoms (including sleep problems), perceived threat, dissociation during the crash, and persistent health problems, as well as other variables, including hospitalization for injuries, persistent financial problems, litigation, female gender, unemployment, emotional problems prior to the crash (including distress from and amount of prior trauma), alcohol abuse, and poor social support before and after the crash (Ameratunga, Tin Tin, Coverdale, Connor, & Norton, 2009; Beck, Palyo, Canna, Blanchard, & Gudmundsdottir, 2006; Blanchard, Hickling, Barton, et al., 1996; Buckley, Blanchard, & Hickling, 1996; Do¨rfel, Rabe, & Karl, 2008; Ehlers et al., 1998; Fujita & Nishida, 2008; Irish et al., 2008; Koren, Arnon, Lavie, & Klein, 2002; Mayou & Bryant, 2002; Mayou, Ehlers, & Bryant,

2002; Mayou, Tyndel, & Bryant, 1997; Murray, Ehlers, & Mayou, 2002). Several cognitive factors, especially cognitive processes, have also been found to maintain and predict PTSD (in some cases to a greater degree than the established predictors noted previously). These cognitive factors include negative interpretations of intrusive memories, rumination, memory disorganization, thought suppression, anger cognitions, and general negative post-traumatic thoughts (Ehring, Ehlers, & Glucksman, 2006, 2008; Ehring, Frank, & Ehlers, 2008; Holeva, Tarrier, & Wells, 2001; Karl, Rabe, Zo¨llner, Maercker, & Stopa, 2009; Murray et al., 2002). A few studies have examined predictors of PTSD development in children who have experienced MVCs, and early trauma symptom severity has been identified as the strongest predictor (Landolt et al., 2005; Scha¨fer et al., 2006), along with severity of MVC-related PTSD in the father. However, variables such as age, gender, injury severity, threat appraisal, prior trauma exposure, prior mental health problems, and maternal MVC-related PTSD have not been found to be significant predictors (Landolt et al., 2005; Meiser-Stedman, Dalgleish, Glucksman, Yule, & Smith, 2009). The presence of nightmares with content that exactly matched the trauma has been found to strongly predict PTSD scores at 2 and 6 months post-MVC, although this finding needs to be replicated with a larger sample (Wittman, Zehnder, Schredl, Jenni, & Landolt, 2010). In line with adult research, studies are beginning to document evidence for the role of various cognitive factors in predicting the development and maintenance of PTSD following MVCs in children. For example, research has examined maladaptive cognitive appraisals such as the meaning of the trauma and trauma symptoms, future vulnerability, rumination, anxiety sensitivity, and the quality of trauma memories (Meiser-Stedman et al., 2009). Information about approaches to treatment of PTSD

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following an MVC is widely available, and readers are referred to the existing literature for more information (Blanchard & Hickling, 2004; Duckworth et al., 2008; Hickling & Blanchard, 1999; Hickling, Kuhn, & Beck, 2008).

3.1.2. Driving-Related Fear, Phobia, and Travel Anxiety In addition to post-traumatic reactions, research has also demonstrated that other types of fear reactions to MVCs are common and can be extremely debilitating (Herda, Ehlers, & Roth, 1993; Kuch, 1997; Kuch, Cox, & Evans, 1996; Kuch, Evans, & Mueller-Busch, 1993). Fear of driving is a diverse experience, ranging from mild driving reluctance to driving phobia as a variant of specific phobia. Research on these phobic fear reactions has focused on avoidance of or reduction in driving, endurance of driving with marked discomfort, and the effect of fear on a person’s lifestyle and everyday functioning. However, variations in terminology and definitions used as well as sampling issues have led to vast inconsistencies in prevalence estimates for problems such as driving phobia, accident phobia, travel phobia, driving reluctance, and phobic travel anxiety. In particular, studies that have used broader criteria in which complete avoidance is unnecessary have reported higher rates (e.g., 60e77%; Hickling & Blanchard, 1992; Kuch et al., 1985) than those that have specified total avoidance for diagnosis (e.g., 2e6%; Blanchard et al., 1994; Hickling & Blanchard, 1999). Driving fear and phobia can also occur in the absence of MVCs but still be severe and have a marked effect on functioning (Ehlers, Hofmann, Herda, & Roth, 1994; Taylor & Deane, 1999, 2000; Taylor, Deane, & Podd, 1999, 2000). Driving phobia is most appropriately considered to be a situational type of specific phobia that is characterized by marked and persistent fear that is excessive or unreasonable, is cued by anticipation of or exposure to driving stimuli, is associated with avoidance of driving stimuli or endurance of such stimuli with considerable anxiety or distress, and has a marked impact on the person’s functioning (American Psychiatric Association, 2000). The content of fear can be much broader than the fear of driving and can relate to various aspects of travel and accident-related stimuli, such as fear of riding in a vehicle as a passenger while having no fear of driving (Koch & Taylor, 1995). Blanchard and Hickling (2004) refer to less phobic forms of driving-related fear as driving reluctance, where the person is able to make essential journeys but avoids nonessential travel or tolerates it with some degree of anxiety. Several reviews of driving-related fear and phobia provide more comprehensive information on this topic, along with information on appropriate interventions (Taylor, 2008; Taylor, Deane, & Podd, 2002).

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Key Variables to Understand in Traffic Psychology

3.1.3. Other Problems Several studies have documented increased rates of depression and other mood disorders following MVCs that may or may not be comorbid with PTSD, with rates of major depression ranging from 6 to 53% 1 year post-MVC (Blanchard et al., 2004; Blanchard, Hickling, Taylor, & Loos, 1995; Dickov et al., 2009; Ehlers et al., 1998). Blanchard, Hickling, Taylor, et al. (1996) also reported that major depression prior to the MVC was a significant predictor of post-MVC PTSD. Depression has been identified as a common consequence of MVCs and one that can overlap with physical effects such as chronic pain and head injury and contribute markedly to functional limitations following a crash (Duckworth, 2008). Substance use disorders have been examined in relation to MVCs, although the mixed findings reported in the literature suggest that longitudinal research is needed to more clearly identify the relationship between substance use and MVC (O’Donnell, Creamer, & Ludwig, 2008). Psychological factors can also contribute in many ways to pain-related syndromes that occur following MVC-related injury (Duckworth et al., 2008).

4. SUMMARY The relationship between mental health and driving is complex. Mental health can have an impact on driving behavior and performance, although the relationships are multifaceted and depend on factors related to the specific nature of the problem, other characteristics of the individual, and specific aspects of the traffic environment and driving situation. However, higher accident rates for those with alcohol-related disorders as well as antisocial personality disorder are relatively consistent findings, although these conditions are also frequently comorbid. The absence of clear information from methodologically sound studies about how mental health affects safe driving can present difficulties for health professionals who are required to make decisions regarding fitness to drive in cases in which mental health is an issue (Knapp & VandeCreek, 2009; Me´nard et al., 2006). The complexity of the relationships involved necessitates that current guidelines focus on the importance of individualized assessment and consideration of factors such as acute illness symptoms as well as side effects of, interactions among, and compliance with medication (Carr, Schwartzberg, Manning, & Sempek, 2010; Land Transport Safety Authority, 2002). Several quite different types of mental health problems have tended to emerge from research on the psychological consequences of MVCs and highlight the diverse and complex types of experiences that might also be influenced by pre-accident mental health as well as the specific nature of the incident and response characteristics.

Chapter | 13

Mental Health and Driving

Although the two fields of traffic psychology and mental health and driving have historically been considered separately, considering them both provides a more comprehensive overview of the role of mental health in driving, particularly in highlighting the ways that mental health might influence driving and be affected by the driving environment.

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Chapter 14

Person and Environment: Traffic Culture ¨ zkan and Timo Lajunen Tu¨rker O Middle East Technical University, Ankara, Turkey

1. PERSON AND ENVIRONMENT: BEHAVIOR AND ACCIDENTS Behavior is a result of a contribution of the person, the situation or environment, and some probabilistic interactive function of person and environment (Lewin, 1952, p. 25). The person is labeled as a human factor component, whereas the situation and/or environment are labeled as vehicle-related factors and road environment in traffic. A human (i.e., road user) is also embedded in a complex multilevel sociocultural and technical environment of traffic. Any outcome, such as an accident, is therefore a result of the contribution of human factor (i.e., road user), environment, and the probabilistic interaction of human ¨ zkan, 2006). factor and the environment (O

1.1. Accident Causation: Perspectives, Theories, and Periods The perspectives, theories, and periods of human error and/ or accident causation have actually evolved systematically throughout the years. Salmon, Lenne, Stanton, Jenkins, and Walker (2010) stated that human factor (error) models can basically be categorized as either person models (e.g., the generic error modeling system by Reason (1990)), focusing on the errors made at an individual operator (e.g., driver) level, or system models (e.g., the Swiss cheese model by Reason (1990)), focusing on the interaction between wider systematic failures and errors made by an individual operator. Elvik (1996) described accident theories that have been proposed to explain road accidents and presented them chronologically as random events (1900e1920), accident proneness (1918e1955), causal theory (1940e1960), systems theory (1955e1980), and behavioral theory (1978e2000). According to Elvik, the theory of accidents as random events and accident proneness theory were designed to explain why some people have more accidents than othersdthat is, their objective was to explain variation in the number of accidents within a certain group (or even “innate characteristics”). Causal accident theory was Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10014-1 Copyright Ó 2011 Elsevier Inc. All rights reserved.

developed to identify the real causes of accidents by probing the events leading to each accident in detail (e.g., in-depth accident analysis). Systems theory, on the other hand, takes the total number of accidents in a system as the starting point of its explanatory efforts. Systems theory proposed that accidents are the result of maladjustments in the interaction between the components of complex systems. Behaviorally oriented accident theories have once more focused on individual road user behavior as a critical determinant of accident occurrence. The basic idea of these theories is that human risk assessment and human risk acceptance are very important determinants of the actual number of accidents that occur during an activity. Similarly, it has been proposed (i.e., by risk homeostasis theory) that every society has the number of accidents it wants to have, and the only way to permanently lower this number is to change the target level of risk (or the desired level of safety; e.g., the number of accidents, injuries, fatalities can be tolerated by the society including decision makers and public). In summary, Elvik stated that (1) all accident theories that have been proposed contain an element of truth, (2) none of the theories tell the whole truth, and (3) almost all theories have been proposed as means of reducing accidents rather than out of intellectual curiosity. Hale and Hovden (1998) described the three ages of safety management as an expansion of perspectives on accident phenomena by emphasizing their supplementary characteristics. The first period was mainly associated with technical measures, whereas the second one focused on behavioral factors and individual behavior. The third period was influenced by ergonomics and later merged with sociotechnical approaches (Hovden, Albrechtsen, & Herrera, 2010). Wiegmann, von Thaden, and Gibbons (2007) claimed that recent years have witnessed the development of a fourth stage, the “safety culture period.” Operators are performing their duties or interacting with technology as coordinated teams embedded within a particular culture (e.g., organizational culture). In this chapter, we propose a framework as a product of intellectual curiosity to “fight” accidents. It is hoped that this framework will also contain an element of truth of 179

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accident occurrence in the “whole truth” of accident causation. In addition, we aim to merge person (i.e., the role of behavioral factors in traffic accidents) and environment perspectives (i.e., the structure of the complex multilevel sociocultural and technical environment of traffic and its goals and mechanisms) in “the fourth age of safety” (i.e., “traffic safety culture”).

1.2. Behavioral Factors in Accidents: Driver Behaviors and Performance Most road traffic accidents can be directly attributed to behavioral factors as a sole or a contributory factor (Lewin, 1982). Behavioral factors in driving can be investigated under two separate components: driver behavior/style and performance/skills (Elander, West, & French, 1993). Driver behavior refers to the ways drivers choose to drive or habitually drive, including the choice of driving speed, habitual level of general attentiveness, and gap acceptance (Elander et al., 1993). In other words, it explains what drivers usually “do.” Driver performance includes information processing and motor and safety skills, which improve with practice and training (i.e., with driving experience). In other words, it explains what drivers “can” do. Reason, Manstead, Stradling, Baxter, and Campbell (1990) classified driver behaviors into errors, violations, slips, and lapses. They defined errors as “the failure of planned actions to achieve their intended consequences” and violations as “deliberate deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system” (p. 1316). Reason et al. (1990) also identified a third Driver Behavior Questionnaire (DBQ) factor, which they named “slips and lapses.” This factor includes attention and memory failures, which can cause embarrassment but are unlikely to have an impact on driving safety (Parker, West, Stradling, & Manstead, 1995). Lawton, Parker, Manstead, and Stradling (1997) extended the violations scale by adding more items and split it into two distinctive scales, ordinary violations and aggressive violations, according to the reason why drivers violate. However, the distinction between violations and errors is also supported by the fact that this two-factor solution was the most stable solution (among possible solutions with two to six factors) in a 3-year follow-up ¨ zkan, Lajunen, & Summala, 2006). study in Finland (O Finally, to extend the DBQ to an omnibus measure of driver ¨ zkan and Lajunen (2005) added to the DBQ behavior, O a scale for measuring positive driver behavior and obtained a clear three-factor solution: violations, errors, and positive behaviors. Spolander (1983) differentiated driver performance as technical (i.e., quick and fluent car control and traffic situation management) and defensive driving skills

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(i.e., anticipatory accident avoidance skills). Hatakka, Keskinen, Laapotti, Katila, and Kiiski (1992) used an internal reference (i.e., the drivers were asked to assess their own abilities in different aspects of driving skills) based on a well-known finding that the majority of drivers assess themselves as better than average drivers in their technical and defensive skills (Na¨a¨ta¨nen & Summala, 1976). Later, Lajunen and Summala (1995) developed the Driver Skill Inventory (DSI) to further assess both general perceptual-motor performance and safety concerns and verified the two-factor structure of the DSI as perceptualmotor skills (i.e., perception, decision making, and motor control-related skills) and safety skills (i.e., anticipatory accident avoidance skills). A consistent factor structure and high reliability of the DSI were obtained in different studies across countries (Lajunen, Corry, Summala, & Hartley, ¨ zkan, Lajunen, Chliaoutakis, Parker, & Summala, 1998; O 2006a). It is well-known that both the components of the human factor in driving (i.e., driver behavior and skills) are associated with different traffic outcomes (e.g., offenses, speeding, and accidents) (Lajunen & Summala, 1995; Reason et al., 1990). Thus, the following section describes a complex multilevel sociocultural and technical environment of traffic in which behavioral factors are embedded.

1.3. Structure of the Multilevel Sociocultural and Technical Environment of Traffic 1.3.1. Level 1: Micro LeveldIndividual Level Characteristics of Behavioral Factors in Driving Driver behaviors and performance can be assumed to reflect many drivers’ individual characteristics, such as personality, attitudes, motives or “extramotives,” and perceptualmotor and information-processing capacities (Elander et al., 1993; Groeger, 2000; Na¨a¨ta¨nen & Summala, 1976). Here, some individual-level characteristics (i.e., age, sex, and cognitive process and/or biases) are presented as examples of critical behavioral factors in driving. Sex and age are directly linked to driver behaviors, performance, and accident liability. Young people are more involved in accidents in virtually every country, and the majority of these drivers are young males (Blockey & Hartley, 1995; Doherty, Andrey, & MacGregor, 1998; Evans, 1991). In addition, men and young drivers tend to commit violations more frequently than women and older drivers. In contrast, women and older drivers committed more errors than males and young drivers (Reason et al., 1990). Road users also have to interact with each other and to take into account each other’s intentions and behaviors in order to drive safely. Thus, drivers’ cognitive processes (i.e., causal attributions) might be a source of their own and others’ risky driving behaviors, performance, and accident

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liability. Attribution refers to the process by which individuals arrive at casual explanations for their own and others’ behavior (Ross, 1977). Most studies that have examined attribution biases in traffic have investigated the false consensus bias and actoreobserver effect (Baxter, Macrae, Manstead, Stradling, & Parker, 1990; Bjo¨rklund, 2005; Manstead, Parker, Stradling, Reason, & Baxter, 1992). False consensus refers to the tendency of persons “to see their own behavioral choices and judgments as relatively common and appropriate to existing circumstances while viewing alternative responses as uncommon, deviant, or inappropriate” (Ross, Greene, & House, 1977, p. 280). Manstead et al. (1992), for example, found that compared to drivers who did not commit specific violations and errors, drivers who committed these driver behaviors perceived these behaviors as being committed by a higher proportion of drivers than they were in reality. The actoreobserver effect refers to a “pervasive tendency for actors to attribute their actions to situational requirements, whereas observers tend to attribute the same actions to stable personal dispositions” (Jones & Nisbett, 1972, p. 80). When reporting causes for close following and running traffic lights, for instance, drivers attribute their own violations to situational factors and others’ violations to their personal dispositions (Baxter et al., 1990). Based on the literature, it can be assumed that age, sex, and cognitive process and/or biases are “universal” individual-level factors influencing driving behavior and performance and accident involvement.

1.3.2. Level 2: Meso LeveldOrganizational/ Company and Group/Community Level Factors 1.3.2.1. Organizational/Company Level Factors Compared to nonprofessional drivers, professional drivers’ driving is a less self-paced task. Nonprofessional drivers can principally determine the difficulties and risk level of their driving (Caird & Kline, 2004). They can choose the mode of transportation, time of travel, and target speed. On the other hand, many factors (e.g., time schedule and working shifts and hours) can increase professional drivers’ task demands. In addition, other factors, such as a company’s culture and/or climate including safety policy and practices (Caird & Kline, 2004), seem to largely determine how, why, when, and where they drive. This might clearly indicate the importance of the role of organizational culture and/or climate in professional drivers’ driving. Organizations are complex systems with different values, principles, attitudes, and viewpoints (Arnold, 2005). As a component of this complex system, organizational climate can be defined as “a summary of molar perceptions that employees share about their work environments”

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(Zohar, 1980, p. 96). Those perceptions are thought to have a psychological utility in serving as a frame of reference for ¨ z, O ¨ zkan, guiding appropriate and adaptive task behaviors. O and Lajunen (2010) used a general organizational culture scale (i.e., Hofstede’s organizational culture scale) to investigate the relationship between organizational culture and/or climate and driver behaviors (i.e., errors, violations, and positive behaviors) among professional drivers. It was found that the highest number of violations was reported when both a low score for work orientation (i.e., low organizational importance for the work being done, rules and regulations, etc.) and a low score for employee consideration (i.e., the employees are given less consideration for their presence in and adaptation to the organization, etc.) were reported. In contrast, the lowest number of violations was reported when both work orientation (i.e., high organizational importance on the work being done, rules and regulations, etc.) and employee consideration scores were high (i.e., the employees are given more consideration for their presence in and adaptation to the organization, etc.). ¨ z, O ¨ zkan, and Lajunen (under review) developed the O Transportation Companies’ Climate Scale, which yielded three factorsdgeneral safety management, specific practices and precautions, and work and time pressure. It was found that drivers with high scores (a low level of pressure) of work and time pressure reported significantly lower frequencies of errors and violations than did drivers with low scores of work orientation. Drivers with high scores of general safety management reported significantly higher safety skills compared to drivers with low scores of general safety management. There was no main effect of any organizational climate dimensions on either the positive driver behaviors scale of the DBQ or perceptual-motor skills dimension of the DSI. Logistic regression analysis revealed a significant relationship between work and time pressure and accident involvement. Therefore, it can be assumed that organizational factors influence especially professionals’ driving behavior and performance and accident involvement, which in turn influence other road users’ driving. 1.3.2.2. Group/Community Level Factors The boundaries of the system become more open and relatively ill-defined as it is considered at higher levels (i.e., community/group level). Also, the effect of the group/ community level on an individual driver’s behaviors and performance might be getting narrower in scope and magnitude. In other words, road users are continuously interacting with each other, and they may not be under supervision as in closed systems (i.e., transportation companies). On the other hand, different cities do appear to have distinct driving cultures, such as differences in overall accident rates (Allstate Insurance Company, 2006) and road rage behaviors (Prince Market Research, 2006). In

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addition, in the same country and even in the same city, drivers from different driver groups (e.g., a truck driver versus a private car user or a young versus an old driver) might follow informal rules of their own group rather than formal rules in driving and therefore develop a different general driving style and pose different levels of accident ¨ zkan, 2002). risks (Su¨mer & O Bener et al. (2008), for example, found that four-wheel drivers committed more violations, errors, and lapses than small car users. Lapses were associated with accident involvement among four-wheel drivers, whereas both errors and aggression speeding were related to accident involvement among small car users. Four-wheel drivers also reported lower seat belt usage and higher speeding compared to small car users. Bener et al. found that four-wheel drivers were involved in nearly 40.3% of road traffic accidents. ¨ z, O ¨ zkan, and Lajunen (2009) investigated stress O reactions, speeding, number of penalties, and accident involvement among different driver groups (taxi drivers, minibus drivers, heavy vehicle drivers, and nonprofessional drivers). The results revealed differences between different driver groups in terms of both risky driving behaviors and stress reactions (aggression, dislike of driving, hazard monitoring, fatigue proneness, and thrill-seeking) in traffic. The nonprofessional drivers drove faster than the taxi, minibus, and heavy vehicle drivers on highways and faster than the heavy vehicle and minibus drivers on city roads. In addition, the minibus drivers reported more penalties than the heavy vehicle drivers. Moreover, the minibus drivers were more aggressive compared to the nonprofessional drivers. The nonprofessional drivers were better with hazard monitoring in traffic compared to the minibus and heavy vehicle drivers. Finally, the heavy vehicle drivers reported more fatigue proneness compared to the nonprofessional drivers. Aggression, dislike of driving, and hazard monitoring dimensions were also related to accident involvement, whereas dislike of driving and thrill-seeking dimensions were related to speeding on city roads.

1.3.3. Level 3: Macro Level: National Level Factors The same drivers can engage in different driver behaviors and display different performance and pose different accident risks in two different countries (Finland and Russia) with roughly the same climate but different traffic safety regulations and practices (Levia¨kangas, 1998) and public awareness and government policies (Svedung & Rasmussen, 1998). For example, Gaygisiz (2010) investigated the relationship between governance quality and road fatality rates in a sample of 46 countries. The Worldwide Governance Indicators (WGI) was used to measure six dimensions of governance. Voice and Accountability measures the extent to which a country’s citizens are able to

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participate in selecting their government as well as freedom of expression, freedom of association, and free media. Political Stability and Absence of Violence refers to the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically motivated violence and terrorism. Government Effectiveness is a measure of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Regulatory Quality measures the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Rule of Law is the extent to which agents have confidence in and abide by the rules of society, particularly the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Control of Corruption measures the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. The correlations between these indexes and traffic fatality rates were -0.41, -0.48, -0.42, -0.51, -0.41, and -0.43, respectively. In other words, WGI and traffic fatalities are significantly associated with each other, and the better governance a country has, the lower traffic fatality rate tends to be. On the other hand, governance including laws and policies might force individuals to behave appropriately and safely in traffic, but it does not necessarily affect the way in which people think overall. Indeed, policies may often fail when they are not supported by the “upper” and “lower” levels of the system and the culture.

1.3.4. Level 4: Magna Level: Ecocultural Sociopolitical Level Factors The factors on the level of ecocultural sociopolitics, called exogenous variables in the traffic literature (Page, 2001), include the usual ecological components of a traffic culture, such as economic, demographic (e.g., population), ecologic (e.g., latitude) (Hofstede, 2001), and broader cultural factors (Levia¨kangas, 1998). These factors are highly correlated with each other (Hofstede, 2001) and cannot be modified by safety policies in the short term. They mostly have indirect, and rarely direct, effects on the level of mobility and safety by interacting with engineering and road user factors of everyday traffic in a country. Economic, societal, and cultural factors appear to be the most important variables in traffic safety (Gaudry & Lassarre, 2000). 1.3.4.1. Economy A high-income country can invest in its road infrastructure, maintenance of infrastructure, traffic safety

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Person and Environment: Traffic Culture

work, vehicles, and driver education, whereas a lowincome country or country in an economic depression will invest less in traffic safety. The composition of the driver population may also change from a dominant majority of professional drivers to private car drivers in periods of economic boom (e.g., China; Zhang, Huang, Roetting, Wang, & Wei, 2006). Along with economic development, the male-dominant traffic society may change to one that is more balanced between females and males; that is, the proportion of male and female driver’s license holders changes, resulting in a higher number of female drivers (United Nations, 1997). The number of young, inexperienced drivers, however, is relatively high in high-income countries. Page (2001) indicated that an increase of 10% in the young population, with all other factors held constant, leads to an 8.3% increase in fatalities. During an economic boom, young adults have more money to spend on leisure activities such as driving. This increases the exposure and probability of accident involvement. New (and safer) car sales (Pelzman, 1975) and car ownership rates are also relatively high in high-income countries. According to the well-known Smeed’s law (Smeed, 1949), traffic casualties are related to the cube root of car ownership. It was evidenced (with data) in 20 different countries that the death rate per vehicle declined when ownership increased. In addition, Smeed’s law was valid for a variety of countries (e.g., in Great Britain from 1909 to 1973) over time and for the data from 62 countries (Adams, 1987, 1995). The economic system, on the other hand, influences the price mechanism (e.g., price of fuel), household consumption and vacation practices (e.g., holiday travel), modes of personal travel (e.g., home-to-work trips), and industrial activity for the transport of goods (Jaeger & Lassarre, 2000). It was found that factors such as the high occurrence of home-to-work trips and holiday travels, greater number of commercial vehicles per unit of work, wine consumption, and low price of fuel explain increases in both total mileage and accident risk. In the SARTRE 1 study conducted in October 1991 and June 1992 targeting major road safety concerns, it was found that the differentiation among drivers of the 15 European countries with regard to their attitudes and behaviors toward major road safety concerns (i.e., alcohol, speed, and seat belt use) was also partly associated with the economic prosperity of the individual countries (i.e., “safe” or “high-income” west/north vs. “dangerous” and “low-income” south) (SARTRE, 1998). ¨ zkan and Lajunen (2007) found that gross national O product (GNP) was the most important predictor for traffic safety in countries and the main reason for regional differences among countries in traffic safety. GNP per capita was negatively related to traffic

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fatalities. GNP also correlated with both culture dimensions and values. 1.3.4.2. Culture Hofstede’s (2001) culture dimensions include inequality between people (“power distance”), the level of stress in a society related to an unknown future (“uncertainty avoidance”), the integration of individuals into primary groups (“individualism versus collectivism”), the division of emotional roles between males and females (“masculinity versus femininity”), and the time perspective of individuals (“long-term versus short-term orientation”). Schwartz values are based on three main concerns that all societies have to confront and solve. According to Schwartz (1999), the first concern, a society’s answer to the question of to what extent persons are either autonomous or embedded in their group, can be summarized by using three value types: “conservatism” (or embeddedness in Schwartz (2004) i.e., social order, “intellectual autonomy” (i.e., curiosity), and “affective autonomy” (i.e., pleasure). The second concern is to guarantee responsible behavior that will preserve the social fabric. Value types “hierarchy” (i.e., authority) and “egalitarianism” (i.e., equality) are the main solutions for preserving the social structure of the society. The third concern is the relationship between an individual and the natural and social environment. The relationship between human and environment can be based on two value types, “mastery” and “harmony.” In this dichotomy, mastery emphasizes a human’s wish to shape his or her environment according to his or her needs, whereas harmony refers to values in which protection of the environment is emphasized. Southern European countries generally score higher on uncertainty avoidance, power distance, collectivism, egalitarianism, and masculinity and are less conservative than northern European countries (Hofstede, 2001; Schwartz, 1992, 1999). Specifically, “dangerous” Greece scores the highest on uncertainty avoidance and mastery scores. “Dangerous” Turkey also has very high scores on uncertainty avoidance, power distance, conservatism, and hierarchy. “Safe” Great Britain and The Netherlands have very high scores on individualism, and Great Britain has a very low score on uncertainty avoidance. “Safe” Finland also has low scores on masculinity, power distance, and uncertainty avoidance. It was found that masculinity dimensions of a culture were positively related to high speed limits in 14 European countries (Hofstede, 2001). In addition, Hofstede reported that uncertainty avoidance and masculinity were positively related to traffic death rates in 1971 in 14 European countries, whereas individualism was negatively related to the accident rate. Drivers in individualistic cultures show a more calculative involvement in traffic (Hofstede, 2001),

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which leads to safer driving. After controlling the effect of GNP per capita, as in earlier studies (Hofstede, 2001), uncertainty avoidance was positively related to traffic fatalities. Partial correlation coefficients showed that conservatism correlated negatively and egalitarianism ¨ zkan & correlated positively with traffic fatalities (O Lajunen, 2007). These findings indicate that the role of economic, societal, and cultural factors should be taken into account to explain the regional differences in traffic safety among countries.

1.4. Summary of the Structure of the Multilevel Sociocultural and Technical Environment of Traffic As presented in Figure 14.1, a road user’s and/or country’s level of safety in traffic is mostly determined by how and to what extent external factors (ecocultural sociopolitical, national, group, organizational, and individual factors) influence either directly or indirectly internal factors (road users/behavioral factors, roads, and environment/engineering), which in turn affect exposure and accident risk. It is highly likely that factors such as geography or climate, which remain relatively constant over time and resist change (Evans, 2004), would have a more direct effect on engineering (e.g., roads and vehicles) than would road users. On the other hand, it is likely that climate (e.g., snow) A country’s environment (external factors)

Key Variables to Understand in Traffic Psychology

could reduce drivers’ exposure and behavior, particularly speed, which in turn might increase the number of accidents but lower the risk of severe injuries (Evans, 2004). However, external factors cannot be restricted to only environment-related factors (i.e., climate); in other words, other variables can be present at the ecocultural sociopolitical level (i.e., economy and culture) as well. For example, the same drivers can engage in different driver behaviors and display different performance and pose different accident risk in two different countries (Finland and Russia) with roughly the same climate but different traffic safety regulations and practices (Levia¨kangas, 1998) and public awareness and government policies (Svedung & Rasmussen, 1998). In the same country, and even in the same city, drivers from different driver groups (e.g., a truck driver versus a private car user or a young versus an old driver) might follow informal rules of their own group rather than formal rules in driving and, therefore, develop a different general driving style and pose ¨ zkan, 2002). different levels of accident risks (Su¨mer & O Organizational culture factorsdthat is, management or company policy (Svedung & Rasmussen, 1998)dmight be more important than formal traffic code and informal group code for professional drivers. In other words, drivers from the same driver group but from different companies, who even drive the same route and vehicles, might have different driver behaviors and performance and accident ¨ z et al., 2010). Driving is therefore to some extent risk (O

Traffic components (internal factors)

Eco-cultural-socio-political level *economy, climate, geography, demography, national culture, and characteristics

Road engineering/ infrastructure

National level *Government, authorities, traffic safety regulations, political climate, public awareness

Exposure

Group level *vehicle types, informal rules, identitites

Automotive engineering/ vehicles

Accidents and its consequences

Organizational/Company level *market and financial conditions, management, organizational safety culture

Individual level *age, sex, personality, attitudes, motives, perceptual-motor, and cognitive abilities

FIGURE 14.1

Road users *behaviour *performance

The framework of the multilevel sociocultural and technical environment of traffic. Source: O¨zkan (2006).

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a “forced-paced” task, and professional drivers’ risk can also be determined by their companies. Furthermore, when all other conditions and situations are constant, each individual driver might have a different general driving style and accident liability. Because driving is to some extent a “self-paced” task and drivers determine their risk by their own choices (Na¨a¨ta¨nen & Summala, 1976), individual factors such as “extra motives,” personality, sex, and age influence an individual driver’s behaviors, performance, and accident risk (Elander et al., 1993). It can be assumed, therefore, that accident risk and differences between road users and/or countries in traffic safety may be the result of how these internal and external factors are operating within levels and between levels in the whole system. Logically, this overall structure would probably work differently among countries (and even among individual road users). It is well-known that road traffic accidents are a major problem throughout in the world. However, regional differences in traffic safety between countries are considerable. In 2002, for example, the World Health Organization’s (WHO) Western Pacific Region and South-East Asia Region accounted for more than half of the absolute number of road traffic fatalities that occurred annually throughout the world. The WHO

FIGURE 14.2

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African Region (including the Middle East) had the highest fatality rate with 28.3 per 100,000 population, which was closely followed by the low- and middle-income countries of the WHO Eastern Mediterranean Region with 26.4 fatalities per 100,000 population (WHO, 2004). The vast differences in traffic fatalities among countries are remarkable in the world in general and in Europe and its close neighbors (e.g., the Middle East) in particular. In the European Union, approximately 40,800 people were killed in traffic accidents in 2000, and an additional 11,600 people were killed in the accession countries (European Transport Safety Council, 2003). As presented in Figure 14.2, eastern/ southern (Mediterranean) Europe (e.g., Greece and Turkey) has the highest accident rates compared to northern/western Europe. In 2003, 7.6 Finns and Britons and 7.7 Dutch per 1 billion vehicle-kilometers were killed in traffic accidents, whereas the corresponding figures for Greeks and Turks were 26.7 and 73 in 2001, respectively (International Road Traffic and Accident Database, 2005). Traffic fatalities were reported to be much higher in Middle Eastern countries (i.e., Iran) than in European countries (i.e., Turkey) (Raoufi, 2003). It could therefore be hypothesized that the vast differences among countries in traffic culture and level of safety

Road fatalities in some European countries per 1 billion vehicle-kilometers on all roads in selected years. Source: O¨zkan (2006).

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would be reflected in drivers’ driving behaviors. As ¨ zkan, Lajunen, Chliaoutakis, Parker, and hypothesized, O Summala (2006b)dby comparing British, Dutch, Finnish, Greek, Iranian, and Turkish driversdshowed that drivers in “safe” western/northern countries scored higher on ordinary violations, especially speeding on the motorway, whereas drivers in “dangerous” southern European/Middle Eastern countries had higher driving error and aggressive driving scores. It was also suggested that the higher level of aggressive driving and errors in southern European and Middle Eastern drivers was due to higher levels of conflict attributed to less developed infrastructure, less respect for traffic rules, and higher levels of driver stress. According to the conclusions, the higher frequency of speeding reported by drivers in “safe” countries reflected the level of enforcement. In addition, it was claimed that the concept of being a “safe driver” is culture dependent and, therefore, understood differently in different countries. On the other hand, based on the overall complex multilevel sociocultural and technical environment of traffic, the countries may be similar to some extend with regard to safety. However, the “software” or “traffic culture” defining the main goals, values, norms, practices, and mechanisms must then be taken into account.

2. TRAFFIC CULTURE: GOALS AND MECHANISMS Levia¨kangas (1998) labeled all the factors (probably all those shown in Figure 14.1) that directly and/or indirectly influence a country’s level of traffic safety as “traffic culture” (AAA Foundation for Traffic Safety, 2007). According to him, traffic culture is the sum of all factors that affect skills, attitudes, and behaviors of drivers as well as vehicles and infrastructure. However, the term traffic culture has not been conceptualized comprehensively. Therefore, this section uses “traffic culture” as a framework of reference for studying the goals and mechanisms of traffic culture. It is well-known that practices overwhelmingly aim to achieve the goals of safety (i.e., decreasing the number of accidents and near accidents) and promoting mobility (i.e., reaching the destination in terms of the amount of travel and trip time in traffic) (Evans, 2004). However, mobility and safety are often, but not always, in conflict. The primary goal of a traffic system in a country is mostly mobility, which should be achieved by minimizing the risk of the unwanted by-productdaccidents (Evans, 2004). In addition, some subgoals, such as environment-friendly, comfortable, cost-effective transportation, are becoming increasingly important to policy makers and the public. It can be assumed that traffic culture in a country or in a region is formed and maintained mostly by formal and informal rules, norms, and values, which are the center of

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the mechanism of traffic culture. They define the acceptable and necessary road user behaviors and performance and choices of engineering practices. Whereas formal rules are mostly applied and enforced by authorities, including education, enforcement, and engineering, road users mostly share informal rules, norms, and values as a result of exposure and interaction with other road users. Exposuredthat is, the degree to which a driver exposes him- or herself to traffic and the probability of being involved in an accidentdis “a systematic process affecting the crash system” (Chapman, 1973) and, therefore, one of the main reasons for the overrepresentation of a particular driver group in accident statistics (Laapotti, 2003). In addition, exposure can be supposed to be the main quantitative (i.e., the amount of driving) and qualitative (i.e., why, when, where, with whom, and in what kind of weather and road conditions) predictor of driving and the of the interaction among internal and external factors, risky general driving style, and accident involvement (Laapotti, 2003). For instance, the average male driver drives more miles than does the average female driver (Stradling & Parker, 1996). Drivers who drive frequently, violate traffic rules more often than those who drive less frequently. They also tend to commit more aggressive driving behaviors than young female drivers and older drivers (Lawton et al., 1997). In addition, driving experience is associated with confidence in one’s own driving skills but negatively related to concern for safety (Lajunen & Summala, 1995). Also, the relationship between mileage and accidents seems not to be linear but, rather, a negatively accelerating curve, with a smaller increase in accident rate at a higher level of mileage (Maycock, Lockwood, & Lester, 1991). Other road users are studied as a source of information, communication, imitation, and as a reference group (Bjo¨rklund, 2005; Zaidel, 1992). Cultural and environmental factors define acceptable and “normal” behaviors, which in turn influence the definition of violations, not only simply in the strict legal sense (Manstead, 1998) but also informally (Bjo¨rklund, 2005). In addition, they might influence appraisals of the intentions and behaviors of other road users, which in turn could influence attribution of intentionality, controllability, and responsibility of driver behaviors and potential reactions (i.e., retaliation). Moreover, these factors might lead to different evaluations of risk, one’s own and other’s performance and behaviors across countries, and interpersonal conflicts in traffic ¨ zkan, Lajunen, Parker, (Bjo¨rklund, 2005). For example, O Su¨mer, and Summala (2010) found that “others” was a critical component of safe driving among British, Dutch, Finnish, and Turkish drivers. It was found that symmetric interpersonal aggression between aggressive warnings and hostile aggression and revenge factors of “self” and “others” created a serious risk of road accident involvement for drivers in every country except among British male and

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Finnish female drivers. The statistically symmetric interaction between aggressive warnings and hostile aggression and revenge factors also indicated that aggressive warnings might have a potential to release anger and escalate aggression both “within drivers” and “between drivers.” Their study also showed that both situational and cultural factors are important for understanding the role of anger and aggression in driving as well as the symmetric interaction between “self” and “others.” Traffic culture is also a result of both the larger cultural heritage and the current state of the environment, including the economy and political climate (Levia¨kangas, 1998). Similar to the culture of a country (Hofstede, 2001), ecological (e.g., economy and geography), societal, and cultural factors seem to lead to the development and pattern maintenance of institutions or political bodies (e.g., legislation, engineering, and educational systems). Once these institutions are established, the societal norms and values and formal and informal rules will be reinforced, and the boundaries of road user behaviors will be determined. Thus, the traffic culture of a country is formed and continues based on the functions of the large number of factors and practices at the multilevels or layers. In summary, traffic culture of a country can be redefined as the sum of all external factors (ecocultural sociopolitical, national, group, organizational, and individual factors) and practices (e.g., education, enforcement, engineering, economy, and exposure) for the main goals of mobility and safety to cope with internal factors (road users, roads, and engineering) of traffic.

2.1. Traffic Safety Culture The conceptualization of traffic culture seems to be broad and sometimes equivalent to the traffic system as a whole. Traffic culture and traffic system are, in fact, mutually inclusive and the main contributors to the differences in traffic safety between countries. However, they are based on different principles. Traffic (or “hardware”) is mainly based on tangible things such as roads, traffic signs, infrastructure, vehicles, tools, and equipment. On the other hand, traffic culture is defined as the sum of all external factors and practices for mainly the goals of mobility and safety to cope with internal factors of traffic. In addition, basic assumptions, formal and informal rules, values, norms, perceptions, and attitudes are the center of the mechanism of traffic culture; in other words, the “software” of the traffic culture is traffic safety culture and/or climate. The safety culture concept emerged after the Chernobyl accident and several reports prepared by the International Atomic Energy Agency. The concept of safety culture was defined for the first time by the Advisory Committee on the Safety of Nuclear Installations (International Nuclear Safety Advisory Group, 1991) as follows: “Safety culture is

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the product of individual and group values, attitudes, competencies, and patterns of behavior that determine the commitment to, and the style and proficiency of, an organization’s health and safety programs.” Zohar (1980) defined the safety climate as “a summary of molar perceptions that employees share about their work environments (safety climate).” The development of these concepts seems to be successive rather than parallel: “The minor substantive differences between culture and climate may prove to be more apparent than real” (Glick, 1985). As presented in Table 14.1, however, it might be difficult to replace them with each other, and it might be difficult to separate these concepts in practicedeven the concepts are very novel in the traffic literature (Antonsen, 2009; Guldenmund, 2000; Wiegmann et al., 2007). “Traffic culture and/or climate” and “traffic safety culture and/or climate” have remained mostly a notion in the literature without attempts to measure it empirically. One special problem related to measuring traffic culture can be seen in studies measuring “safety culture”: The “traffic culture” seems to largely overlap with the concept of “traffic climate,” and sometimes these concepts are used interchangeably. However, they are different concepts while being mutually inclusive (Antonsen, 2009; Guldenmund, 2000). Wiegmann and colleagues (2007), for example,

TABLE 14.1 Features and Differences between Culture and Climate Culture

Climate

Beliefs and values about people, work, the organization, and the community that are shared by most members within the organization

Common characteristics of behavior and expression of feelings by organizational members

More qualitative approach

More quantitative approach

Research focused on the dynamic process, creating and shaping culture An enduring aspect of the organization with traitlike properties

Reflection and manifestation of cultural assumptions

The perception of a coherence of numerous processes by all the members in the organization

The underlying meaning given to this coherence

Not easily changed and relatively stable

Shaped by interactions

Multiple dimensionality; holistic, mutual, and reciprocal; and shared by people

Tension between reductionism and holistic

Exists at a higher level of abstraction than climate

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gave 13 and 12 example definitions for safety culture and safety climate, respectively. However, the definition of traffic safety culture/climate remained unexplored. As in the literature on safety culture and climate, traffic safety culture can thus be defined as the product of exposure and interaction of road users and the set of formal and informal rules, norms, basic assumptions, attitudes, values, habits, and perceptions in relation to safety and/or to conditions considered risky, dangerous, or injuries. As presented in Table 14.1, safety climate will then be the surface features of the safety culture (Mearns, Flin, Gordon, & Fleming, 1998) or the temporal state measure of culture (Cheyne, Cox, Oliver, & Thomas, 1998). In addition, climate exists at a lower level of abstraction than culture (Guldenmund, 2000) and provides a limited set of variables that can be operationalized and measured (Cox & Flin, 1998). Thus, climate research is conducted mostly using quantitative methods (e.g., questionnaires) dealing with the members’ perceptions and practices and how these practices and perceptions are categorized into the analytical dimensions defined by the researchers (Guldenmund, 2000). ¨ zkan and Lajunen (under review) used and defined O “traffic climate” as preferred metric and the manifestation of traffic culture discerned from the road users’ attitudes and perceptions at a given point in time (Cox & Flin, 1998). Traffic climate is therefore defined as the road users’ (e.g., drivers’) attitudes and perceptions of the traffic in a context ¨ zkan & Lajunen, (e.g., country) at a given point in time (O ¨ under review). Ozkan, Lajunen, Walle´n Warner, and

Key Variables to Understand in Traffic Psychology

Tzamalouka (2006) found that compared to Swedish and Finnish drivers, Turks and Greeks perceived their traffic climate to be more dangerous, dynamic, fast, dense, unpredictable, chaotic, and free flowing, thus requiring more patience. In contrast, compared to Turks and Greeks, Swedes and Finns perceived their traffic climate to be more harmonious, safe, functional, enforced (including the use of preventive measures), dependent on mutual consideration, planned, and mobile. It can be claimed that the vast differences among countries (i.e., Greece, Finland, Sweden, and Turkey) in traffic safety also reflect their drivers’ perceptions of the traffic climate. The set of formal and informal rules, norms, basic assumptions, attitudes, values, habits, and perceptions can operate in different layers of traffic safety culture and climate. For example, there are some basic assumptions, core values and norms, and goals that are underlying factors of traffic safety culture at each level of the traffic culture. In addition, there are some espoused values and artifacts (e.g., attitudes, habits, and perceptions) at the upper layers of traffic safety climate for each level of the traffic culture (for a multilevel model of culture including basic assumptions, espoused values, and artifacts, see Schein, 1992). It is desirable in relation to safety that these layers operate consistently and harmoniously to minimize the exposure of road users and, sometimes, members of the public to conditions considered dangerous or to injuries at each level of traffic culture. In addition, levels of traffic culture should operate consistently and harmoniously as

FIGURE 14.3 “Swiss cheese” model. Source: Adapted from Reason (1990).

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Person and Environment: Traffic Culture

well. It can be assumed that any simultaneous latent or active failures either within layers and/or levels or between layers and/or levels could result in risky or dangerous acts or injuries in due course. Like the “Swiss cheese” model (Figure 14.3), traffic culture and traffic safety culture/climate focus on the interaction between latent and active conditions/failures within and between layers (i.e., traffic culture and traffic climate) and/or levels (i.e., ecocultural, sociopolitical, national, group, organizational, and individual) and unsafe acts and their contribution to accidents. Safety is therefore the responsibility of actors at all layers and/or levels of the system, especially in the absence of “defense barriers” (e.g., enforcement). In addition to the integrative perspective, differentiation and fragmentation perspectives can also be applied in the open system (i.e., traffic; Antonsen, 2009). Salmon and colleagues (2010) stated that The Netherlands’ Sustainable Safety approach (Wegman, Aarts, & Bax, 2008), for example, highlights the fact that the fallibility of human operators is underpinned by the assumption that the responsibility for safety is shared among actors across all levels of the complex sociotechnical system (e.g., regulators, policy makers, designers, line managers, manufacturers, supervisors, and front-line operators). It is not just the responsibility of front-line operators (i.e., road

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users) alone. In contrast to a closed system (e.g., factories), the levels of traffic and traffic culture influence and are influenced by each other and are underpinned by traffic safety culture/climate (e.g., basic assumptions, espoused values, and artifacts). Then, they are reflected in individual road user behavior, which in turn influences the likelihood of being in a traffic accident and thereby affecting the content and development of the other levels (Figure 14.4).

3. CONCLUSION An accident has been defined either as independent or a combined outcome of behavioral factors, vehicle-related factors, and road environment in the literature. However, as presented in this chapter, behavioral factors, vehicle-related factors, and road environment are actually embedded in a larger system (see Figure 14.1). An accident is therefore either an independent or a combined outcome of internal factors of the multilevel sociocultural and technical environment of traffic. Briefly, a road users’ and/or country’s level of safety in traffic is mostly determined by how and to what extent external factors (i.e., individual, organizational, group/community, national, and ecocultural sociopolitical levels) influence either directly or indirectly internal factors (i.e., behavioral factors, vehicle-related FIGURE 14.4 Multilevel model of “traffic safety culture and climate”

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factors, and road environment), which in turn affect exposure, risk, and accident involvement. All these factors are interactive and (see Figures 14.3 and 14.4) operate simultaneously in daily life. Each component of traffic culture (see Figure 14.1) has its own weight on safety (or unintentional injuries), and that weight depends on its relevance and importance in time and space of an event. Compared to nonprofessional drivers, many factors (e.g., time schedule, working shifts, and hours) can increase professional drivers’ task demands. A company’s culture and/or climate, including safety policy and practices, can also largely determine how, why, when, and where they drive. It is also possible that the interaction among these levels will influence traffic safety. Gaygisiz (2010) found that among the low WGI countries, increasing hierarchy was positively related to road fatalities, whereas in the high WGI countries hierarchy was not related to road fatalities. Similarly, among the low WGI countries, increasing mastery was positively related to road fatalities, whereas the effect was smaller in the moderate and high WGI countries. This chapter provided the first definitions of traffic culture/climate, the multilevel approach, and a comprehensive framework for traffic safety. Note, however, that traffic is one of the most open systems of all. Therefore, applying the traffic culture framework in such a system is not as easy as applying safety culture in closed ones (e.g., industrial companies). In addition, theoretically and potentially, any road user of the system can trigger a factor within one of the levels for another road user embedded in the same system. In other words, there is a shared, continuous, active and interactive environment for all road users. It should also be noted that it is not easy to change some of these factors (e.g., societal and cultural factors). On the contrary, they are external factors to the traffic system and, therefore, it is very likely that internal factors (i.e., engineering and road user factors) might buffer or facilitate their effects on traffic safety. It seems that in addition to the traditional three E’s in injury prevention (i.e., engineering, enforcement, and education), economy and exposure (including interaction with other road users) should be added as the fourth and fifth E’s (see Chapter 1). For example, economic incentives should be used to encourage injury prevention (e.g., monetary incentives for purchasing safety equipment) and structural modifications. Economic resources might also be efficiently spent not only on traffic, road, and automotive engineering but also on education, enforcement, and emergency services to develop a more predictable, certain, interpretable and preventive traffic system. In addition, GNP also correlates with both culture dimensions and values, which influences their relationship with unintentional injuries. It is likely that economic incentives could be used as a tool for developing more

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Key Variables to Understand in Traffic Psychology

safety-minded cultures and values. Moreover, up-to-date highway codes and applicable legislative interventions (i.e., enforcement) and education should also target driver behavior and performance of everyday traffic. Furthermore, because the balance between safety and mobility in traffic is important and initiated by the decisions of policy makers and planners, goals for traffic should be carefully evaluated at each level of the traffic system for all road users. The priority goal also has the potential to determine the dominant group in traffic. For example, high-mobility priority might put too much weight on drivers and cars compared to pedestrians, motorcyclists, and others. Despite having some principles, assumptions, and frames for road safety strategies, the model may have a number of flaws that limit its utility somewhat in terms of practical road transport applications. First, there are no structured road transport-specific methodologies associated with the model. Valid data collection and analysis methods for road transport are also necessary to test the model’s assumptions. Second, because the model is generic, it also lacks a clear definition of the different failures residing at each of the levels and the role of the components of safety culture and climate within the model. Finally, the model is currently rather descriptive. On the other hand, we hope the proposed framework supports accident occurrence as a product of intellectual curiosity to reduce the number of accidents. In addition, the framework, including definitions of traffic culture and traffic safety culture/climate, seems to some extent merge person (i.e., the role of behavioral factors in traffic accidents) and environment perspectives (i.e., the structure of the complex multilevel sociocultural and technical environment of the traffic, its goals, and its mechanisms) in “the fourth age of safety” (i.e., traffic safety culture).

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Chapter 15

Human Factors and Ergonomics Ilit Oppenheim and David Shinar Ben-Gurion University of the Negev, Beer Sheva, Israel

1. INTRODUCTION “Human factors” or “ergonomic” aspects of traffic psychology refer to the implications of the road user’s physical, physiological, cognitive, personality, and social behavior concerns and considerations in the design of vehicles and roadways. Human factors/ergonomics (HFE) is a relatively new scientific discipline, with the first book in this area published by Chapanis, Garner, and Morgan in 1949. It is distinct from psychology, engineering, and design because the focus of analysis is on the interaction between people and technology rather than on people or technology independently from each other. This means that HFE requires an interdisciplinary approach. HFE scientists are concerned with human performance in technological systems with a view to optimization of the design of the system in terms of values such as effectiveness, safety, comfort, and well-being. Like all scientific disciplines, HFE is characterized by theoretical and methodological development together with empirical investigations. The latter tend to shift between real-world studies and laboratory studies (Stanton, Young, & Walker, 2007). The importance of HFE in highway traffic safety was highlighted in two landmark studies that were published at approximately the same time in the United States (Treat et al., 1977) and in the United Kingdom (Sabey & Staughton, 1975). The studies focused on the causes of traffic accidents, and they identified factors associated with large samples of driving crashes. The research groups, which were unaware of each other’s activities, obtained remarkably similar findings. The U.S. study found the road user to be the sole cause of 57% of crashes, the environment in 3%, and the vehicle in 2%. The corresponding values from the UK study were 65, 2, and 2%, respectively. Approximately half the crashes were caused by a combination of factors, in which had one not existed the crash would not have occurred. Thus, in conjunction with other causes, the road user was identified as a sole or contributing factor in 94% of crashes in the U.S. study and in 95% of crashes in the UK study. The road environment alone or with other factors was identified as a causal factor in Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10015-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

31% of crashes in the U.S. study and in 27% of crashes in the UK study (Figure 15.1). Although the driver is the main actor in the driving activity, driving is not an isolated activity. It takes place in a wider context in which the driver constantly interacts with his or her immediate environment and the vehicle. Although the human factor is more dominant than environmental or vehicle factors in the causation of accidents, the control of the road factorsdthat is, any external conditions and surroundings of the vehicle (e.g., road, traffic, and visibility conditions)dand the control of the vehicle characteristics (e.g., braking and steering performance and passenger protection) are often much easier than the control of the human factor. Moreover, by good design, it is even possible to compensate for various human failures and limitations and thus decrease the number of traffic accidents (Iyinam, Iyinam, & Ergun, 1997; Shinar, 2007).

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FIGURE 15.1 Road user, environment, and vehicle contribution to crashes. Source: Reproduced from Rumar (1985) with kind permission of Springer Science and Business Media.

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2. THE VIEW FROM THE DRIVER’S SEAT: DRIVER-CENTERED DESIGN IMPLICATIONS The human-centered approach to design places the user in the heart of the system. In driving, this means that all aspects of vehicle and environmental design considerations have to be subservient to the driver’s characteristics, needs, capabilities, and limitations. From these, we try to derive the most appropriate technologies. The human-centered approach was first applied to the humanecomputer interaction and has since been extended to other systems, such as automotive systems (Michon, 1993) and aviation (Cacciabue, Mauri, & Owen, 2003). HFE addresses the interaction between human beings and the devices or systems that they operate in various capacities. Good HFE design requires an understanding of the characteristics of the users and the tasks in which they are engaged. Therefore, the user-centered design philosophy is that an effective, safe, and accepted product must be designed around the user. It takes into account not only physical and perceptual capabilities (e.g., reaction time or visual acuity) but also behaviors, knowledge, motivations, and attitudes. Common tools that we use to understand how we interact with our environment (including our vehicle, other vehicles, the roadway, and other road users) are the theories and models. Their importance for improving highway safety was made very succinctly by Kantowitz et al. (2004, as cited in Shinar, 2007): Absent the theories, it is almost impossible to specify what new countermeasures might emerge. Thus, what is a standard operating procedure for many human factors researchers (using models) might require an act of faith from practicing highway engineers who do not normally invoke theories of human behavior. If aviation, nuclear power, and humanecomputer interaction can create better countermeasures through models, so can driving. (pp. 85e86)

According to Kantowitz (2000), a theory is the best practical human factors tool because (1) it fills in where data are lacking; (2) in a computational format, it can provide quantitative predictions needed by engineers; (3) it prevents us from reinventing the wheel by allowing us to recognize similarities among problems, such as the tendency of drivers to adopt inappropriate decision criteria in many situations; and (4) it is reusable. Once the investment has been made to build a model for a particular domain, the theory can be recycled inexpensively to answer many system design questions. The body of research that has accumulated on driving behavior is not just a collection of findings and conclusions but also a coherent picture that emerges out of these findings and conclusions. That picture is our theory of driving

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behavior. Once we have a theory, we can continue gathering additional “facts” to fill the remaining gaps. The purpose of the models and theories of driver behavior is to make sense of it all. A theory and a model are not synonymous. A theory is a conceptual organization of concepts, mechanisms, and processes that are involved in the operation of a system, such as the driver in traffic. A model is less presumptive in the sense that it does not presume that these mechanisms and processes actually exist but only that if we posit them, then we can explain human behavior. Often, a model of human behavior is developed and then a search is made to determine if some of its mechanisms actually exist. A model can often serve as a basis for a theory. In general, unless there is independent evidence for the existence of specific processes and mechanisms, it is safer to talk of models of driver behavior than theories of driver behavior (Shinar, 2007).

2.1. Models of Driver Behavior The first attempts to model drivers’ behavior were made in the early 1960s (Delorme & Song, 2001) to improve driving safety and driver education (McKnight & Adams, 1971). Since then, a plethora of models have been suggested, and the best way to describe them is by presenting them within a conceptual framework. Figure 15.2 provides one such attempt (based in part on Weller, Schlag, Gatti, Jorna, & Leur, 2006). One approach is to describe driving behaviors within various driving tasks or what the driver does. The principal limitation of this approach is that it is purely descriptive and with very little predictive power. An alternative approach, the functional approach, is to model behavior relative to the driver’s tasks or functions. This approach attempts to predict driver behavior by focusing on why drivers do what they dodthat is, the situational and motivational factors that are involved in the risk management of driving. One advantage of these models is the potential to implement them, either by generating a simulation of the driver or by integrating them into some already

Descriptive models

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FIGURE 15.2 Driver models. Source: Based on Weller et al. (2006).

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existing traffic simulation tools or driver assistance devices such as collision warning systems. Some of the variation among the models is due to the different applications for which the models are intended, and some of the variation is due to the part of the driving task they are intended to describe. Because driving encompasses so many tasks and subtasks at different levels, often performed by the driver simultaneously, it is perhaps not surprising that it is difficult to find any consensus in the literature on how the process of driving should be modeled. The scope of this chapter precludes a detailed description of the different models, and they are only described here in general terms as they relate to the different types listed in Figure 15.2. Descriptive behavioral models focus on what the drivers do. These models attempt to describe the entire driving task or some components of it in terms of what the driver does or has to do. The predictive power of such models is very limited because they do not take into account the forces that shape the different behaviors such as driver motivation, skills, capabilities, and limitations in different situations (Carsten, 2007). Despite this severe limitation, these models have provided a strong impetus to driving safety research (Lee, 2008; Michon, 1985; Parasuraman & Riley, 1997; Salvucci, 2006; Sheridan, 1970, 2004). The descriptive models can be divided into hierarchical models (e.g., Michon, 1985) and control loop models (e.g., McRuer & Weir, 1969). The hierarchical models describe behavior in terms of a hierarchy of three distinct types of behaviors, each building on the level below it. The lowest level is an operational, control level. At this level, most behaviors are automatic and consist of quick

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responses to the changing environment (e.g., braking when a lead car slows down). The second level is a tactical, vehicle maneuvering level referring to how traffic situations are mastered. The behaviors are less reflexive and consist of conscious decisions in the driving, such as a decision to change lanes before exiting a highway. The third and highest level is a planning or strategic level, and it consists of long-term decisions such as which route to choose or even whether to drive at all. Thus, the three levels can be distinguished by the task requirements, the time frame needed to carry them out, and the cognitive processes involved at each level (Figure 15.3). The second type of descriptive models are the control loop models. These models describe the operation of the driving task in terms of inputs, outputs, and feedback. Control loop models deal primarily with the steering control aspect of driving in order to follow a specified route (McRuer, Allen, Weir, & Klein, 1977; as cited in Fastenmeier & Gstalter, 2007). These models of driving have traditionally been couched either in terms of guidance and control or in terms of human factors. Unfortunately, expanding these models to accommodate the rapidly growing complexity and sophistication of modern cars is a very daunting task. Within limits, due to their quantitative approach, such models can provide coherent and consistent ways of describing driver performance that help engineers develop and validate technical concepts for semi- and fully automated systems in cars (Hollnagel, Na˚bo, & Lau, 2003). Functional models, which include motivational models and information processing models, are the most likely to help understand complex tasks such as driving (Michon, 1985). These models focus on the mental activities

Knowledge-based behavior Identification

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FIGURE 15.3 Combination of performance levels according to Rasmussen (1986) and the hierarchical model according to Michon (1985) in Weller et al. (2006)

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involved in driving and attempt to explain why the driver undertakes certain actions. These models are necessary to understand human errors and difficulties and also to design driving assistance adapted to driver needs (Keith et al., 2005). These models strongly emphasize the driver’s cognitive state and have incorporated important psychological concepts such as motivation and risk assessment. Information processing models of the driving task belong to functional models because they involve interactions between different components (Michon, 1985). These models consist of different stages, which include perception, decision and response selection, and response execution. Each stage is assumed to perform some transformation of data and to take some time for its completion (Wickens, 1992). The driver in such models is described as a passive information transmission channel who performs different acts within capacity limitations. The system has two more crucial components: the attention allocation mechanism and a feedback loop. The feedback loop indicates that the process is an ongoing one that is continuously modified in accordance with new stimuli (Figure 15.4;Shinar, 2007). Much experimentation has been directed at determining which types of processing can occur simultaneously and which must occur sequentially. During the 1970s, a paradigm shift took place in the study of attention (Kahneman & Treisman, 1984). The shift involved a move away from determining the limits of processing and locus of the attentional bottleneck. Later, based in large part on theoretical advances by Schneider and Shiffrin (1977; Shiffrin & Schneider, 1977), research was directed at determining the characteristics and conditions under which automaticity develops. This work influenced research in HFE and began to influence theory in driving behavior (Ranney, 1994). Rasmussen’s model of information processing (Rasmussen, 1986), which has been proven to be heuristically fruitful in many HFE applications, serves as a starting point. Furthermore, a feedback loop was integrated into the model

Own car Pedestrians

of driverevehicleeenvironment, helping to adapt task difficulty or the desired amount of strain experienced by coping with the stressors that originate from drivers’ behavior, showing the influence of the drivers’ actions on the future situations with which they have to cope. Generic information processing models of the human driver can provide useful generalizations. They have value in systems engineering and in seeking to predict asymptotic limits of human performance. Even relatively simple models can have value as engineering tools by providing structure and drawing attention to possible limitations in the performance of humans in systems. However, as a means of understanding why specific individuals on a particular day in a particular set of circumstances behaved (or failed to behave) in a particular way, or predicting how an individual might behave in a given set of unexpected circumstances, such models are very limited and have little to offer. Motivational models of driving emerged in the 1960s and 1970s. Motivational models focus on “what the driver actually does” in a given traffic situation rather than on the level of driving skill. The main assumptions of these models are that driving is self-paced and that drivers select the amount of risk they are willing to endure in any given situation. The driver is seen as an active decision maker or information seeker (Gibson, 1966) rather than as the passive responder implicit in many information processing models. The importance of the situational factors resulted from the failure of earlier attempts to relate stable personality traits to accident causation. The risk associated with possible outcomes is seen as the main factor influencing behavior; however, these models also assume that drivers are not necessarily aware of the risks associated with other outcomes. Examples of motivational models include risk compensation models (Wilde, 1982), risk threshold models (Naatanen & Summala, 1976), and risk avoidance models (Fuller, 1984).

Automatic sensors

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Key Variables to Understand in Traffic Psychology

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A simplified block diagram of the driver functions in the driver-vehicle-road system.

FIGURE 15.4

A limited capacity model of driver information processing. Source: Reproduced from Shinar (1978).

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Motivational models take into account interactions between general mechanism and individual differences. Although in most cases motivational models are equivalent to risk models, recent approaches view workload homeostasis as the most important motivational background (Fuller, 2005). Motivational models aim to describe how the driver manages risk or task difficulty (Carsten, 2007). For validation, all models have to rely on some dependent measure of driving safety. This could be an intermediate measure (e.g., the use of seat belts), but more typically it is based on crash-based measures, such as accident frequencies and rates or injury frequencies and rates. However, traffic safety is more than the mere absence of accidents (Ranney, 1994). Relative to the driver, we often strive to evaluate these measures in terms of driver performance and behavior measures manifested by errors and reaction time to various events. Various taxonomies of human error have been proposed, and three perspectives currently dominate the literature: Norman’s (1981) error categorization; Reason’s (1990) slips, lapses, mistakes, and violations classification; and Rasmussen’s (1986) skill, rule, and knowledge error classification. Here, we focus only on Reason’s classification of different types of unsafe acts (Figure 15.5). Slips and lapses are defined as behaviors related to attentional and memory failures that might impact driver safety (Wickens, Toplak, & Wiesenthal, 2008), and both are characterized by unintended behaviors (or failures to behave in some way). Slips relate more directly to psychomotor components of driving at the operational level

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of control and refer to events in which the planned action would have achieved the desired goal but the right intention is incorrectly executed (e.g., when a driver who plans to push the brake pedal to slow down inadvertently pushes the accelerator pedal; the intention was correct, but the execution was erroneous). On the other hand, lapses represent the failure to carry out any action at all. These are omission errors based on forgetfulness (e.g., a driver forgetting to turn off the lights when departing the car, although fully intended to do so). Lapses are of particular relevance to roadway accidents because they reflect deficiencies in skill-based automatic behaviors (Ranney, 1994; Reason, 1990). On the contrary, mistakes occur when a driver intentionally performs an action that is wrong (e.g., a driver decides to accelerate when the right action would have been to brake or slow down) as a result of limitations in perception, memory, and cognition. Mistakes originate at the planning level rather than at the execution level, and they are likely to precipitate inappropriate maneuvering decisions. Although both rule- and knowledge-based mistakes characterize intentions that are not suitable for the situation, there are some differences between the two. Rulebased mistakes tend to be made with confidence (misapplication of a good procedure; e.g., performing a task that has been successful before in a particular context), whereas knowledge-based mistakes are more likely to occur in a situation in which rules are not applicable and the operator becomes less certain (e.g., performing a task that is “unsuitable, inelegant, or inadvisable” at the most basic

Attentional failures - Intrusion - Omission - Reversal - Misordering - Mistiming

Unintended action Memory failures Lapse

- Omitting planned items - Place-losing - Forgetting intentions

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Rules-based mistakes -Misapplication of good rule -Application of bad rule Knowledge-based mistakes -Many variables forms

Intended action Intentional noncompliance Violation

-Routine violations -Exceptional violations -Acts of sabotage

FIGURE 15.5 Reason’s (1990) classification of unsafe acts. Source: Reproduced from Weller et al. (2006).

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level). The knowledge-based mistakes will also involve much more conscious effort, and the chances of making a mistake while functioning at this level are higher than they are at a rule-based level because there are many more ways in which information acquisition and integration may fail (Reason, 1990). The final category, violation, involves “deliberate deviations from those practices deemed necessary to maintain the safe operation of a potentially hazardous system” (Reason, 1990, p. 195). In the case of driving, this would be deliberate deviations from accepted procedures, standards, and rules of safe driving (i.e., speeding). Research has shown that violations are positively statistically associated with crash involvement (Lindgren, Brostro¨m, Chen, & Bengtsson, 2007). Comparing violations with errors, Reason states that errors should be related to the individual cognitive processes, whereas violations concern the social text in which they occur. Errors may therefore be minimized by retraining, memory aids, and better humanemachine interfaces. Violations, on the other hand, should possibly be dealt with by trying to change users’ attitudes, beliefs, and norms and by improving the overall safety culture (Lindgren et al., 2007). When measuring behavior in terms of continuousdrather than discretedmeasures, we tend to use driver response time to various events. Response time is typically composed of at least three components: (1) the time required for the driver to perceive the sensory input and to decide on a response, often labeled perceptione reaction time; (2) the time used to perform the programmed movement, such as lifting the foot from the accelerator and touching the brakedoften labeled movement time; and (3) the time the physical device requires to execute its response, such as the time needed for the brakes to engage once the brake pedal has been depressed. Because the mental processing involved in the perception reaction time is an internal quantity that cannot be measured directly and objectively without a physical response, it is usually measured jointly with movement time (Setti, Rakha, & El-Shawarby, 2007), although not always (WarshawskyLivne & Shinar, 2002). Brake reaction time (RT) is a parameter of driving behavior that has not only attracted the interest of researchers but also is of great importance in road design and the accident litigation process. Brake RT is used in assessing stopping sight distance, durations of the amber phase in traffic signals, recommended distance in car following (recommended headways), etc. In accident litigation, the legal outcome often hinges on whether or not the participant driver reacted to the impending collision within an “acceptable” time, where acceptability is established from a certain percentile of RT distribution thought to represent the driver population (or relevant fraction of it) in relevant conditions (Summala, 2000).

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2.2. Behavior (TypicaldAffected by Needs) Versus Performance (Capabilities) In modeling the driver’s interactions with the vehicle and the environment, we must also distinguish between driver performance and driver behavior (Naatanen & Summala, 1976; Shinar, 1978). Performance refers to “best behavior,” or what drivers are capable of doing in a given situation (limits of maximal behavior), and it is typically measured in a controlled experiment. Behavior refers to “typical behavior,” or what drivers actually do in most of their driving, and it is more difficult to measure. Driver performance is the end product of what a driver can do, given human limitations and given vehicle and environmental constraints. Driver behavior is what the driver actually does given the previously discussed limitations and constraints and given the driver’s needs, motivation, level of alertness, and personality. This distinction helps clarify differences between the major research paradigms used to study driving behavior. Models designed to predict driver performance are based on cognitive and physiological psychology and most often depict the driver as a limited capacity information processor. Models designed to explain and predict the more complex real on-road behavior are based on theories of personality, social psychology, and organizational behavior and assume that actual driving behavior represents the style and strategy drivers adopt to achieve their goals. In reality, our behavior on the road is a combination of both typical behavior (most of the time we drive) and maximal performance (when we find ourselves in very demanding situations). Thus, both models are complementary, and they are useful in rather different contexts (Shinar, 2007). The distinction is useful in analyzing factors that contribute to crashes. The contributing factors that have been studied include cognitive abilities, social context, emotion, personality, experience, and hazard perception skills. The situation or the context in which the driver drives plays a crucial role in determining the type of actions a driver is likely to take. Without the context, the validation of driving behavior models in real driving situations would be difficult (Rakotonirainy & Maire, 2005). How do drivers make their choices in concrete situations? To what extent are they affected by various potential competing needs and constraints? The answer is not a simple one. For example, to demonstrate how different situations and needs can affect speed choice, Shinar (2001) conducted roadside interviews of 225 Israeli drivers. The interviews were conducted at gas stations along three types of interurban roads with three different speed limits: twolane undivided roads without hard shoulders with a speed limit of 80 km/h (50 mph), improved two-lane roads with hard shoulders posted at 90 km/h (56 mph), and four-lane limited-access divided highways posted at 100 km/h

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Speed category FIGURE 15.6 Average speed in kilometers/hour that drivers report they would select to maximize different goals relative to their actual speed and to the speed limit. Source: Reproduced with permission from Shinar (2001).

(62 mph). At each site, the drivers were asked about the speed that they usually maintain on that road, the speed they select when driving with their family, the speed they would choose if their primary goal was to save on fuel and vehicle wear and tear, the speed they considered safe, and the speed they would choose if there were no enforcement at all on the roaddthe one they would select to maximize pleasure or enjoy the “fun” of driving. Finally, they were tested on their knowledge of the actual speed limit on that road. The results, plotted separately for each road type, are displayed in Figure 15.6. First, note that in general, on all choice dimensions, peoples’ choices are primarily affected by the roadway, with roadways posted at and designed for slower speeds generating lower speed choices. Second, different motives lead to different speed choices. The desire for fun motivates people to drive at the highest speed, whereas frugality and safety motivate them to drive at the lowest speed. Interestingly, the drivers’ perceptions of the “safe” speed were sensitive to the nature of the road, but on the roads with the lower speed limits, the perceived safe speed was slightly higher than the legal speed. This is despite the fact that nearly all drivers on all roads correctly identified the legal speed limit. Finally, the actual speed drivers report driving on the road seems to be a compromise among the various motives, road design constraints, and enforcement, although it does seemdat least in the Israeli driving culturedto be much closer to the “fun” speed than the “safe” speed.

3. DRIVER VARIABLES AFFECTING THE DRIVEReVEHICLE INTERACTION Research on driver behavior and performance has identified at least six relatively independent psychological dimensions

that are likely to impact driver behavior when faced with advanced automobile automation (Stanton et al., 2007): locus of control, mental models, mental workload, situation awareness, stress, and trust. These factors are likely to interact with each other and with automation in a complex and unpredictable way. Useful models should include these dimensions in order to be able to predict driver behavior. For example, by postulating how a driver manages attention to multiple inputs, we can predict the negative effects of underload or overload on performance. This is applicable to support systems that are designed to reduce driver information load (e.g., adaptive cruise control, hazard detection systems, and lane deviation warnings). Thus, reducing mental demand will not necessarily mean that drivers will have spare attentional capacity. Rather, it suggests that reduction in mental demand in the driving task may lead to either (1) corresponding reductions in attentional resources allocated to the driving, and ultimately task-induced fatigue, or (2) engagement in additional tasks (e.g., talking on the phone or text messaging) that may negate the benefits of the original load reduction by inducing driver distraction. Both outcomes are probably counterintuitive predictions for those without HFE training and illustrate the importance of considering HFE in vehicle design. Intuitive introductions of driver support systems to future vehicles without careful considerations of HFE principles and knowledge will more likely lead us into an age of automation nightmare than automation utopia (Stanton et al., 2007). A critical factor in automation is that it shifts the emphasis in the role of the driver from the lower level “vehicle control” to a much higher level of “driving control.” The implication of this shift is that automation not only frees the driver from select physical tasks (e.g.,

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maintaining the vehicle at a constant speed) but also eliminates some of the driver’s cognitive tasks (e.g., deciding whether to brake or accelerate in response to other road users). The foundation of automation philosophy comes from aviation, in which time constants are often very short and cognitive overload is a real risk. The direct application without careful consideration of the differences between the two systems may not be appropriate. Some solutionsdbased on the distinction between hard automation (in which the pilot/driver cannot overrule the automated response) and soft automation (in which the pilot/ driver can to various degrees override the automated response)dhave been offered (Young, Stanton, & Harris, 2007), but the field is still in an embryonic stage.

3.1. Vision and Perception: Physical and Psychological Variables If the eyes are our windows to the mind, then observing eye behavior is a natural tool to use to understand how the mind acquires and processes visual information (Shinar, 2008). The extensive research that has been conducted on eye movements in the past half century clearly establishes that eye movement data reflect moment-to-moment cognitive processes (Rayner, 1998), and that eye movements are closely linked to attention: People tend to direct their gaze and fixations to the objects of their attention (Hoffman & Subramaniam, 1995). Thus, an alert driverdunlike a fatigued or drugged driverdhas a very active eye movement pattern in which the eyes constantly jump (in what is labeled as saccadic movements) from one area of interest to another (collecting information in what is labeled fixations). The information gained from our studies of eye movement behavior can be appreciated when we compare the fixation patterns of novice drivers to those of experienced drivers. Experienced drivers move their gaze among various sources of information, directing most of their fixations ahead on the road where hazards are likely to first appear. In early seminal research, Mourant and Rockwell (1970, 1972) showed that experienced drivers’ fixations are widely dispersed with a modal location slightly above and to the right of the roadway (for U.S. drivers, who drive on the right side of the road), where most signs tend to be concentrated and from where pedestrians may enter the driving lane. As drivers become more familiar with the route, they attend to fewer signs, their fixations become more concentrated, and their modal position drifts closer to the driving lane and farther down the road, where drivers can obtain information with maximal lead time to respond to it. Their studies also revealed that experienced drivers rarely fixate on the lane markers, suggesting that monitoring vehicle position within the lane is accomplished through peripheral visiondand therefore the cues, such as lane markings, should be

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conspicuous. In contrast, beginning drivers have to learn “how to look at their surroundings” just as they have to learn how to control their car. Unfortunately, it takes longer to learn how to acquire information than how to control the car. Thus, novice drivers are much more dependent on their fixations for maintaining their car in the lane, and their fixations are distributed over a much smaller part of the visual scene, much closer to the car and on the lane markings. Also, the novice driversdwho are probably visually overloadeddfixate on their rearview and side mirrors much less than do experienced drivers. In short, novice drivers are much less efficient at obtaining the necessary cues to drive safely and consequently are deficient in their ability to anticipate hazardsda key to safe defensive driving (Borowsky, Shinar, & Oron-Gilad, 2010). Similarly, an analysis of their precrash behavior indicates that they have inadequate visual scanning for potential obstacles and inattention to the driving task in generaldor not looking at the right place at the right time (McKnight & McKnight, 2003). Eye movements are also a powerful tool for assessing time-sharing and workload (Kiger, Rockwell, & Tijerina, 1995; Mourant, Rockwell, & Rackoff, 1969; Rockwell, 1988), for evaluating sign design and placement (Bhise & Rockwell, 1973), for showing impairments from alcohol and marijuana (Papafotiou, Stough, & Nathan, 2005), and for assessing the demands of various road geometries (Shinar, McDowell, & Rockwell, 1977). In approximately the past 10 years, eye movement research findings have also been incorporated into models of the effects of novel in-vehicle systems on visual search ¨ stlund, 2005; Reingold, (Engstro¨m, Johansson, & O Loschky, McConkie, & Stampe, 2003; Salvucci, 2006; Salvucci, Zuber, Beregovaia, & Markley, 2005). This type of modeling can set the stage for what is probably the most intriguing application of eye movements in driving: as triggers for actuating in-vehicle alerting and control systems. For example, eye movement behavior (in combination with driving performance measures) can be used to detect visual driver distraction in real time (Victor, Harbluk, & Engstro¨m, 2005; Zhang, Smith, & Witt, 2006). A note of caution is appropriate in interpreting and using eye movement data. Visual fixations are not synonymous with attention. Although people tend to move their eyes to the targets of their attention, the converse is not true: The location of our fixations does not always reveal the target of our attention. The common phenomenon of “looked but did not see” that precedes many crashes is a testament to that (Stutts, Reinfurt, Staplin, & Rodgman, 2001; Treat et al., 1977). The open eyes always fixate somewhere in space, but the mind is still free to roam and concentrate on nonvisual stimuli, such as auditory inputs (e.g., when talking on the cell phone) and deep thoughts. In fact, in such situations, there is a suppression of much of the

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saccadic eye movements, and the apparent concentration of the driver’s gaze on the road ahead is misleading, making target detection performance poorer (Victor et al., 2005). Focusing the eyes on the right object at the right time is often necessary to perceive safety-related information. However, once focused, how welldor more technically speaking, with what acuityddo we need to be able to see each object? It is a fact that the blind cannot drive. On the other hand, it is not at all obvious how well we need to see in order to drive. As typically measured and defined, visual acuity is a measure of a person’s ability to resolve details when they are presented under optimal illumination (meaning high levels of illumination and no glare), in the middle of the observer’s visual field (meaning when the observer is directly staring at it), with both the target and the observer being static (meaning neither one of them is moving), and under no time constraints (meaning the person can take as long as he or she needs to decide what the detail is). This highly constrained measure of visual performance is very relevant to reading from the board in a classroom (for which it was originally developed by Snellen) or to deciphering the name of a street when stopped at an intersection in the middle of the day. However, driving involves a very different visual task. In driving, none of the previous conditions apply most of the time: The driver is moving relative to the visual environment, the lighting conditions are often far from optimal (night, fog, and glare), emerging dangers typically first appear off to the side of the visual field and not where the driver is necessarily looking (e.g., when a pedestrian darts into the street or a car converges from an adjacent lane), and the driver has very little time to perceive and respond to many hazardous situations. Finally, seeing small details may not be the skill we need at all. Pedestrians and vehicles with which we might collide are not small details, and we do not collide with them because we are unable to read the words on a T-shirt or the license plate number of the car. Measures other than static visual acuity that appear to be much more relevant to driving safety (and licensing) include contrast sensitivity, dynamic visual acuity, and useful field of view (Owsley et al., 1998; Shinar & Schieber, 1991).

of our memory and biases in decision making are critical to our safety, and much of the current vehicle and environmental design is geared toward adjusting for these (as discussed later). Automation is one potential aid to memory and decision making. A simple example is the automatic railroad crossing gates that come down whenever a train reaches a threshold distance from the crossing that would not enable some drivers to cross safely. The effectiveness of such systemsdas with most automated support systemsd depends on their perceived validity. To be safe for nearly all drivers, the flashing lights or gates are often activated when the train is 2.5 minutes away. However, because some drivers consider this a very long lead time, they are tempted to cross the rails despite the flashing lights or the lowered gates because the information provided is incongruent with their direct perceptions (that they still have enough time to cross) (Shinar & Raz, 1982). The alternative approachd that of providing drivers with more information to facilitate their decisionsdalso does not ensure greater safety (e.g., providing pedestrians with countdown signals that indicate the time left to crossing an intersection). Because all of these aids have some levels of errors (either false alarms or misses), driver compliance with the warnings, on the one hand, and reliance on the system, on the other hand, is an issue that has to be resolved empirically for each specific system (Maltz & Shinar, 2004). Consequently, we cannot assume that partial automation of the driving taskdmostly in the form of assistive information and control devicesdis inherently beneficial. Two notable examples are the antilock braking system (ABS) and electronic stability control (ESC). ABS has not been conclusively proven effective in reducing car crashes, despite its demonstrated effectiveness in controlled testing (Farmer, 2001). However, it does seem to be effective in reducing motorcycle fatal crashes (Teoh, 2010). In contrast, ESC has been shown to be very effective (Lie, Tingvall, Krafft, & Kullgren, 2006), and it is now mandatory in all passenger cars in the United States and European Union countries.

3.2. Decision Making and Memory

The separation of the driverevehicleeenvironment system into its three major components is a convenient way to organize the discussion of the various ways that HFE is applied to safety improvements. However, because we are dealing with an integrated system, many approaches to improving driver performance involve more than one component. To illustrate, new and emerging technologies to improve hazard detection, to increase vehicle control, and to shape driver behavior are applied to the interactions between the driver and the roadway and the driver and the vehicle, and often to the interaction among all three

Once a driver attends to and perceives some information, he or she must then decide what to do with it (see Figure 15.4), and that decision is largely dependent on the driver’s decision-making skills and past experience (memory). These decisions are most explicitly detailed in the various hierarchical models (as noted in the previous discussion of the alternative models), and their outcomes can then be described in terms of the time needed to make the decisions and their correctness or errors (see Figure 15.5). The limits

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components. However, to avoid redundancies, the following discussion is organized by the primary component that is addressed by each approach.

4. VEHICLE VARIABLES AFFECTING THE DRIVEReVEHICLE INTERACTION Traditionally, issues of fitting the car to the driver focused on the drivers’ dimensions (anthropometry) and physical abilities (biomechanics). However, in the past three decades, HFE considerations in vehicle and road design have focused primarily on the drivers’ sensory and cognitive limits and capabilities and how they relate to various safety and communications technologies. Thus, HFE issues are at the center of the evaluation of current advanced driver assistance systems (ADAS); new in-vehicle information systems (IVIS) that are designed to support the driver in an appropriate, user-oriented way; and various infotainment systems, such as navigation and cell phones and e-mail communications, that are an increasing source of driver distraction. The primary orientation is usercentered design: optimizing performance through the satisfaction of human needs and performance limitations. These can determine the technical requirements, the usability of ADAS and IVIS, the operability of humane machine interface, behavioral adaptation and risk compensation, acceptance of innovations, and social impacts. To bring some order to the issue, various standards and guidelines for vehicle design include some HFE recommendations. No attempt is made to deal with all of these, but they are discussed briefly where they relate to safety. The objective is to acquaint the reader with the basic vehicle design features that affect driver performance and traffic safety. Thus, the following sections deal with select in-vehicle technologies, cockpit design, field of view and mirrors, intervehicle and driverevehicle communications, and automatic versus manual gear.

4.1. In-Vehicle Technologies Information and communication technologies are increasingly in use on the roads in so-called “intelligent transport systems” (ITS) and in safety-oriented electronic systems (e-Safety). Many of the current ITS applications focus on means to improve road users’ safety, and when the focus is elsewheredfor example, on comfort and traffic managementdthe implications for safety still have to be considered. In general, ITS should support the driving task so that fewer errors will be made and unsafe behavioral choices will be avoided. ITS have different functions: (1) to provide the driver with time-, situation-, and location-dependent information; (2) to provide warnings; and (3) to physically intervene

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with the vehicle control in critical situations. ITS can also be classified based on their technical aspects: Vehicle systems with no outside interaction; roadside systems without interaction with vehicles; and the most “intelligent” categorydsystems with interaction between individual vehicles and other data sources, such as between vehicles or between the vehicle and the roadway. These systems can provide the latest situational information to an individual driverdfor example, weather conditions, temporary speed limits, or hazardous situations farther along the road. Some systems prevent unsafe driving in advance. Examples include alcohol ignition interlocks that prevent drivers from starting their cars if their blood alcohol content exceeds the legal (or other preset) limit and seat belt interlocks that warn drivers if seat belts are not fastened when the motor is running (or even intervene with the driving). Many cars already have a warning system, but the intervention systems go one step further by actually preventing driving in an unsafe mode. The second category of systems is those that prevent unsafe situations or actions while driving. Examples include systems that (1) offer support for vehicle control, such as ABS, ESC, adaptive cruise control (ACC), and lane-keeping support; (2) record and/or prevent intentional and unintentional errors, such as Intelligent Speed Adaptation; (3) offer support in observing, interpreting, and predicting traffic situations, such as forward collision warning; and (4) react to (temporarily) reduced driver capabilities, such as driver drowsiness monitoring and warning. Although in general the potential safety effects of ITS applications are large, the ultimate effects are definitely smaller than expected. This is because initial effectiveness assumes that “all other things remain the same,” whereas in fact everything we have learned of human behavior and the concept of the human-in-the-system indicates that when any part of the system changes, all other parts may change as welldespecially the human component. Possible unintended negative side effects of ITS and e-Safety can lead to the various types of errors discussed previously (see Figure 15.5) and include the following: l

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Underload and diminished attention level: A common assumption (not always correct, however) is that road safety will benefit from reductions in mental workload on the driver. However, when driving tasks are partly replaced by ITS, the level of stimulation decreases to a point at which the driver is in an underload situation that is characterized by reduced attention (e.g., when fatigued or when driving on a very monotonous road). Information overload: ITS can also create additional information that the driver has to or chooses to deal with. For example, navigation systems can also provide

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information about speed, points of interest along the way, time and distance to arrival at target location, etc. All of these can attract attention and impair the driver’s ability to react to dangerous situations that are associated with rapid increases in information (e.g., when a car ahead suddenly stops). Thus, a good ITS requires judicious decisions concerning when and what information to present to the driver. Green (1999) recommended the use of a “15-second rule” for assessment of risks in in-vehicle navigation systems. The rule, which has been adopted by the American Society of Automotive Engineers, stipulates that a task that requires more than 15 seconds to perform while the vehicle is parked should be considered hazardous to driving. Note that this is a human factors performance-based recommendation rather than a vehicle design-based recommendation, acknowledging the value of human factors considerations as a guideline for vehicle-related design. Incorrect interpretation of information: The driver must be able to understand what the system does and what it means. The wrong interpretation of information can have an opposite than intended effect. For example, ABS was designed to improve braking and control performance provided that the driver also adopted a new emergency braking strategydone of continuous hard braking instead of the pumping action in which most drivers on the road today were trained. Thus, assuming that the ABS is a totally vehicle-based system and not one requiring a certain change in the driver’s behavior leads to less efficient rather than more efficient braking. Overreliance on the system: The driver’s expectations of a system must be realistic. Because no assistive system is 100% correct, complete trust can lead to errors and other inappropriate behaviors (e.g., not paying attention to the road). For example, overreliance on an obstacle detecting system when reversing may cause a fatal accident if a small child is behind the car because these systems often fail to detect small objects. Risk compensation: There is evidence that when the system’s benefits are obvious to drivers, drivers are often inclined to compensate for the reduced risk by taking risks they would not otherwise take (e.g., see Wilde’s (1982) theory of risk homeostasis). This can result in a smaller net effect, zero benefit, or even actual increased risk. Obvious examples of risk compensation are the tendency to increase speed on divided highways compared to two-lane roads and the tendency to engage in distracting tasks (e.g., dialing a phone number) in low-traffic situations. However, the issue is whether the drivers manage to retain the same risk level under all situations. Thus, per mile driven, even at the highest speed limits, most divided highways are still safer than

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the two-lane rural roads that they replace. Drivers also increase their speeds when they drive with better tires on slippery (e.g., snowy) roads, but the net effect on crashes is still positive (Fridestroem, 2001). In contrast, ABS has not provided the actual safety benefitsdas measured in crashesdthat were predicted from experimental studies, and in some situations it actually seems to increase crashes (Delaney & Newstead, 2004). Effects on non-users: Especially when not all vehicles are equipped with a particular ITS system, it is possible that some drivers without such a system anticipate the supposed behavior of cars that do have that system. It is also possible that car drivers without such a system would behave as if they did have one (by mistake or imitation).

To improve safety, ITS, as with any application, should be designed in a manner consistent with the capabilities and behaviors of the range of people who will be using them. Furthermore, although the designer of the ITS may have a specific purpose in mind, realization of that objective through design should be consistent with the driver’s task as the intended users understand it. Many ITS applications are not designed to improve safety, but they may still have a road safety effect. Examples include ACC, which keeps a constant speed unless it detects an obstacle ahead, in which case it automatically reduces a car’s speed to avoid a collision. ACC can have both positive and negative effects. Positive effects are obtained when ACC functions as planned, but negative effectsdor even disastrous effectsdcan occur on rare occasions when it fails while the driver’s attention is not focused on the road ahead (e.g., when a pedestrian darts into the road). Driver GPS-based navigation systems are designed to help drivers in their navigation tasks (maneuvering level in Michon’s model; see Figure 15.3), but they have both positive and negative effects on safety. They reduce the driver load by eliminating the need to read street signs and cues for directions, but they distract the driver from the road whenever the driver gazes at them. An analysis estimated both types of effects and concluded that in sum they provide a net safety benefit (TNO, 2007). Another obstacle to predicting the safety effects of a system is its level of penetration into the overall traffic system. In general, however, theoretical analyses, simulation studies, and field operational tests confirm that the effects can be sizeable (SWOV, 2008). An interesting analysis of the potential benefits of various in-vehicle safety systems (not all involving the driver) was conducted as a part of an EU project, eIMPACT, and it provided concrete, unified estimates of traffic and safety benefits in terms of percent fatalities that could be reduced by each of the candidate systems. The findings are presented in Figure 15.7, and they provide both the potential benefits

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potential −18% −16% −14% −12% −10% −8%

−6%

−4%

−2%

year 2020 high penetration rate

0% Electronic stability control, ESC Lane keeping support MAPS&ADAS Dynamic speed adaptation due to weather conditions, obstacles or congestion Emergency braking SASPENCE - Safe speed and safe following eCall Driver drowsiness monitoring and warning Local danger warning Cooperative intersection collision warning Night vision warning Lane change assistant and warning Pre-crash protection of vulnerable road users Full speed range ACC Post crash warning Reversible lanes due to traffic flow

Estimated safety impacts on fatalities (%) of 16 IVS. “Potential” assumed 100% fleet penetration of systems; “Year 2020 high penetration rate” assumed a promoted penetration in 2020.

FIGURE 15.7 Estimated safety effects of intelligent in-vehicle systems based on 80% market penetration and full 100% penetration. Source: Reproduced with permission from Rama (2009).

(assuming 100% penetration) and the “high penetration rate” benefits (assuming 80% penetration). Note that the two are not always correlated. For example, ESC (also known as electronic stability program) is a very effective system, and there is a strong relationship between 100% use and high penetration. In contrast, a lane-keeping support system has a high potential benefit, but at 80% penetration, the expected benefit is very small.

4.2. Cockpit Design The primary focus in vehicle occupant packaging is the driver’s workstation. Implementing ergonomic methods into the design of the driver workspace and interface is essential to ensuring a safer, healthier, and more comfortable driving experience. It is important to consider both drivers’ limitations and capabilities (in terms of vision, hand reach, anthropometry, and force) and design constraints (e.g., available space, collision safety, aesthetic design, and size of components). The driver “package” usually refers to the spatial dimensions of the intended user population when considering locations and adjustment ranges of the steering wheel, the seat, and the pedals; the physical locations of controls and displays with which the driver interacts; and the visual

field afforded by the widows, windshield, and mirrors (Parkinson & Reed, 2006). In fitting the car to the driver, it is not enough to consider what the driver can handle or see; how the driver prefers to interact with the vehicle interior must also be considered. Drivers with similar anthropometric dimensions may have different preferences in optimal locations of controls and displays (e.g., some like to sit erect, and some like to slouch). Failure to include variability in preference that is not attributable to anthropometry can produce misleading design recommendations that do not take into sufficient account the need for adjustability (Garneau & Parkinson, 2009). The process of designing a new vehicle involves fulfilling a large number of requirements. A list of recommended guidelines regarding occupant packaging for vehicle interior designs is provided by the Society of Automotive Engineers (Jung, Cho, Roh, & Lee, 2009).

4.3. Field of View and Mirrors Visibility from inside vehicles can be either direct (the environment is observed directly) or indirect (the environment is observed through mirror reflection). Direct visibility is determined mainly by the sizes and locations of

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the windows: A wider field of view includes more environmental cues and hence increases the ability to detect potential hazards. Indirect visibility is affected by the width, reflectance, and types of mirrors. For example, convex mirrors provide a much wider field of view allowing detection of objects that would otherwise be missed, but they create distortions in distance due to minification of the objects viewed, leading to errors such as overestimating the distance from these objects (Dewar & Olson, 2002). ITS-based systems are also improving the field of view with cameras to provide video views of difficult-to-see locations, such as immediately behind and in front of buses and trucks where pedestrians are otherwise invisible (Bota & Nedevschi, 2008; Nedevschi, Bota, & Tomiuc, 2009).

4.4. Intervehicle and DrivereVehicle Communications Icons can be used to communicate information to the driver in a language-free and space-efficient manner. Incomprehensible icons have the potential to affect safety (Campbell et al., 2004). Icons are generally preferred to text messages because there are no language barriers and a driver licensed to drive in any country can therefore drive almost any car in another country. However, icons are problematic when their meaning is not immediately apparent to all users. Icons representing rare events may not be comprehensible to all drivers; therefore, in some situations, text labels are preferable. In general, comprehension is improved when an icon is highly familiar, its use is standardized, and its graphics are compatible with the content it conveys (e.g., the image of a person with a shovel to indicate a work zone) (Ben-Bassat & Shinar, 2006). When these conditions are not met, it may be better to use a text label, despite its obvious limitations. Using the previously discussed principles makes most of the displays on car dashboards relatively easy to understand. However, with increasing possibilities of displaying all kinds of information, increasingly complex and crowded displays can compromise safety by distracting the driver from the road and the traffic. For example, the use of air-conditioning was traditionally associated with a single button. Today, there are multiple gages providing climate controls in different parts of the car. The basic idea behind ergonomically “good” in-car warning technology is to provide drivers with information that is otherwise not directly perceivable. Thus, there are indicators relating to the operational state of the vehicle (e.g., fuel level, oil pressure, and water temperature) and to the performance of the vehicle (e.g., speed). In the past, this information was typically displayed either in the form of (hard) dials or colored lights. Currently, much more sensitive and exact information on many more vehicle functions is displayed via digital soft displays but at the cost of more complicated

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program controls. For critical information, some of the visual information is also augmented with auditory feedback, such as extreme low fuel and seat belt reminders. A difficulty encountered by all drivers is detecting passing and overtaking vehicles while they are in the driver’s “blind zones”dthose zones that are outside the forward field of view afforded by the windshield and front windows and the rearview mirrors. A recent aid is an alerting system that notifies the driver if there is a nearby vehicle in the adjacent lane whenever the driver signals an intention to switch lanes. A critical human factors issue is the nature of this warning. Should it be auditory or visual, and if it is visual, where should it be displayed. An auditory system that is gaining popularity is Mobileye (Mobileye, 2010). Reed and Flannagan (2003) suggested placing such a warning along with turning signals on the outside rearview mirrors. They found that it is more visible than conventional turn signals. Furthermore, these signals are closer to the driver’s line of sight when his or her vehicle is in or near the blind zone (Reed & Flannagan, 2003). Sivak, Schoettle, and Flannagan (2006) evaluated the effects of mirror-mounted turn signals on the frequency and the severity of turn signal-related crashes. The results indicated a tendency for vehicles with mirror-mounted turn signals to be less likely involved in relevant crashes, but the effect was not significant and could have been confounded by other safety-related factors. Furthermore, there are doubts regarding the possibility of a reduction in crash severity for vehicles with mirror-mounted turn signals.

4.5. Automatic Versus Manual Gears During the twentieth century, the automobile evolved from a humble horseless carriage into one of the most technologically advanced mass market ubiquitous humane machine systems. Currently, we are starting to see a new generation of technologies enter the vehicle in the form of driving automation. Of course, vehicle automation has been around for some time, with electric starters and automatic gearboxes becoming widely available in the first half of the previous century. However, whereas traditional automatic systems sought to take over the lower level, operational components of vehicle control (see Figure 15.3), new technologies are taking over more tactical and even strategic aspects of driving (Ranney, 1994). Thus, Stanton and Young (2000) distinguish between two types of automation: vehicle automation that addresses low-level vehicle operations (vehicle control) and driving automation that addresses tactical (maneuvering) and strategic-level functions. An immediate by-product of automation is a change in the driver information processing load, often reducing the cognitive costs of driving. Shinar, Meir, and Ben-Shoham (1998) had novice and experienced drivers drive their own

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cars along a previously selected course of urban streets and requested them to note whenever they saw a “Slowd Children” or a “No Stopping” sign. They found that the novice drivers with a manual gearshift detected significantly fewer signs (of both types) than novice drivers with automatic gearshift, whereas experienced driversdwho in general detected more signsddid not manifest this difference between the two gearboxes. Automation is gradually taking over the driver’s role, with some going so far as to predict full vehicle automation for British roads by 2030 (Walker, Stanton, & Young, 2001). Given the state-of-the-art today, driver needs (some people like to have control and choose manual gears), the highly dynamic environment of driving (a pedestrian may dart into the road), and the high stakes involved (people can get killed), we believe that this is highly unlikely. Also, although such a shift can reduce the driver’s load (as in the gearbox example), assuming the driver does not stay at home while the car goes shopping, it can also fundamentally change the driver’s role from that of controller to that of a monitor. This in turn creates new difficulties, most notably that of situation awareness (Endsley, 1995)d a situation in which the driver’s world differs from that of the automated system. In the context of driving, although ITS may behave in exactly the manner prescribed by the designers and programmers, it may lead to some scenarios in which the driver’s perception of the situation or the actual but unanticipated reality is at odds with the system operation (Stanton & Young, 2000).

5. ENVIRONMENTAL VARIABLES The road infrastructure conveys a wealth of informationdsuch as road signs and markings and implicitly by means such as road layoutdthat guides drivers’ activities. Although there are circumstances in which drivers have to react to some unexpected event, usually drivers execute planned actions that are shaped by their expectations of the road, traffic scenarios, and the reality they actually face. The role of the environment is secondary to human factors in the causes of accidents, but it is much more significant than that of vehicles (see Figure 15.1). Treat et al. (1977) found that view obstructions are the most frequent environmental cause of accidents, followed by slick roads. In an analysis of the causes of accidents (National Highway Traffic Safety Administration, 2008), the environment was listed as a critical reason for the crash of 16% of the vehicles. In these crashes, slick roads were listed in approximately 50%, glare was listed in 16.4%, and the weather was listed in 8.4%. In a naturalistic driving study, Dingus et al. (2006) monitored the crash involvement of 100 cars during a period of approximately 1 year. Although the number of accidents was low, the detailed monitoring of the vehicles

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provided insights into the specific crashes that are not otherwise available in post-crash analysis. They defined 18 types of conflicts (e.g., conflict with either a lead, adjacent, or following vehicle, single-vehicle conflict, and conflict with an obstacle or with another road user), with each describing the factors that precipitated, contributed to, and were associated with the event. The environment was classified in terms of its static or dynamic nature, with the former consisting of the infrastructure and the latter consisting of the changing driving environment (including traffic, visibility, and weather). The infrastructure category includes the factors that are fixed and do not change with the environment: (1) Trafficway flowdone way and divided roadway; (2) traffic control devicedsignals and signs; (3) localitydinterstate and residential areas; (4) roadway alignmentdstraight, curve, level, or hillcrest; and (5) relation to junctiond intersection and entrance/exit ramp. The driving environment category consists of conditions that change on a daily or hourly basis: (1) surface conditiondwet or snowy; (2) lightingdstreetlamps or daylight; (3) traffic densitydstable flow and limited speed or flow; and (4) atmospheric conditionsdclear or rainy. For example, results for the single-vehicle crashes revealed that infrastructure and driving environment (e.g., weather and visibility, roadway alignment, and roadway delineation) were contributing factors in 29% of the crashes, in 23% of the single-vehicle near crashes, and in only 10% of the incidents. In the case of “lead-vehicle crashes,” when an interaction occurred between the subject vehicle and the vehicle directly in front of it, the environmental factors were not judged to be a strong contributing factor, with only one crash being due to weather and visibility. This is somewhat surprising considering that more than 40% of the crashes included inclement weather and wet or snowy surface conditions. Not surprisingly, traffic flow was fairly strongly associated with the lead-vehicle crashes and near crashes. The infrastructure associated with the crashes and near crashes was straight and level in most of the crashes (87%). Regarding the lead-vehicle incidents, none of the driving environment factors were identified as contributing, and only one crash infrastructure factor (i.e., roadway delineation) was identified as contributing. Roadway alignment may have played a role, with 42% of the crashes being on curves. Two-thirds of the crashes were intersection related. Inadequate road infrastructure or poor environmental conditions can potentially impact road user behavior and performance in a way that can potentially lead to road user errors. These inadequate conditions include confusing layout, misleading signage, poor road surface-related conditions, poor weather conditions, poor lighting conditions, time of day, and misleading or inappropriate rules and regulations (Stanton & Salmon, 2009).

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5.1. Roadway Design Driver workload can also be affected by the roadway design. Therefore, road design should meet driver expectations and take into account the abilities and limitations of all road users (Dewar, 2008). Long sight distances and divided highways with wide, high shoulders that enable high speeds ease the driver workload, whereas congested roads, frequent curves, narrow bridges, short sight distances, and in general situations that increase uncertainty increase workload. Here, too, current practices are designed with these goals in mind. Good examples are those of positive road guidance and self-organizing roads. The positive guidance approach enhances the safety of hazardous locations by providing drivers with direct visual cues of how to behave. For example, delineation and analogous curve signs that visually display the amount of curvature are preferable to text-based signs. This approach combines the highway engineering and human factors technologies to produce an information system matched to the characteristics of the location and the attributes of drivers (Alexander & Lunenfeld, 1979). The self-organizing roads are design principles that increase the probability that a driver will “automatically” select appropriate speed and steering behavior for the roadway without depending on road signs or enforcement. The geometric features of the road encourage the desired driver behavior, and thus compliance does not rely on the driver’s ability or willingness to read and obey road signs. A roundabout is a self-organizing road. The road geometry forces the driver to slow down. Pavement markings help the driver perceive this lower speed requirement. Also, intentionally narrowing the roadway and shoulders creates self-organizing features that instruct the driver to slow down. When there is a conflict between road features and road signs, drivers may often follow the speed implied by the roadway design rather than the speed instructed by the road sign. Another important example of a self-organizing road is the 2 þ 1 roadway, which is a three-lane road with the passing lane alternating on each side of the road in a regular manner. This organizes the driver’s expectations about being able to pass. These roads also work well for speed management. Studies of human reaction time helped formulate standards for geometric roadway design (Keith et al., 2005).

5.2. Roadway Illumination Roadway lighting is designed, fabricated, and installed for the public benefit at night. It is difficult to quantify its value because it rests not only on its concrete implementation and operation costs but also on its expected benefits, which are inherently difficult to estimate. Most studies have revealed that roadway lighting provides positive safety and security

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benefits (i.e., crash prevention) for drivers and pedestrians (Rea, Bullough, Fay, Brons, & Van Derlofske, 2009; ROSPA, 2007). Roadway lighting has been suggested as a relatively low-cost intervention with the potential to prevent traffic crashes by improving drivers’ visual capabilities and ability to detect roadway hazards and by reducing contrast between headlight glare and the surrounding environment. However, it is also argued that roadway lighting could have an adverse effect on road safety; drivers may “feel” safer because lighting gives them improved visibility, which could result in drivers increasing speed and reducing concentration (Beyer & Ker, 2009). Hogema, Veltman, and Van’t Hof (2005) measured the effects of variations in motorway lighting on driver behavior and concluded that when the lighting was switched off, mental effort increased because heart rate and blink rate increased and speed decreased. When drivers have to deal with higher workload (i.e., task demand), they often have two options: They can increase their effort or they can try to reduce the task load, for example, by reducing speed. Thus, drivers have more time available to anticipate potential hazards, and this reduces the workload. The potential safety benefit of roadway lighting can be summarized as follows: l

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Darkness (or the absence of lighting) results in an excessively large number of crashes and fatalities, particularly those involving pedestrians. Lighted intersections and interchanges tend to have fewer crashes than unlighted ones. Visibility is primarily associated with crashes involving pedestrians and intersections at night. Thus, in these scenarios, the roadway lighting might have the greatest effect with regard to the reduction of nighttime crashes.

5.3. Traffic Control Devices: Road Markings, Signs, and Signals Understanding drivers’ comprehension and predictive behavior of traffic control devices is critical. If the driver does not understand the message being presented, then his or her response may vary significantly and affect safety. Intersection traffic control devices are composed of signs, signals, roundabouts, or pavement markings that can be placed alongside the intersection. They are used to move vehicles and pedestrians safely and efficiently, consequently preventing collisions by providing the “rightof-way” principle assignment. The most extensively used devices for current traffic control include traffic signals, stop signs, and roundabouts. Improper placement of a traffic control device may decrease its efficiency. If drivers recognize the signal too late to safely react to the situation, an increase in the

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number of accidents at the intersection may occur. One such example is placing the device too closely around the bend of a sharp curve. Catastrophic results will occur when drivers fail to stop in time. The primary goal of all traffic control devices is to maintain the safety of the drivers advancing through the intersection. Some conventional devices currently in use have significant shortcomings that can actually hinder safety. Traffic signal lights are one such example. When configuring a signal light, an engineer must be careful with regard to the duration of the green, red, and amber (yellow) phases. An extensively studied issue is that of the yellowlight dilemma. If the yellow phase is too short, drivers might have to slam on their brakes to avoid crossing the intersection before the light turns red. This could and does cause an increase in rear-end collisions. On the other hand, if the yellow light is on for too long, drivers might ignore it and speed up to cross the intersection, and this candand doesdincrease the likelihood of side crashes in the intersection (which are typically more severe than rear-end crashes) (Fadi & Hazem, 2009). The duration of the green and red phases also appears to affect driving behavior: Drivers tend to run red lights more when the green phase is short and the red phase is long than when the green phase is long and the red phase is short (Shinar, 1998), and they also tend to do so more when the green phases in consecutive intersections are not synchronized than when they are (Shinar, Bourla, & Kaufman, 2004).

6. CONCLUSION The aim of this chapter was to provide a brief understanding of the approaches to driverevehicle modeling and the impact of driver, vehicle, and roadway characteristics on highway traffic safety. As vehicles have evolved, there has been a gradual shift from a vehicle-centered approach to a user-centered approach to designing vehicles and roadways. This requires a scientifically based understanding of the motivations, capabilities, and limitations of people in operating their vehicles and in correctly perceiving the roadway and traffic demands. The need for this understandingdexpressed in models of driver behaviordis underscored by the fact that the overwhelming majority of traffic accidents involve a human error, most often a misperception of the situation or inappropriate human decision or action. Here, “to err is human” is not an empty statement. However, the role of human factors in highway safety is not simply to identify the human failings and recommend ways to correct them but, rather, to identify the elements in the driverevehicleeroadway that give rise to these errors. Thus, the aim is not to train drivers not to err but, rather, to prevent the situations that give rise to errors. In this chapter,

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we discussed some of the areas in which human factors research has contributed to this goal. Because of space constraints and potential overlap with other chapters, many areas were excluded in this discussiondsuch as the effects of impaired or distracted driving, age, and individual differencesdbut the reader can find more about these areas in the other chapters. More information on human factors in highway safety can also be found in Dewar and Olson (2007) and Shinar (2007).

REFERENCES Alexander, G. J., & Lunenfeld, H. (1979). A users’ guide to positive guidance in highway control. Human Factors and Ergonomics Society Annual Meeting Proceedings, 23(6), 452e455. Ben-Bassat, T., & Shinar, D. (2006). Ergonomic guidelines for traffic sign design increase sign comprehension. Human Factors, 48(1), 182e195. Beyer, F. R., & Ker, K. (2009). Street lighting for preventing road traffic injuries. Cochrane Database of Systematic Reviews CD004728. Bhise, V. D., & Rockwell, T. H. (1973). Toward the development of a methodology for evaluating highway signs based on driver information acquisition. Highway Research Record, 440, 38e56. Borowsky, A., Shinar, D., & Oron-Gilad, T. (2010). Age, skill, and hazard perception in driving. Accident Analysis and Prevention, 42, 1240e1249. Bota, S., & Nedevschi, S. (2008). Multi-feature walking pedestrians detection for driving assistance systems. Intelligent Transport Systems, 2(2), 92e104. Cacciabue, P. C., Mauri, C., & Owen, D. (2003). The development of a model and simulation of aviation maintenance technician task performance. International Journal of Cognition, Technology & Work, 5, 229e247. Campbell, J. L., Hofmeister, D. H., Kiefer, R. J., Selke, D. J., Green, P., & Richman, J. B. (2004). Comprehension testing of active safety symbols. (Paper No. 2004-01-0450). Warrendale, PA: SAE International. Carsten, O. (2007). From driver models to modelling the driver: What do we really need to know about the driver? In P. C. Cacciabue (Ed.), Modelling driver behaviour in automotive environments (pp. 105e120) London: Springer. Chapanis, A., Garner, W. R., & Morgan, C. T. (1949). Applied experimental psychology. New York: Wiley. Delaney, A., & Newstead, S. (2004). The effectiveness of anti-lock brake systems; A statistical analysis of Australian data. Proceedings road safety research, policing and education conference. Perth: Road Safety Council of Western Australia. Delorme, D., & Song, B. (2001). Human driver model for SmartAHS. (California PATH Research Report No. UCB-ITS-PRR-2001-12). Berkeley: University of California Press. Dewar, R. E. (2008). The role of human factors in road safety audits. In Proceedings of the 18th Canadian multidisciplinary road safety conference. British Columbia: Whistler. June 8e11, 2008. Dewar, R. E., & Olson, P. L. (2002). Human factors in traffic safety. Tucson, AZ: Lawyers & Judges. Dewar, R. E., & Olson, P. L. (2007). Human factors in traffic safety (2nd ed.). Tucson, AZ: Lawyers & Judges.

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Factors Influencing Safety Belt Use Alcohol-Impaired Driving Speed(ing): A Quality Control Approach Running Traffic Controls

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Chapter 16

Factors Influencing Safety Belt Use Jonathon M. Vivoda and David W. Eby University of Michigan Transportation Research Institute, Ann Arbor, MI, USA

1. INTRODUCTION In 1763, the Frenchman Nicolas Joseph Cugnot invented the first “automobile” (Chambers, 1902; Sinclair, 2004). It was self-propelled using steam actuation and could travel only for approximately 10e15 min before stopping to build up more steam (Sinclair, 2004). Cugnot’s invention was difficult to control, and in 1771 he crashed it into a stone wall, creating the first recorded automobile crash in history (Figure 16.1; Bellis, 2010; Sinclair, 2004). As you might imagine, Cugnot’s vehicle was not equipped with safety beltsdit was not even equipped with brakesdbut luckily for him, it could travel only slightly faster than 2 miles per hour, and no one was hurt in the crash. Although there were no injuries in this very first crash, this story illustrates that even from the earliest development of automobiles, there has been a need for safety devices to help keep motorists safe. The seat belt, or safety belt, was invented in the 1800s by George Cayley, and the first patent was registered to Edward J. Claghorn in 1885 (Carter & Maker, 2010; Kett, 2009). One of the most important safety devices ever invented, its early application was primarily for use in aircraft to keep pilots in their seats during maneuvers. In the

1930s, a group of physicians in the United States recognized the potential value of safety belts in motor vehicles and began to urge automobile manufacturers to install them in cars as standard equipment. These doctors felt so strongly about this cause that they even took it upon themselves to equip their own cars with safety belts (School Transportation News, 2010). It was not until the 1950s that a few vehicle manufacturers began to include safety belts in vehicles, and during the 1950s and 1960s, several states began to require vehicles to have safety belt anchors installed (Kahane, 2004; School Transportation News, 2010). In 1968, a U.S. federal standard went into effect requiring auto manufacturers to include safety belts in all new vehicles (Kahane, 2004; National Highway Traffic Safety Administration (NHTSA), 1998).3

2. EFFECTIVENESS Today, safety belts are standard equipment for every seating position in every vehicle produced for sale in the United States. Since their early development, researchers have continued to assess the effectiveness of safety belts and make design improvements. Early safety belt designs FIGURE 16.1 Depiction of Nicolas Joseph Cugnot crashing his automobile into a wall. Source: Wikipedia.

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10016-5 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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reduced injuries compared to riding unbelted, but they also had some serious weaknesses. For example, the first designs typically included only one belt strap (EDinformatics, 2010). Designs with a strap that only went across the waist (lap belts) left a motorist’s upper torso vulnerable to injury, and designs with only a sash (a strap that went over the shoulder) sometimes resulted in motorists sliding forward under the sash belt during a collision. A more effective design combined the two into one belt (often called a three-point belt), a variation of which is still used in most modern vehicles (Kahane, 2004). The modern three-point belt is a very effective safety device. Given a crash, these belts are estimated to reduce the likelihood of a fatality by approximately 40e45% and to reduce the likelihood of an injury by as much as 80%, depending on the type of crash and vehicle (Cummings, Wells, & Rivara, 2003; NHTSA, 2001). Between 1960 and 2002, safety belts were responsible for preventing 168,524 motor vehicle deaths in the United States (Kahane, 2004), and in 2008 alone, the estimated number of prevented fatalities was 13,250 (NHTSA, 2008). These numbers highlight the enormous public health impact safety belts, belt use legislation, and belt use intervention programs have had. When combined with a modern air bag, the likelihood of injury and death is reduced even more (NHTSA, 2001). Most cars today also include many other safety features as standard equipment (e.g., antilock brakes, crumple zones, and third brake lights), as well as optional safety equipment (e.g., adaptive cruise control, lane departure warnings, and rearview cameras). However, there is an important key difference between safety belts and other safety features such as air bags and third brake lights: motorists must use safety belts in order for them to be effective.

3. MEASUREMENT The gold standard for measurement of safety belt use is a methodology known as direct observation. As the name suggests, data collectors look into passing vehicles and visually observe the safety belt use of occupants in those vehicles. Direct observation studies are expensive and difficult to coordinate, but compared with other methods, they provide a less biased accounting of the true belt use rate for the area under observation. If sampled and conducted properly, these studies can provide an accurate “snapshot” of belt use that is representative of a much larger area, including a state, region, or even a country (Chaffe, Solomon, & Leaf, 2009; Eby, Vivoda, & Cavanagh, 2009; Pickrell & Ye, 2009a). Direct observation also has some important limitations, however. By definition, this method only allows for collection of data on phenomena that can be readily

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Key Problem Behaviors

observed. Generally, such studies only assess vehicle type, seating position, sex, age, race, and, of course, safety belt use. Understanding why people choose not to wear safety belts on a given trip, or identifying underlying factors that can influence belt use, cannot be assessed. In addition, because this technique is unobtrusive, data collectors must observe and make judgments about many of the vehicle occupants’ characteristics. The validity of these judgments is rarely assessed because the only way to do so would be to stop vehicles and ask occupants their age, race, etc.dan approach that would be disruptive and thus is rarely used. For a more detailed description of the benefits and problems associated with direct observation surveys, see Chapter 5. Aside from direct observation, several other methods are also used to assess safety belt use. One of the most popular is self-report. Chapter 4 provides a complete description of self-report instruments and methods; issues specifically related to safety belt use are described here. Self-reported belt use is usually obtained by using either telephone interviews or paper-and-pencil questionnaires. These types of surveys are much less expensive to administer than direct observation, but research has shown that they tend to be less accurate, with self-reported belt use nearly always higher than observed use (Hunter, Stewart, Stutts, & Rodgeman, 1993; Nelson, 1996; Streff & Wagenaar, 1989). This consistent difference is usually attributed to either social desirability bias or a mismatch between how people understand the survey questions and what the researchers actually intended. Social desirability bias refers to the tendency for participants to respond in a way that they believe will be viewed favorably by others (Paulhus, 2002). Because most people believe that wearing a safety belt is a good thing to do, to avoid being judged negatively by others, some people may respond affirmatively even when they do not wear a safety belt. The second common reason for higher self-reported belt usedthe misunderstanding of the question being askeddis best illustrated in the Motor Vehicle Occupant Safety Survey (MVOSS). In the MVOSS, participants were asked how often they wear a safety belt while driving. Immediately following this question, they were asked when was the last time that they did not wear a safety belt while driving. Of the 88% of drivers who reported wearing a safety belt “all of the time” while driving, 10% immediately reported not wearing their belt within the past month (Boyle & Lampkin, 2008). It is clear that many respondents did not interpret the meaning of “all of the time” to be literal but, rather, an approximation, thus creating measurement error within the survey results. For these reasons, direct observation methods are used when it is important to estimate the most accurate belt use rate possible, but self-report is used when a richer exploration of the factors that influence safety belt use is needed.

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Factors Influencing Safety Belt Use

Self-report surveys can easily assess age and race, as well as many other factors that influence belt use (i.e., income and education), by simply including questions about them. This method can also explore why people choose to wear or not to wear safety belts in given situations. This method allows us to better understand how factors such as the expected length of a given trip or the purpose of a trip influence one’s decision to wear a safety belt. Because these different methods allow us to answer different questions about safety belt use, it is important to continue to use both to best understand all aspects of this behavior. Other data sources are also sometimes used to determine belt use rates, including police reports, crash databases, and motor vehicle fatality databases. These tend to be used less often because of the inherent biases associated with them. Safety belt citations from police records are often not aggregated across precincts, making analysis of population-wide belt use difficult. In addition, these records only contain information for motorists who actually received a citation, and these motorists were often originally stopped for some other reason, resulting in no viable comparison group of those who were in compliance with the law. Data from crash and fatality databases can also present problems largely because everyone represented in the database was involved in a crash (or fatal crash). These motorists are not representative of the larger population, thus making it difficult to generalize belt use to the motoring public. However, even given these inherent weaknesses, one valuable use for these data is to evaluate changes in belt use over time. For such an analysis, it is not necessary to generalize the results, and any biases that exist will presumably be constant at both points in time. Although direct observation methods offer the most accurate estimation of safety belt use, self-report methods, citation databases, and crash databases can all be valuable for assessing different aspects of safety belt use. Depending on the available funding and the questions that need to be answered, each of these sources has its place in a researcher’s repertoire. However, when using each of these methodologies, it is important to understand the strengths and weaknesses of the given approach.

4. INTERNATIONAL SAFETY BELT USE According to the World Health Organization (WHO, 2009), 1.27 million people died globally in traffic crashes during 2008. WHO estimates that by 2030, there will be 2.4 million yearly traffic crash-related fatalities throughout the world, and that traffic crashes will be the fourth leading cause of death. The same report also suggests that the regular use of safety belts could significantly reduce this projected fatality number.

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Table 16.1 shows published safety belt use rates from a sample of countries. There are clear differences, with belt use varying from 11 to 96%. Note, however, that there are some challenges when comparing safety belt use rates. Table 16.1 also includes the data collection method, sample, area, and year(s) data were collected, and each of these factors could impact the reported rates. Data from studies of city or regional belt use may be quite different from countrywide data. Indeed, statewide belt use in the United States during 2009 ranged from 67.6% in Wyoming to 98.0% in Michigan (Chen & Ye, 2010). Despite these issues, safety belt use clearly differs among countries for a variety of reasons. One important reason is the presence of a mandatory safety belt law. A study of 178 countries found that only 88% had national or regional laws requiring use of a belt, and only 57% of countries required restraint use by all vehicle occupants (WHO, 2009). Furthermore, the economic condition of countries was related to the likelihood of a mandatory belt use law, with only 54% of low-income countries surveyed having a belt use law. Even with a law, belt use can be low if enforcement of the law is weak. The WHO (2009) report found that only 19% of countries reported strong enforcement of their law. In addition, use laws cannot be effective if vehicles are not equipped with safety belts. Only 29% of countries that manufacture vehicles had regulations requiring safety belts for all vehicle seating positions (WHO, 2009). China, for example, only started requiring all vehicles sold in that country to have belts for all seating positions in 2004 (Routley et al., 2008). International belt use rates also vary because of cultural differences among countries. Among university students studied in 15 different countries, attitudes toward the value of safety belts were significantly related to countrywide belt use rates (Steptoe et al., 2002). In the United Arab Emirates, cultural and attitudinal differences about safety belts may account for the low belt use within this high-income, developing country (Barss et al., 2008). Likewise, low safety belt use observed in Russia may be due in part to the lack of a culture around using belts (Akhmadeeva, Andreeva, Sussman, Khusnutdinova, & Simons-Morton, 2008). Other studies have addressed the effects of cultural beliefs in destiny (fatalism) on belt use and found that use of belts is lower among populations who hold those beliefs (Peltzer, 2003). From a global perspective, increasing the use of belts can have a profound impact on the reduction of unintentional injury. WHO (2009) recommends a 5-point strategy to increase belt use worldwide: (1) Require vehicle manufacturers to install belts in all seating positions; (2) improve laws to require safety belt use in all vehicle seating positions; (3) strengthen enforcement and ensure

218

PART | III

Key Problem Behaviors

TABLE 16.1 A Sample of Safety Belt Use Rates in Countries Throughout the World Country

Rate (%)

Method

Sample

Area

Year(s)

Reference

Argentina

86.0

Not reported

All vehicles

City

2004

Silveira (2007)

Australia

96.0

Direct observation

Not reported

Country

2009

Australian Automobile Association (2010)

Belgium

76.0

Self-report

University students

Country

2000

Steptoe et al. (2002)

Canada

92.5

Direct observation

Occupants, cars

Country

2006e2007 Transport Canada (2008)

China

55.1

Direct observation

Drivers, cars

Two cities

2005e2007 Routley et al. (2008)

Columbia

32.0

Crash data

Occupants, all vehicles

Country

2005-2006

O’Bryant (2008)

Costa Rica

82.0

Direct observation

Occupants, cars

Country

2004

FIA Foundation (2005)

England

95.0

Direct observation

Drivers, cars

Country

2009

Walter (2010)

France

91.5

Self-report

University students

Country

2000

Steptoe et al. (2002)

Germany

76.5

Self-report

University students

Country

2000

Steptoe et al. (2002)

Greece

57.5

Self-report

University students

Country

2000

Steptoe et al. (2002)

Hungary

73.0

Self-report

University students

Country

2000

Steptoe et al. (2002)

Iceland

84.0

Self-report

University students

Country

2000

Steptoe et al. (2002)

Ireland

85.5

Self-report

University students

Country

2000

Steptoe et al. (2002)

Italy

83.5

Direct observation

Drivers, cars

Region

2005

Zambon et al. (2008)

Jamaica

81.2

Direct observation

Front outboard, cars

City

2004

Crandon et al. (2006)

Native United States

55.4

Direct observation

Front outboard, cars

Tribal 2004 land, USA

Leaf and Solomon (2005)

Netherlands

86.0

Self-report

University students

Country

2000

Steptoe et al. (2002)

Poland

76.5

Self-report

University students

Country

2000

Steptoe et al. (2002)

Portugal

94.0

Self-report

University students

Country

2000

Steptoe et al. (2002)

Russia

77.9

Direct observation

Not reported

Territory

2006

Global Road Safety Partnership (2010)

Scotland

95.0

Direct observation

Drivers, cars

Country

2009

Walter (2010)

Slovenia

95.6

Self-report

Front outboard, cars

Country

2000

Bilban and Zaletel-Kragelj (2007)

South Africa

81.0

Crash data

Drivers, all vehicles

Country

2002

Olukoga and Noah (2005)

Spain

80.0

Self-report

University students

Country

2000

Steptoe et al. (2002)

Turkey

35.5

Self report

Convenience sample

City

2007

S¸ims¸eko glu and Lajunen (2009)

United Arab Emirates

11.0

Direct observation

Drivers, all vehicles

City

2003e2004 Barss, et al. (2008)

United States

83.0

Direct observation

Front outboard, cars

Country

2008

that enforcement is equal for all seating positions; (4) establish systems to collect data on rates of safety belt use; and (5) supplement all efforts with a mass media campaign that highlights the safety benefits of belt use, the increased likelihood of being cited for non-use, and the penalties for being cited.

Pickrell and Ye (2009a)

5. SAFETY BELT USE IN THE UNITED STATES In 1984, New York became the first state to enact a law requiring belts to be worn by occupants riding in the front seats of vehicles. Prior to this first law, the overall U.S. belt use rate was estimated to be between 16 and 18% (Hedlund,

Chapter | 16

Factors Influencing Safety Belt Use

219

Gilbert, Ledingham, & Preusser, 2008). Many other states quickly followed New York’s example, and belt use increased dramatically throughout the United States. By 1987, 20 states and the District of Columbia had enacted belt use legislation, and U.S. belt use had increased to more than 40%. By 1995, every state except New Hampshire had passed a law, with belt use estimated at approximately 70% (Hedlund et al., 2008). Achieving this level of belt use, and nearly universal belt use legislation, was quite an accomplishment, but closer analysis of both revealed an important limitation. When many of these belt use laws were passed, they were done so with what was termed “secondary enforcement.” Secondary enforcement means that a police officer cannot stop a motorist only because safety belt non-use is observed; the motorist must be stopped for some other infraction (e.g., speeding) and can then also be cited for failing to buckle up. Indeed, 42 of the original 50 jurisdictions enacted secondary laws (Hedlund et al., 2008). Because secondary laws are much more difficult to enforce, states with this provision generally have much lower belt use rates than primary enforcement jurisdictions. During the 1990s, several states began efforts to upgrade their enforcement provisions to primary, with California becoming the first to do so in 1992 (Hedlund et al., 2008). When a state changes its belt use law from secondary to primary enforcement, a specific pattern of use typically

emerges. Immediately following the legislative change, belt use increases dramatically, but then it generally decreases somewhat as the months continue. As the rate stabilizes, overall use remains at a significantly higher level than was observed before the change (Figure 16.2). The median observed safety belt use increase in states that have made this change is 14 percentage points (Shults, Elder, Sleet, Thompson, & Nichols, 2004), which translates to a substantial number of reduced injuries and deaths. NHTSA (2009) estimates that for each percentage point increase in U.S. belt use overall, approximately 270 lives are saved. However, as of 2010, only 30 states (and the District of Columbia) allow for primary enforcement, so there is still work to be done (Insurance Institute for Highway Safety (IIHS), 2010). Aside from legislative changes, interventions have also been very effective at increasing belt use. The most famous and most successful safety belt intervention is known as “Click It or Ticket” (CIOT; Hedlund et al., 2008). CIOT utilizes widespread media messages informing the public that police will be specifically focusing on safety belt use enforcement during a given time frame. These messages are supplemented with high-visibility police enforcement of the safety belt law. Coupling the media messages with enforcement serves to increase motorists’ perception of the likelihood of receiving a ticket for safety belt non-use. As

100.0 90.0 80.0

Use rate, percent

70.0 60.0 50.0 40.0 30.0 20.0 10.0

Safety belt law implemented

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

0.0

Primary enforcement implemented Year

FIGURE 16.2 Example of safety belt use in Michigan, 1983e2009. Source: Based on data from Datta and Savolainen (2009); Eby, St. Louis, and Vivoda (2005); O’Day and Wolfe (1984); and Streff, Molnar, and Christoff (1993).

220

PART | III

a collective result of all these effortsdfrom the original safety belt laws to the primary enforcement upgrades and the many iterations of various safety belt intervention campaignsdsafety belt use in the United States is at an alltime high of 84% (Pickrell & Ye, 2009a). Although this is an impressive increase from where belt use began, there are still key groups that continue to ride unbelted. Males, young people, pickup truck occupants, motorists in rural areas, and several other groups use safety belts less often than their counterparts (Pickrell & Ye, 2009a, 2009b). The following section describes the factors that influence belt use among different groups and explains what is known about why these differences exist.

6. FACTORS THAT INFLUENCE SAFETY BELT USE What is currently known about why individuals within various “low belt use groups” wear safety belts less often than others is described in the subsections that follow. Figure 16.3 depicts these differences in belt use. This figure allows for comparisons within a given category (e.g., belt

Key Problem Behaviors

use by vehicle type), but it also gives the reader an idea of the scope of the differences in belt use across categories (e.g., pickup truck occupants compared to young people). However, because no single study collects data about all these factors, this figure was created using several different sources, so some comparisons should be made with caution. In addition, a given individual is likely to fit into several of these categories simultaneously (e.g., a young male driver in a rural area), and Figure 16.3 does not account for these potential confounders.

6.1. Part-Time and Non-Users Prevalence rates of safety belt use are often considered on an “all-or-nothing” basis. We tend to think of people in a given group as either more or less likely to wear a safety belt compared to people in other groups. Although there is value in taking this group-based approach, it is also important to consider the variation within a given group at both the situational and the individual level. Self-report surveys and qualitative research have identified the existence of three distinct types of safety belt users: (1) people

0-3

20.0

Some college college grad

>$100 K

Daytime

$15-30 K

Primary

Other White

Driver

Suburban

Urban

Female

25-69 70+

4-7 8-15

5.0

Cars Vans/SUVs

-20.0

Vehicle type

Age

Gender

Population Seating denstiy position

Race

Vehicle Law purpose type

Time of day

Income†

$30-50 K $50-75 K $75-100 K

<$15 K

Nighttime

Secondary/none

Black

Commercial (medium/heavy)

-15.0

Front passenger Rear passenger

-10.0

Rural

-5.0

Male

16-24

0.0

Pickups

Percentage point difference from mean*

10.0

Non-commercial

15.0

Education†

FIGURE 16.3 Differences in safety belt use by various factors. *83% for direct observation surveys; 88% self-reported. yPercentage of selfreported “always” use. Source: Data from Boyle and Lampkin (2008), Chaudhary and Preusser (2006), Federal Motor Carrier Safety Administration (2009), and Pickrell and Ye (2009a, 2009b, 2009c).

Chapter | 16

Factors Influencing Safety Belt Use

who always wear a safety belt, (2) those who never wear a safety belt, and (3) part-time (or situational) safety belt users. Although the exact proportion of people within each of these groups is unknown, there is general consensus that those who never wear safety belts in any situation comprise a relatively small group (Boyle & Lampkin, 2008; Committee for the Safety Belt Technology Study (CSBTS), 2004; Hunter et al., 1993). From a safety standpoint, “all the time” users do not need an intervention, leaving parttime users as the largest group on which to focus. Regardless of the relative proportions, understanding how part-time users and non-users differ from each other has very important implications for intervention development. Although people who never use safety belts comprise a relatively small group, it is important that these motorists are not ignored. Several studies have demonstrated that risky behaviors tend to co-occur in people (Brener & Collins, 1998; CSBTS, 2004; Harre´, Field, & Kirkwood, 1996; Williams, Wells, & Reinfurt, 1997). Those who fail to buckle up are more likely to speed (CSBTS, 2004), use cell phones while driving (Eby & Vivoda, 2003), accumulate more traffic violations (S¸ims¸eko glu & Lajunen, 2009), commit more driving errors (S¸ims¸ekoglu & Lajunen, 2009), drive under the influence of alcohol, and be involved in fatal crashes (CSBTS, 2004). The cooccurrence of these behaviors suggests that if safety belt use within this group can be increased, the traffic safety impact can be even greater by mitigating the potential for injury that arises with these other risky driving behaviors. When belt use rates in a given jurisdiction are very high, it is likely that those who continue to travel unbelted include a higher proportion of these “hardcore” non-users, creating a challenge for program planners. Indeed, this group generally does not acknowledge the benefit of wearing safety belts (CSBTS, 2004) and is more likely to cite discomfort and a resistance to being told what to do as reasons for safety belt non-use (Boyle & Lampkin, 2008). The “stages of change” theory (transtheoretical model (TTM)) suggests that these individuals may be in a “precontemplation” stage and are therefore not likely to change their behavior in the near future (Kidd, Reed, Weaver, Westneat, & Rayes, 2003). For these individuals, the most effective strategy may be to acknowledge their beliefs and design interventions that move them through the stages of change rather than focusing on changing belt use immediately. Developing such an intervention would be a good future test of the application of the TTM to this population. At the very least, interventions should specifically address belt use comfort and libertarian attitudes toward belt use laws as focal points. More severe measures, such as ignition interlocks or license demerit points, could also be considered if educational and enforcement-based programs are ineffective (for a review of issues surrounding safety belt technologies such as interlocks, see CSBTS, 2004).

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For part-time users, it is important to understand what factors influence the choice to wear a safety belt or ride unbuckled. In a review of the literature conducted in 1973 (almost a decade before even the first U.S. safety belt laws began to be enacted), several of these factors were identified, including the traffic environment (e.g., driving in cities versus on highways), traveling short distances, and driving at lower speeds (Fhane´r & Hane, 1973). Interestingly, traveling only a short distance and driving in light traffic remain two of the most popular situational reasons for nonuse today (Boyle & Lampkin, 2008). Other often-cited reasons for non-use include forgetting to put the belt on, being in a rush, and the influence of others in the vehicle (Boyle & Lampkin, 2008). Each of these reasons suggests different potentially effective solutions. For example, the problem of forgetting to use a safety belt could be mitigated by increased utilization of safety belt reminder systems (Farmer & Wells, 2010). In addition to these conscious choices, there may be subconscious influences that affect safety belt use in given situations. Issues such as repression, denial, rationalization, and fatalism are related to how one views the world (including risks related to traffic safety), and all are known to influence safety belt use (Brittle & Cosgrove, 2006). The level at which one engages in this type of thinking may affect whether or not one chooses to buckle up on a per-trip basis, or it may influence an individual’s decision-making heuristic that is utilized for a given belt use situation. Experts recommend increasing mindfulness (one’s attention to these subconscious motivators), enhancing belt use self-efficacy, increasing the social desirability of wearing safety belts, disrupting resistance to use, and encouraging anticipatory regret as ways to mitigate these factors (Brittle & Cosgrove, 2006). Conducting more basic research, to understand the extent to which these factors influence both part-time and non-users, is an important first step.

6.2. Vehicle Type A group that has historically been one of the least likely to buckle up is those who travel in pickup trucks (see Figure 16.3). Direct observation studies typically find belt use to be approximately 10e12 percentage points lower for these motorists (Nichols et al., 2009; Pickrell & Ye, 2009a). Research studies have identified several reasons why this difference exists. One explanation can be directly linked to policy decisions that were made when early safety belt laws were created that exempted motorists traveling in pickups (Wells, Williams, & Fields, 1989). Arguments were made that pickup trucks at the time were predominantly used on farms, and that they often were only on public roadways for a short time driving from one field to another. Arguments for this

222

exemption were tenuous at the time and are now generally viewed unfavorably for two main reasons. First, the way pickup trucks are used has dramatically changed in the 25 years since these early laws took effect. What was once considered a work tool, with few creature comforts, has evolved into a common alternative to driving a car, van, or SUV. Second, we now know that vehicular crashes are more likely to occur on short trips than on longer trips, thus making it even more important for farm workers riding in pickups to buckle up. Throughout the years, many states have removed the pickup truck exemption from their safety belt law, with Georgia being the last to do so in 2010 (Badertscher, 2010; Governors Highway Safety Association, 2010). However, even for states without an exemption for pickup trucks, belt use remains lower within this group. Larger vehicles typically fare better in a crash than small vehicles, and this may give people a false sense of security when riding in a pickup (Nitzburg & Knoblauch, 2004). This belief may lead these motorists to think that it is less important to wear a safety belt when riding in a pickup truck than in a different type of vehicle. There may also be more subtle reasons underlying the lower safety belt use rate within this group. Anderson, Winn, and Agran (1999) found that pickup truck owners are more likely to be male, have higher household incomes, and have lower educational levels. The fact that members of this group tend to have higher household incomes but wear safety belts less often is counterintuitive and should be investigated in future research. Qualitative studies have further explored this issue and noted that pickup truck occupants believe that the size of their vehicle will protect them from serious injury, that safety belts are not needed for short or work trips, and that they report a fear of being trapped in the vehicle if a crash occurs (Boyle & Lampkin, 2008; Nitzburg & Knoblauch, 2004; Rakauskas, Ward, & Gerberich, 2009). Some pickup truck drivers also cite the lack of a motorcycle helmet law as justification for ignoring the safety belt law and are opposed to government-mandated behaviors in general (Boyle & Lampkin, 2008; Nitzburg & Knoblauch, 2004). When they do wear safety belts, this group cites the presence of their family or friends, traveling on highways, and traveling during inclement weather as reasons for use (Nitzburg & Knoblauch, 2004). It seems that at least for some, the size and purpose of these vehicles is a real deterrent to safety belt use. These motorists seem to be more likely than others to make a decision about safety belt use on a trip-by-trip basis or to have a preexisting decision heuristic that differs by vehicle type and various situational factors.

6.3. Age In general, children tend to have fairly high levels of restraint use. However, once a young person is old enough

PART | III

Key Problem Behaviors

to drive, restraint use declines dramatically, and then it slowly increases across the life span. One likely contributor to these changes is that when children are young, their parents assume the responsibility to keep them safe. The vast majority of trips made in cars by children are with their parents as drivers. When teenagers gain more autonomy and begin to drive themselves, their parents are no longer present to insist on safety belt compliance. Teenagers and people in their early twenties have lower belt use than motorists of any other age group (approximately 4e6 percentage points lower than that of other groups; Eby et al., 2009; Pickrell & Ye, 2009b). Self-report studies find that young people also report wearing belts less often than older vehicle occupants (Boyle & Lampkin, 2008). When asked about their reasons, people of different ages identify different factors that influence their use and non-use of safety belts. More young people than older people report wearing a safety belt because of their desire to avoid receiving a ticket, and fewer young people report that “it’s a habit” as a reason for use (Boyle & Lampkin, 2008). Compared to their older counterparts, young people’s reasons for non-use are more likely to include forgetting, being in a rush, discomfort, and driving in light traffic (Boyle & Lampkin, 2008). Understanding differences such as these has helped to inform the development of successful interventions, such as CIOT, designed primarily to increase belt use among young people, particularly males. Evidence-based interventions such as CIOT are effective because they use what is known about a given populationdyoung drivers in this casedand apply that information to existing behavior change theory. CIOT focuses mostly on the constructs of “perceived threat” and “perceived benefits” as described in the health belief model (Rosenstock, Strecher, & Becker, 1988), increasing one’s perception of the likelihood of receiving a ticket. Media messages disseminate the message and high-visibility police enforcement reinforces it. CIOT also describes the associated consequences of safety belt nonuse (e.g., citation and fine) to affect a change in one’s perceived benefits or “outcome expectations” (social cognitive theory) of the behavior (Rosenstock et al., 1988). Other research has suggested that differences in the biological, developmental, and neurobehavioral development of adolescents may influence the risky behaviors of young people. Changes that occur during puberty, for example, may lead to increased risk-taking and sensationseeking behaviors (Dahl, 2008). Adolescents may be less able to control their emotions and may incorrectly assess their behaviors as less risky than they are (Begg & Langley, 2000; Halpern-Felsher et al., 2001; Keating & HalpernFelsher, 2008). Young people also have less driving expertise than their older counterparts (Keating & HalpernFelsher, 2008), which may lead to inappropriate conclusions about the riskiness of their driving behavior.

Chapter | 16

Factors Influencing Safety Belt Use

223

Most safety belt studies combine young and middleaged adults into a single group and older adults into a separate group (age 65 years is usually used as the cutoff between adulthood and older adulthood). Belt use is higher for both of these groups compared to teens, and most studies find that as motorists move into older adulthood, belt use increases slightly again (Boyle & Lampkin, 2008; Pickrell & Ye, 2009b). In general, people older than age 65 years have the highest safety belt use rate of any age group. High belt use for older motor vehicle occupants is particularly important because they are at a higher risk of crashing, per mile driven (IIHS, 2010). In addition to the higher crash risk, older people are more likely to be injured during a crash (Liu, Utter, & Chen, 2007), primarily due to increased frailty associated with medical conditions that are more common among older people (Centers for Disease Control and Prevention, 2008). High safety belt use among older motorists can help to mitigate the potential for injury, but it should also be noted that older people may be more likely to suffer injuries related to safety belt use. However, any injuries resulting from a safety belt are likely to be much less severe than if the older person was riding unbelted during a crash. For a more complete discussion of the issues related to aging drivers, see Chapter 24.

In fact, risk perception seems to play a very important role in understanding the gender differences in many risky behaviors, including safety belt non-use. Compared to females, males generally perceive all behaviors to be less risky and tend to engage in more risky behaviors. Gender differences in risk perception have been consistently found in many different fields outside traffic safety, as well as for various risky driving behaviors (Lundborg & Andersson, 2008; Martins, Tavares, Lobo, Galetti, & Gentil, 2004; Powell & Ansic, 1997). Indeed, risk of receiving a safety belt citation is perceived to be lower among males compared to females (Chaudhary, Solomon, & Cosgrove, 2004), and males perceive safety belts to be less useful than do females, given a crash scenario (Calisir & Lehto, 2002). However, it is also important to note that because of males’ greater tendency to engage in risky behaviors, simply increasing males’ perceived risk to the level of females’ may not be enough to level the gender gap in belt use (Brener & Collins, 1998; Harre´ et al., 1996). A more complete understanding of why gender difference in sensation seeking exists may help to better inform interventions. For a literature review of behavioral and biological correlates (including gender) of sensation seeking, see Roberti (2004); for a review of sensation seeking and driving behaviors, see Jonah (1997).

6.4. Gender

6.5. Population Density

Male motorists make up another low belt use group that has been very well documented, with belt use often observed to be approximately 5e7 percentage points lower than that of females (Boyle & Lampkin, 2008; Kim & Kim, 2003; Pickrell & Ye, 2009b). Males and females give similar reasons when asked about why they choose not to use safety belts, but males are slightly more likely to report “low probability of a crash” as a reason (Boyle & Lampkin, 2008). However, when asked about why they choose to wear belts, their reasons differ, with females being more likely than males to state feeling uncomfortable without it, a desire to set a good example, and because it is the law (Boyle & Lampkin, 2008). The lower likelihood of males to report wearing belts because it is the law is supported by other work related to a stronger libertarian attitude. Tipton, Camp, and Hsu (1990) found that males are more resistant to laws that try to regulate their behavior and therefore may not internalize the behavior. In a study of people who live in a state with a secondary safety belt law, researchers found that males are less likely than females to support passage of a primary law (Perkins, Helgerson, & Harwell, 2009). These results suggest that a law alone may not be enough to change males’ behavior, and they again highlight why programs that focus on perceived risk of negative consequences (e.g., CIOT) have been successful for this group.

The type of area in which people travel also seems to affect safety belt use. Motorists traveling in rural areas generally have belt use rates approximately 4e6 percentage points lower than those in nonrural areas (Boyle & Lampkin, 2008; McCartt & Northrup, 2004; Pickrell & Ye, 2009a). Lower safety belt use in rural areas is of particular concern because crashes are more likely to occur in these areasda consistent risk noted when calculating the rate based on population or vehicle miles of travel (Brown, Khanna, & Hunt, 2000). Differences in the design elements of rural roadways, such as higher speed limits, more narrow roadway shoulders with ditches, and the lack of barriers in the median, all contribute to this difference (Ward, 2007). If a crash does occur in a rural area, the occupants in the vehicle are also less likely to survive because of the longer emergency response times and the increased distances to physicians and other medical resources (Clark, 2003; Melton et al., 2003). These challenges highlight the importance of primary prevention of injuries and fatalities for rural motorists, which in this case involves reducing crashes whenever possible and increasing safety belt use to reduce injury if a crash does occur. Preventing crashes and injuries, however, is obviously much more difficult in practice than in theory. Program planners should consider the factors that are known to affect the belt use of motorists in these areas. One potential

224

reason for low belt use among this group is the relative lack of other vehicles on the roadways, which may lead to the belief that safety belts are unnecessary. Some have also suggested that there may be a different safety “culture” in rural areas, where rural drivers engage in riskier driving behaviors and perceive the risks associated with these behaviors to be lower than do their urban counterparts (Rakauskas et al., 2009). In addition, rural drivers perceive both enforcement-based and engineering-based traffic safety interventions as less useful than do urban drivers (Rakauskas et al., 2009). This finding may make it particularly difficult to develop and implement effective interventions for these motorists. Rural and urban areas may also be composed of different types of people. Studies that only investigate differences in belt use by geographic area may be confounding their results with other important factors. Strine et al. (2010) used regression analyses to explore rural belt use while controlling for age, gender, race, education, marital status, employment status, body mass index, and type of safety belt law (primary vs. secondary). This study revealed a generally decreasing trend: as population density decreased, reporting of “always” wearing a safety belt also decreased. Collectively, these results suggest that not only is living in a rural area an independent factor that influences safety belt use but also additional factors, such as race or age, may lower the likelihood of belt use even further. The apparent interplay of these factors also highlights the importance of using behavior change theory to properly tailor interventions specifically for this population.

6.6. Seating Position When safety belt use by drivers is compared to use by front seat passengers, studies have consistently found that passengers exhibit slightly lower belt use (usually approximately 2 or 3 percentage points; Chaffe et al., 2009; Pickrell & Ye, 2009a). Although this minor difference has been well documented in the literature, it is interesting to note that there is an extremely strong association between driver and passenger belt use. In fact, if a driver is unbelted, passengers are 70 times more likely to also be unbelted compared to passengers riding with a belted driver (Kim & Kim, 2003). This relationship may be due to similarities between people who tend to travel together in vehicles. They may share beliefs and attitudes about traffic safety or risk taking, or these similarities may be due to behavior modeling or peer pressure. Safety belt use in the rear seating position is substantially lower than that of front seat occupants and is one of the lowest of any group (as shown in Figure 16.3). During 2008, rear seat belt use in the United States was 74%, compared to 83% for front seat occupants (Pickrell & Ye,

PART | III

Key Problem Behaviors

2009c). Examining U.S. trends over time reveals that major increases in rear seat belt use have occurred since 2004, when the observed rate was only 47%, 33 percentage points lower than occupants riding in the front seat during that year. This increase may be the result of recently implemented legislative changes in several states that now require rear seat occupants to wear safety belts, but approximately half of the states continue to exempt these occupants (IIHS, 2010; Pickrell & Ye, 2009c). Studies based on self-report have also examined rear seat safety belt use and found that only 58% of rear seat passengers report always buckling up (Boyle & Lampkin, 2008). A potential explanation for this finding may be that people feel safer when riding in the back seat of a vehicle. Research has demonstrated that there is a decreased risk of both injury and death when traveling in the back seat, but it is still risky to ride unbelted in any seating position (Smith & Cummings, 2004). It is also true that relatively few trips are made by adults in the rear seating positions. Indeed, national estimates suggest that only approximately 7.4% of trips made by adults are in the rear seat, whereas 79.3% of children’s trips are in the back (Trowbridge & Kent, 2009). Adults riding in the back should not be ignored, however, because as Trowbridge and Kent correctly note, this low proportion still translates into 19.1 billion rear seat persontrips in a given year. Coupling this number of trips with the lower belt use observed and reported by adults in this seating position further highlights this importance.

6.7. Race Safety belt use also tends to differ by race. National U.S. direct observation surveys, statewide surveys, and smaller scale surveys have all found differences by this factor (Lerner et al., 2001; Pickrell & Ye, 2009b; Vivoda, Eby, & Kostyniuk, 2004). In the National Occupant Protection Use Survey, belt use was approximately 8 percentage points lower for vehicle occupants identified as black compared to those identified as white (Pickrell & Ye, 2009b). However, this disparity may be due to differences in the enforcement provisions of safety belt laws. Several studies have found no differences in belt use by race within jurisdictions that allow for primary enforcement but lower belt use among black motorists in secondary jurisdictions (Briggs et al., 2006; Reinfurt, 2000, as cited in Wells, Williams, & Farmer, 2002). One potential explanation for these belt use differences may be differing perceptions of police enforcement between racial groups. When asked about one’s chances of receiving a citation for failing to wear a safety belt, for example, blacks (Boyle & Lampkin, 2008; Preusser & Preusser, 1997) and Hispanics (Boyle & Lampkin, 2008) are significantly more likely than whites to state there is a high likelihood of receiving a ticket. Another study that interviewed people

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Factors Influencing Safety Belt Use

who had actually received safety belt citations assessed feelings of being “singled out” for the ticket they had received. Motorists identified as black or “other” were significantly more likely than whites to report feeling singled out for the ticket because of their race (Eby, Kostyniuk, Molnar, Vivoda, & Miller, 2004). Negative past experiences with police, and the perception that police may be singling them out based on race, may account for the higher belt use among minorities in primary enforcement jurisdictions compared to secondary enforcement areas. These apparent differences in the perception of police enforcement may also make belt use among minorities particularly sensitive to legislative changes or increases in police enforcement.

6.8. Vehicle Purpose Safety belt use by vehicle purposedcommercial versus noncommercialdhas also been investigated, and belt use in commercial vehicles is generally lower. In a U.S. study, drivers of medium and heavy commercial vehicles had a belt use rate of 72% in 2008 (compared to 83% for noncommercial vehicles; Federal Motor Carrier Safety Administration (FMCSA), 2009). Belt use for occupants traveling in light commercial vehicles (cars, SUVs, pickups, or vans) is also generally lower than that of noncommercial vehicle motorists (Eby, Fordyce, & Vivoda, 2002). Many of the factors that influence safety belt use among the general public also affect those driving commercial vehicles. For example, commercial vehicle belt use is higher in states with primary enforcement, in urban areas, when traffic is traveling faster, among females, and among older drivers (FMCSA, 2009; Kim & Yamashita, 2007). However, there are also unique issues among this group that provide both challenges and opportunities for intervention development. Many commercial vehicle drivers cite the fact that they often must make frequent stops, only travel short distances, and drive very large vehicles as reasons why they do not wear safety belts (FMCSA, 2010; Kim & Yamashita, 2007). Commercial drivers are also more likely to report “always” wearing a safety belt when traveling in their personal vehicle compared to when they drive their commercial truck (Kim & Yamashita, 2007). For these motorists, there seems to be something unique about either the vehicle or the type of trip that leads to lower safety belt use. Addressing these issues within an intervention can be a challenge, but these should be clear focal points. The fact that the trips made in commercial vehicles are part of the driver’s job is another key element that can be addressed by interventions at an organizational level. Indeed, many companies have policies that require their drivers to wear safety belts. Employees who receive “constant

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encouragement” from their supervisors are the most likely to report always using a safety belt while driving their commercial vehicle (Kim & Yamashita, 2007). Such policies may explain the fact that direct observation studies of belt use have found that truck drivers for major regional or national fleets have belt use rates more than 10 percentage points higher than those among independent ownere operators (FMCSA, 2010). Continuing to focus on commercial vehicle drivers and passengers is extremely important because these motorists spend more time on the roadways, thus increasing their exposure and crash potential.

6.9. Income and Education Income and education are two additional factors that influence safety belt use, but the literature regarding these effects are mixed. The MVOSS shows very little difference in belt use by income level, but it does reveal differences by education (Boyle & Lampkin, 2008). Respondents with a college education reported always wearing a safety belt 91% of the time, compared to an 87, 84, and 88% level of “always” use by those with education levels classified as less than high school, high school graduate, and some college, respectively (see Figure 16.3). Injury avoidance is a popular reason given for safety belt use among all groups, but it is increasingly likely to be given as the primary reason for using a belt as educational attainment increases. The group with higher educational attainment is also much less likely to state discomfort from the belt as a reason for driving unbelted (Boyle & Lampkin, 2008). Stating that “it’s the law” as the main reason for use is more likely among those with less formal education, and higher levels of fatalism are more likely to be expressed among people of lower incomes (Shin, Hong, & Waldron, 1999) and lower educational levels (Boyle & Lampkin, 2008). Perceived risk has also been explored as a potential factor that contributes to differences in belt use by income and education. Contrary to typical findings in which an increase in perceived risk is associated with an increase in safety belt use, people who live in higher income areas actually report lower perceived risk of receiving a belt use citation but a greater likelihood of reporting always wearing a safety belt (Chaudhary et al., 2004). To explain these counterintuitive findings, Chaudhary et al. suggest that the higher levels of education often related to higher incomes may be responsible for this difference and moderate the typical effect of perceived risk. More educated individuals may recognize the obvious safety benefits of buckling up, regardless of citation risk. This potential relationship should be explored in future research projects to allow for a better understanding of how each of these factors contributes to one’s likelihood of wearing a safety belt.

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6.10. Nighttime Lower safety belt use at night was first noted in research using large crash and fatality databases. Although the rates obtained using these databases are not representative of belt use within the motoring public, the markedly lower belt use rate observed for crashes that occurred at night compared to those during the day was concerning (Chaudhary, Alonge, & Preusser, 2005; Li, Kim, & Nitz, 1999; McCartt & Northrup, 2004; Salzberg, Yamada, Saibel, & Moffat, 2002; Vivoda, Eby, St. Louis, & Kostyniuk, 2007). However, until recently, nighttime direct observation surveys were not conducted because of the inherent difficulties associated with visually observing belt use in the dark. If observation locations were chosen only where there was enough ambient lighting (i.e., from businesses or street lamps), these “convenient” locations would be biased and not allow for the results to be generalized. With the advent and increased availability of night vision equipment during the early to mid-2000s, researchers and government agencies began to conduct such studies. The first nighttime direct observation study was conducted on a small scale in 2004 (Chaudhary et al., 2005), and a few more have been conducted since then (Chaudhary & Preusser, 2006; Vivoda et al., 2007). Most of these observational studies have found that belt use is lower at night, but the results are mixed. When a difference in belt use has been observed, nighttime use has been approximately 6 percentage points lower than daytime use. Given that the technological ability to conduct this research is so new, much more work remains to be performed to better understand the scope of this potential problem. The reasons why people may use safety belts less often at night are also unknown. One potential explanation may be a difference in the perceived risk of receiving a citation at night. People may believe that the limited visibility at night creates a lower risk of receiving a safety belt ticket because police officers cannot see into their cars. People may also believe that there are fewer officers patrolling the streets at night. In addition, motorists driving at night may not be the same people as those who drive during the day. Those who tend to drive at night may engage in more risky behaviors in general, including lack of safety belt use and impaired driving. Exploring these differences will be critical to developing effective future interventions for nighttime motorists.

7. CONCLUSIONS As this chapter has described, there has been substantial progress in increasing safety belt use during the past few decades, both in the United States and internationally. The effectiveness of safety belts, and of mandatory use laws, has resulted in enormous public health savings in terms of both

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Key Problem Behaviors

injuries and fatalities, in addition to medical costs. There remains room for improvement, however. Previous research has identified many of the groups who tend to use safety belts less often, and many countries throughout the world continue to have extremely low rates of use. Policy decisions such as requiring all vehicles produced for all countries to have belts installed is a necessary first step, as is continued enactment of primary enforcement legislation. Research that addresses differences in belt use by demographics, situational characteristics, environmental aspects, and psychological factors must also continue. As more work is done in this area, it is important for researchers to consider that a single individual belongs to many different groups and is influenced by many different factors simultaneously. Future research can consider the interplay between these factors by conducting analyses that control for the influence of covariates, test statistical interaction effects, and use hierarchical models. This type of research and analysis will contribute to a better understanding of how different factors influence each other, which factors are most important to focus on, and how to develop effective evidence-based interventions. Such programs are critical to allow for the most efficient use of the scarce resources available for this work and to ensure continued progress toward reducing injuries, fatalities, and costs associated with safety belt non-use.

ACKNOWLEDGMENTS This chapter was developed through the support of the Michigan Center for Advancing Safe Transportation throughout the Lifespan, a University Transportation Center sponsored by the U.S. Department of Transportation’s Research and Innovative Technology Administration (Grant No. DTRT07-G-0058), the University of Michigan Transportation Research Institute, and donations from several organizations. Lisa J. Molnar and Rene´e M. St. Louis provided valuable feedback on the manuscript. Amanda Dallaire provided administrative support. The contents of this chapter reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.

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Chapter 17

Alcohol-Impaired Driving Krystall Dunaway,* Kelli England Will* and Cynthia Shier Saboy Eastern Virginia Medical School, Norfolk, VA, USA, yVirginia Commonwealth University, Richmond, VA, USA

*

Portions of this chapter are adapted from a chapter titled “Large-Scale Prevention of Alcohol-Impaired Driving” in Advances in Psychology Research, 40 (2006), by Kelli E. Will and Cynthia Shier.

1. INTRODUCTION The World Health Organization (WHO) reports that 1.3 million people die annually on the world’s roads, and 20e50 million people sustain nonfatal injuries (WHO, 2009). Although the frequency of drinking and driving varies between countries, it is almost universally a major risk factor for road traffic crashes (WHO, 2004). Traffic crashes are the leading cause of death in the United States for 4- through 34-year-olds and rank third in years of potential life lost for all ages combined (Centers for Disease Control and Prevention (CDC), 2010; National Highway Traffic Safety Administration (NHTSA), 2007c). Alcohol impairment of drivers is considered the most important contributing cause of car crash injuries (Connor, Norton, Ameratunga, & Jackson, 2004). Among all age groups, alcohol consumption is the third leading cause of death and motor vehicle crashes (including alcoholimpaired driving) are the sixth leading cause of death in the United States (Mokdad, Marks, Stroup, & Gerberding, 2004). There are more than 159 million episodes of impaired driving each year in the United States, and there are nearly two deaths every hour in alcohol-related traffic crashes (NHTSA, 2004, 2007c). NHTSA (2008d) reports that the arrest rate for driving under the influence (DUI) is 1 for every 139 licensed drivers in the United States. Other studies (Levitt & Porter, 2001; Rauch et al., 2010) report that the arrest rate is 1 for every 27,000 miles driven by drunk drivers, and that a person can drive while impaired by alcohol 200e2000 times before being arrested once. Among the 36,790 U.S. traffic fatalities in 2008, 32% (11,773) were alcohol related (Drew, Royal, Moulton, Peterson, & Haddix, 2010). These numbers represent one fatality every 45 minutes in alcohol-related crashes. Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10017-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

Intoxicated drivers are involved in more motor vehicle crashes, they need more health resources, and are 17 times more likely to be involved in a fatal crash than unimpaired drivers (WHO, 2004). One motor vehicle fatality typically costs society $1,300,000 (this includes wage and productivity losses, medical expenses, administrative expenses, motor vehicle damage, and employers’ uninsured costs) (National Safety Council, 2009). In 2008, alcohol-related crashes in the United States cost $64 billion (Shults et al., 2009). These startling statistics have placed alcohol-impaired driving prevention among the nation’s top priorities. Alcohol-impaired driving prevention has been a riskreduction priority for more than two decades (U.S. Department of Health and Human Services, 2000, 2010), and the United States has made great strides toward decreasing DUI occurrences (Drew et al., 2010). However, after nearly two decades of steady decline (NHTSA, 2007b), the number of deaths due to drunk driving has leveled off. After a brief review of background information regarding impaired driving, this chapter reviews multiple prevention approaches to decreasing DUI-related risk. Emphasis is placed on summarizing the relative effectiveness of the most common approaches to the prevention of DUI, and generally focuses on efforts in the United States. Finally, two exemplary community intervention trials that combine multiple prevention approaches are discussed as model prevention programs.

2. BACKGROUND 2.1. Effects of Alcohol on Driving Ability Driving-related skills include alertness, divided attention, vigilance, visual tracking, and quick reaction time to everchanging information and the ability to execute maneuvers based on these decisions (NHTSA, 2009b; Schermer, 2006). Drinking alcohol impairs a wide range of skills necessary for performing these tasks (National Institute on Alcohol Abuse and Alcoholism (NIAAA), 2001a). Blood 231

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alcohol concentration (BAC), expressed as the percentage of alcohol in deciliters of blood, helps provide an indication of how much alcohol an individual has consumed in the past few hours. A positive BAC is present in 33e69% of fatally injured drivers and in 8e29% of nonfatally injured drivers (Fabbri et al., 2005). A BAC of 0.02 is enough to affect a driver’s ability to divide attention. At a BAC of 0.05, a driver suffers impairment in eye movements, glare resistance, visual perception, reaction time, steering ability, information processing, and other aspects of psychomotor performance (NIAAA, 2001a). The risk of a motor vehicle crash increases as BAC increases and is complicated further with increased demands of the driving task. The risk of a singlevehicle fatal crash for drivers with a BAC of 0.10e0.14 is 48 times higher than the risk for sober drivers. With BACs of 0.15 or higher, the risk is 382 times higher (McCammon, 2001; NIAAA, 2001a). Approximately 85% of BAC-positive drivers involved in crashes are above the legal limit of 0.08 (Yi, Chen, & Williams, 2006), and more than 50% of drivers in fatal crashes have BACs at or above 0.16 (NHTSA, 2003a). For young, inexperienced drivers under the influence of alcohol, the risk is even greater (NIAAA, 2006a). In fact, inexperienced drivers with BACs of 0.05 have 2.5 times the risk of a crash compared to more experienced drivers (WHO, 2004). Behavioral tolerance is thought to explain this increased risk. The repeated performance of a task in association with alcohol consumption can lead to the development of an adaptation referred to as learned or behavioral tolerance (NIAAA, 2001a). That is, for a welllearned task such as repeatedly driving a certain route home from a bar after drinking, behavioral tolerance decreases the impairment ordinarily associated with alcohol consumption. However, when conditions unexpectedly change (e.g., a raccoon dashing in front of one’s car), the tolerance is negated (NIAAA, 2001a).

2.2. Drug-Impaired Driving Although research shows that drugs other than alcohol (namely marijuana, cocaine, and methamphetamine) are involved in 16e18% of motor vehicle driver deaths, they are generally used in combination with alcohol (Fitzpatrick, Daly, Leavy, & Cusack, 2006; NHTSA, 2007c, 2009b). This chapter focuses on alcohol-impaired driving; however, the reader is referred to NHTSA (2009a) for an excellent review of drug-impaired driving. The following is a summary of what is currently known about drugged driving: l

l

High doses generally have a larger effect than small doses. Well-learned tasks are less affected than novel tasks.

l

l

Key Problem Behaviors

Certain variables, such as prior exposure to a drug, can either reduce or accentuate expected effects, depending on circumstances. Drugs affect driving behavior by slowing one’s ability to perceive things or by increasing response time. Amphetamines may encourage risk-taking behavior, whereas benzodiazepines and marijuana can reduce the capability to attend to and/or react to sudden, unexpected emergencies.

Compared to alcohol-impaired driving, understanding and deterring drug-impaired driving is much more complex for a variety of reasons: (1) There is an inordinately large number of substances (not all of which are illegal) with the potential to impair driving and increase crash risk; (2) drug presence does not necessarily imply impairment, and when they do impair, the various drugs impair driving differently; (3) most psychoactive drugs are chemically complex molecules, whose absorption, action, and elimination from the body are difficult to predict; (4) considerable differences exist between individuals with regard to the rates with which these processes occur; (5) the ability to predict an individual’s performance at a specific dosage of drugs other than alcohol is limited; and (6) for many drug types, drug presence can be detected long after any impairment that might affect driving has passed. Alcohol, in comparison, is a simple molecule that is readily and fairly rapidly absorbed, distributed, and metabolized in a predictable manner, making it much easier to understand (NHTSA, 2009a; WHO, 2004).

2.3. Risk Factors Related to Alcohol-Impaired Driving Some demographic descriptors are associated with increased risk for impaired driving. Males have historically been more likely to drink alcohol and approximately twice as likely to drive after drinking compared to females (Drew et al., 2010; NHTSA, 2009b; NIAAA, 2001b; Stout, Sloan, Liang, & Davies, 2000). Although males still comprise the majority of impaired drivers (McKay, Thoma, Kahn, & Gotschall, 2010; NHTSA, 2003a; Schermer, 2006; Tsai, Anderson, & Vaca, 2010), research indicates that the number of alcohol-impaired female drivers has increased at an alarming rate. Since 1998, there has been a 28.8% increase in the number of females arrested for DUI (NHTSA, 2009c), whereas DUI arrests among males decreased 6.6% in the same period (McKay et al., 2010). Females accounted for 15% of drunken drivers in fatal crashes in 2007, up from 13.5% in 1998 (McKay et al., 2010). In addition, in the 10 states in which the number of alcohol-impaired female drivers increased from 2007 to 2008, the percentage increase was greater than the corresponding percentage increase among men (23 vs. 9%) (NHTSA, 2009c).

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Alcohol-Impaired Driving

Other demographic characteristics associated with increased risk of driving under the influence include (1) being younger than 35 years old (Maskalyk, 2003; NHTSA, 2003a; Tsai et al., 2010), (2) being unmarried (Stout et al., 2000), (3) having a high school education or more (Stout et al., 2000), (4) being employed (Stout et al., 2000), (5) being born in a single-parent family (Sauvola, Miettunen, Ja¨rvelin, & Rasanen, 2001), (6) living in a rural setting (Augustyn & Simons-Morton, 1995; Cox & Fisher, 2009), (7) being a smoker (Stout et al., 2000), (8) having a prior conviction of DUI (Maxwell, Freeman, & Davey, 2007; Schermer, 2006), (9) having a family history of alcohol abuse (Turrisi & Wiersma, 1999), (10) being a heavy drinker or alcohol dependent (Baker, Braver, Chen, Li, & Williams, 2002), (11) being a binge drinker (ValenciaMartin, Galan, & Rodriguez-Artalejo, 2008), and (12) a history of being an intoxicated passenger in a car with an intoxicated driver (Schermer, Apodaca, Albrecht, Lu, & Demarest, 2001). Previous studies indicate that whites are at increased risk of driving under the influence (Augustyn & SimonsMorton, 1995; Stinson et al., 1998), and more recent research supports this finding. For instance, Drew and colleagues (2010) collected data on driving after drinking and found that the prevalence of impaired driving in the past 30 days was 23% for whites, 15% for American Indians/Alaskan Natives, 14% for Asians, 13% for Hispanics, and 10% for blacks. Heavy drinking is particularly associated with increased risk for DUI. Heavy episodic drinking, or binge drinking, is typically defined as a pattern of alcohol consumption that brings BAC level to 0.08% or higher. This pattern of drinking usually corresponds to five or more drinks on a single occasion for men or four or more drinks on a single occasion for women, generally within approximately 2 h (NIAAA, 2004). Binge drinkers are more than 30 times as likely to DUI (Liu et al., 1997), and more than 70% of drivers convicted of DUI are heavy drinkers or are alcohol dependent (CDC, 2011). Alcohol outlets have also been found to be associated with overall alcohol consumption, automobile crashes, and fatalities among adults (Gruenewald, Johnson, & Treno, 2002). Research has shown that adding a single alcohol outlet in a community results in 2.7% more drunken driving traffic injuries. However, neighborhoods systematically differ in densities of alcohol outlets, with higher alcohol concentrations being found in low-income, ethnic minority neighborhoods (Duncan, Duncan, & Strycker, 2002). It is important to note that roadside studies indicate that people younger than age 25 years are less likely to drive after drinking (Drew et al., 2010), but their crash rates at low and moderate BACs are substantially higher than crash rates among older adults (NIAAA, 2001a). This is particularly disturbing given that young men aged 18e20 years

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report DUI almost as frequently as men aged 21e34 years (CDC, 1999). Furthermore, from 1995 to 2007, the proportion of female drivers aged 19e24 years involved in alcohol-related fatal crashes increased much more than the proportion of males in the same age group (3.1 vs. 1.2%) (Tsai et al., 2010). Young drivers have the highest rate of involvement in fatal crashes of any age group (NHTSA, 2003b), and traffic crashes are the leading cause of death for this group (CDC, 2010). Not surprisingly, DUI often coincides with other highrisk behaviors. Specifically, safety belts were used by only 34% of fatally injured intoxicated drivers in 2007, which is a 6% decline from the previous year (NHTSA, 2008b). Also, the intoxication rates for drivers in crashes in 2009 were highest for motorcycle drivers (28%) (NHTSA, 2009b). These high-risk behaviors, including DUI, are most common among drivers with low perceptions of risk. Research shows an association between risk perception and risk-taking behavior, and that people tend to overestimate low probabilities and to underestimate high probabilities (Dionne, Fluet, & Desjardins, 2007). Dionne and colleagues found that those who underestimate crash risks or the probability of arrest for DUI are less cautious, are more likely to be arrested for a violation, and have more crashes. In fact, the odds of drinking and driving are more than four times greater among people with weak compared to strong beliefs in the dangers of the behavior (Steptoe et al., 2004). This perception is also evidenced by a greater percentage of individuals being “almost certain” that an accident would occur than being “almost certain” an impaired driver would be stopped by police (Drew et al., 2010). To make significant changes in driver behavior and effectively deter drunk driving, it seems necessary to increase drivers’ perceptions of the risk of detection (NHTSA, 2007b; WHO, 2004).

2.4. Self-Reported Alcohol-Impaired Driving Despite national DUI deterrence initiatives within the United States, self-reported impaired driving remains a problem. In a 2008 status report (Drew et al., 2010) on current attitudes, knowledge, and behaviors regarding drinking and driving among the general public, 30% of persons reported driving a motor vehicle at least once when they thought that they were over the legal limit for alcohol consumption in the past year. Furthermore, 13% reported driving within 2 h of drinking alcohol in the past 30 days, with an average number of occurrences of 2.8. Males were significantly more likely to report these behaviors than were females. There is an underestimation by the general driving public in the perceived amount of alcohol it takes to reach the legal limit of 0.08. Forty-eight percent of drivers who consumed alcohol reported that it would be safe for them to have three or more drinks within a 2 h period before driving,

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and a portion of that percentage reported that it would be safe to have five or more drinks. Only 26% placed their personal limit for safe operation of a motor vehicle at one or fewer drinks (Drew et al., 2010). Quinlan and colleagues (2005) found more conservative results. In an analysis of the Behavioral Risk Factor Surveillance System (BRFSS) national telephone survey data from 2002, 2.3% of adults reported alcohol-impaired driving during the month prior to the interview (compared to 13% reported in Drew and colleagues’ 2008 status report). This conservative percentage still equates to an estimated 159 million episodes of DUI. This estimate includes 128 million men and 31 million women. It is noteworthy that this estimate is 106 times higher than the 1.5 million arrests for DUI in the United States each year (Quinlan et al., 2005). Self-reported binge drinking episodes increased from 1.2 billion to 1.5 billion between 1993 and 2001 (Naimi et al., 2003) and continue to increase. Binge drinking per person per year has increased by 17%, which is disturbing because binge drinkers are 14 times more likely to get a DUI than are non-binge drinkers. The greatest increase for binge drinking is among those aged 18e25 years. In another analysis of BRFSS data from 1997e1999, Nelson, Naimi, Brewer, Bolen, and Wells (2004) found that binge drinking varies greatly by area of the country, and rates are as high as one-third of people aged 18e34 years. Males, who are overrepresented in alcohol-involved crashes and fatalities, were actually more likely than females to report having deliberately avoided driving when they thought they had too much to drink (50 vs. 38%). Of those who avoided driving after drinking, 28% did so by riding with a designated driver, 26% rode with another driver at the drinking location, and 11% stayed the night to avoid driving (Drew et al., 2010).

2.5. Public Perceptions of DUI and Associated Risk Perception of risk associated with alcohol-impaired driving in the United States has increased over time. Berger and Marelich (1997) compared telephone interviews of California drivers in 1983e1986 (N ¼ 291) to phone interviews of California drivers in 1994 (N ¼ 608). Self-reported drunk driving showed a substantial decline, and respondents expressed an increased expectation that violators of DUI laws would be punished. Participants in 1994 also had greater knowledge of drinking and driving laws than did those in the early 1980s. In addition, perceptions among respondents that drinking and driving is morally wrong and that friends and relatives would disapprove of one’s driving after drinking were significantly increased in 1994 relative to 1983e1986 (Berger & Marelich, 1997).

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This trend continues and was demonstrated by Drew and colleagues (2010), who showed that 81% of the driving-age public perceived drinking and driving as a major threat to the personal safety of themselves and their families. In addition, 66% believed that penalties for violators of drinking and driving laws should be more severe, whereas 28% believed they should remain the same. Seventy-five percent of driving-age persons endorsed weekly or monthly sobriety checkpoints. Interestingly, approximately one in three persons (29%) were not aware of the minimum legal drinking age in the United States. Of the individuals who were aware of the minimum legal drinking age, 86% correctly identified it as 21 years old.

2.6. Trends in Alcohol-Involved Crashes In many high-income countries, approximately 20% of fatally injured drivers have excess alcohol in their blood. In low-income countries, on the other hand, excess alcohol is present in 33e69% of fatally injured drivers (WHO, 2004). This is likely due to the fact that less than half of countries worldwide have drunk driving laws based on BAC limits (WHO, 2009). During the past 20 years in the United States, there have been 858,741 traffic deaths, 366,606 of which (42.7%) were attributed to alcohol use (Cummings, Rivara, Olson, & Smith, 2006). Also during the past 20 years, alcohol-related crash death rates have declined markedly. Namely, there has been a 53% decline in mortality rates attributed to alcohol use, representing 153,168 lives saved by decreased drinking and driving (Cummings et al., 2006). Furthermore, a 2007 National Roadside Survey found a dramatic decline in the number of drinking drivers with BACs at or above 0.08 on weekend nights (2.2%) compared to previous surveys, which represents a decline of 71% in the percentage of alcoholimpaired drivers on the road on weekend nights since 1973 (NHTSA, 2009b). Figure 17.1 depicts the U.S. rate for total alcohol-related fatalities (and total fatalities) per 100 million vehicle miles traveled (VMT) for every year from 1977 to 2004 (NIAAA, 2006b). As evidenced by Figure 17.1, the percentage of fatal crashes involving alcohol remained relatively constant until 1986 and has steadily decreased during the following two decades. In 2004, there were 16,694 alcohol-related traffic fatalities, accounting for 39% of all traffic fatalities (NHTSA, 2005a). By 2007, the number of alcohol-related traffic fatalities had decreased to 12,998, which accounted for 31.7% of all traffic fatalities (Tsai et al., 2010). The number of alcohol-related traffic fatalities decreased to 11,773 by 2008 (NHTSA, 2008d), representing a 9% decrease from the previous year (McKay et al., 2010; NHTSA, 2009c). Government agencies such as NHTSA attribute these downward trends to national large-scale

Chapter | 17

Alcohol-Impaired Driving

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FIGURE 17.1 Total and alcohol-related traffic fatality rates in the United States per 100 million vehicle miles traveled (VMT), 1977e2004. Source: National Institute on Alcohol Abuse and Alcoholism (2006b).

prevention strategies implemented during the past two decades.

3. LARGE-SCALE PREVENTION OF ALCOHOL-IMPAIRED DRIVING Large-scale prevention approaches, as opposed to individual behavior therapy or one-time interventions (for an excellent review of such interventions, see NHTSA, 2007c), are often more feasible and economical when addressing problems as prevalent as alcohol-impaired driving. Therefore, it seems only natural that a number of large-scale approaches for impaired driving prevention have been used in the United States during the past two decades.

3.1. Overview of Large-Scale Prevention Approaches In response to very high alcohol-related fatality rates (averaging 26,000 annually) in the 1980s, states strengthened their impaired driving laws, there was a significant increase in enforcement activities focused on impaired driving, and considerable media attention was paid to the problem. Targeted DUI interventions arose, beginning with the grassroots community advocacy efforts of organizations such as Mothers Against Drunk Drivers (MADD), Students Against Destructive Decisions (SADD), and Remove Intoxicated Drivers (Maskalyk, 2003). These efforts were soon followed by specific deterrence policy efforts aimed at punishing offenders and reducing recidivism. When it became evident that specific deterrence efforts were not enough to control alcohol-impaired driving, general deterrence strategies were developed to prevent first occurrences of DUI (DeJong & Hingson, 1998). A broad spectrum of prevention approaches have been suggested and implemented for decreasing alcohol-impaired

driving. Efforts specifically focused on individual behavior change include mass media campaigns, programs to change DUI-related norms, placing warning labels on alcoholic beverages, enforcement efforts and sanctions, and treatment for alcoholic problems. Approaches aimed at decreasing alcohol access include raising the cost of alcohol through taxes, increasing the minimum age to purchase alcohol, and decreasing the geographic density of bars and liquor stores (Holder, 2000; Schermer, 2006). Efforts to change the drinking context include training for persons who serve alcohol and safe ride and designated driver programs (Voas & Fell, 2010). Here, a number of these approaches are reviewed in more detail.

3.2. Large-Scale Implementation Versus Large-Scale Systems Interventions This chapter reviews the most popular large-scale approaches to the prevention of alcohol-impaired driving. It is important to note that although the strategies reviewed here are implemented on a large scale, not all are truly systems interventions because many of them target individuals. A true large-scale community systems intervention that combines multiple strategies (including some targeting individuals) with the intent to change the system may be the best approach to alcohol control and impaired-driving prevention (DeJong & Hingson, 1998; Holder, 2000). Multicomponent systems interventions are reviewed later.

3.3. Problems Evaluating the Effectiveness of Large-Scale DUI Interventions Evaluation of large-scale prevention programs warrants mention because separating the effects of independent interventions is laden with difficulties. Some difficulties researchers face include the following: (1) Random assignment of states or communities is politically and

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financially unrealistic; (2) it is common for several laws or programs to start within a relatively short period of time; (3) publicity about DUI is often broadcast nationally or over state lines; (4) there is always the possibility of some other unmeasured variable affecting outcomes; (5) underlying national trends may be affecting outcomes; and (6) researchers must often rely on survey data, proxy data, or broad indicators of change (DeJong & Hingson, 1998). Nevertheless, the conclusions that researchers have been able to draw regarding relative effectiveness are reviewed in this chapter.

4. NON-POLICY PROGRAMS FOR LARGESCALE PREVENTION OF DUI Non-policy large-scale programs include all large-scale programs except national and state-level policy or law changes aimed at the prevention and deterrence of alcoholimpaired driving. Non-policy programs reviewed here include grassroots advocacy organizations, mass media interventions, server intervention and safe ride programs, BAC and normative feedback interventions, and designated driver programs.

4.1. Grassroots Advocacy Organizations Grassroots organizations such as MADD and SADD have played a significant role in galvanizing public opinion about the carnage caused by impaired drivers (NHTSA, 2007b) and are the forerunners of DUI prevention (DeJong & Hingson, 1998). MADD is the grandmother of grassroots DUI programs. In 1980, 13-year-old Cari Lightner was killed by a drunk driver. The subsequent lack of fitting court action inspired Cari’s mother, Candy, to start a group known as Mothers Against Drunk Drivers (Merki & Lingg, 1987). MADD soon became a central force in getting legislators and public officials to take action. MADD also fought to increase public outrage about the issue and change attitudes about drunk driving (Merki & Lingg, 1987). MADD chapters stimulated the passage of more than 2000 new state laws between 1982 and 1994 (Hingson, Heeren, & Winter, 1996). In addition, MADD rates each state’s program participation, and people living in states with a MADD grade of D are 60% more likely to report alcohol-impaired driving than are people from states with a MADD grade of A (Shults, Sleet, Elder, Ryan, & Sehgal, 2002). SADD has been one of the most highly visible and widely promoted anti-DUI programs for youths since the 1980s (Klitzner, Gruenewald, Bamberger, & Rossiter, 1994). The overall mission of SADD chapters is to provide students with the best prevention tools possible to deal with the issues of underage drinking, drug use, impaired driving, and other destructive decisions. SADD has grown to

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become the nation’s dominant peer-to-peer youth education and prevention organization, with thousands of chapters in middle schools, high schools, and college campuses throughout the country (SADD, 2009). Numerous other student programs have emerged in the past decade; however, evaluations of the effectiveness of such student safety clubs are lacking. In an effort to quantify the program’s effectiveness, Preusser Research Group conducted a national SADD evaluation in 1995. Results indicated that students attending schools with an active SADD chapter, compared to students attending schools without one, were more aware of and informed about underage drinking, impaired driving, and drug use and expressed more positive reasons not to use alcohol and/ or drugs. It appears that the results regarding the two most visible advocacy organizations, MADD and SADD, are positive. It is clear that these organizations succeed in increasing awareness of a number of DUI-related initiatives and positively altering the behaviors of individuals with regard to decisions about drinking and driving.

4.2. Mass Media Interventions Mass media campaigns are the most widely recognized community intervention strategy for alcohol-impaired driving (Augustyn & Simons-Morton, 1995). Media interventions are primarily implemented through the medium of television. Alcohol-impaired driving media campaigns include (1) general awareness messages, (2) individual behavior change messages such as the increased use of a designated driver, (3) calls for public action such as the formation of school or community-based programs, and (4) restrictions on alcohol advertising (DeJong & Hingson, 1998). DeJong and Atkin (1995) conducted a content analysis of 137 public service announcements (PSAs) for DUI prevention that aired on U.S. television between 1987 and 1992. Findings indicate that there were more PSAs for the use of designated drivers than for any other subject, and most PSAs focused on promoting general awareness and individual behavior change, not on public policy or law. Celebrity endorsements and emotional appeals were common. In addition, PSAs were intended to reach mass audiences rather than those at greatest risk (DeJong & Atkin, 1995). In general, media campaigns are more effective when delivered through news media with high exposure and credibility than when delivered through PSAs (Holder, 1994). In 1993 and again in 2000, MADD conducted “Rate the States” programs that consisted of MADD rating the states (grades of A through D) regarding their DUI policies and then generating mass news coverage of their report (Russell, Voas, DeJong, & Chaloupka, 1995; Shults et al., 2002).

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Alcohol-Impaired Driving

Based on surveys following the 1993 program, it was estimated that more than 60 million Americans saw or heard a news story related to the program, and it was concluded that MADD’s generation of news coverage for their “Rate the States” report was an effective strategy for prompting state legislatures and governors to enact new policies. An evaluation of the “Innocent Victims” PSAs (a spinoff of the “Friends Don’t Let Friends Drive Drunk” PSA campaign that was introduced in 1984) was conducted in 2003 by NHTSA (2003a). Rather than focusing on the use of designated drivers (as previous PSAs had), the “Innocent Victims” PSAs featured home video donated by the families of victims of impaired driving crashes to convey the dramatic impact on families. Results indicate that 84% of Americans recalled having seen or heard one of the PSAs, and nearly 80% reported having taken action to prevent a friend or loved one from driving impaired. Furthermore, 25% reported they stopped drinking and driving as a result of the campaign. Although the campaign has achieved record levels of recall, its effectiveness has leveled off. In 2005, Tay conducted a meta-analysis of eight mass media campaigns (TV, radio, and print) on drunk driving. He found that the mass media campaigns reviewed in the study reduced alcohol-related crashes in the period during or after the campaign by a median of 13%. In addition, the resultant savings in medical costs, property damage, and productivity caused by mass media campaigns far outweighed the costs. He provided the following recommendations for effective campaigns: (1) Decisions regarding message content should be grounded in theory and empirical evidence, not on opinions of experts or focus groups; (2) fear appeal approaches must be accompanied by specific information about actions people can take to protect themselves; (3) paid advertising campaigns should be utilized rather than PSAs in order to maintain control over placement and maximize exposure; and (4) there is no significant difference in the effectiveness of campaigns that focus on legal deterrence and those that focus on social and health consequences. Conclusions from numerous researchers are that mass media interventions are effective in changing awareness, knowledge, and attitudes and have contributed to the reduction of trauma related to alcohol-impaired driving (Augustyn & Simons-Morton, 1995; Holder, 1994; Tay, 2005). Adding a mass media component to other prevention efforts has a beneficial and reinforcing effect. For example, adding a mass media component to school-based curriculum approaches nearly doubles the effect either has by itself (Augustyn & Simons-Morton, 1995). The Guide to Community Preventative Services (Elder et Al., 2004) showed that mass media campaigns that focused on awareness of enforcement/legal consequences and awareness of social/health consequences were effective in

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reducing alcohol-impaired driving and alcohol-related crashes when paired with a program component (e.g., high enforcement). Similarly, an increase in news coverage is associated with increased public perception of risk for DUI arrest (Holder, 1994). In the Community Trials Project (Holder, 1994, 2000; Holder et al., 1997), reviewed later, news media coverage was a central component for increasing (1) public awareness of alcohol-involved trauma, (2) public support for alcohol prevention policies, (3) perceived risk of detection and arrest for DUI, and (4) community support of law enforcement activities.

4.3. BAC and Normative Feedback Interventions Feedback interventions attempt to change alcohol-impaired driving by providing feedback to individuals regarding their current BAC or by providing feedback regarding norms for alcohol use for a participant’s peer group.

4.3.1. BAC Feedback BAC feedback interventions typically set up stations near drinking establishments or parking lots where participants can anonymously have their BAC assessed by a Breathalyzer and reported to them prior to driving. Variations on BAC feedback interventions have included staffing feedback stations with trained research assistants who conduct sobriety tests such as a ruler-drop task, a verbal task, a body-balance test, and a handwriting analysis (Geller, Clarke, & Kalsher, 1991; Russ & Geller, 1986). Another variation includes the dissemination of “nomograms,” or wallet-sized charts displaying normative values for BACs based on body weight, gender, and number of drinks consumed in 2 h (Geller, 1998). In a review of his work with BAC feedback, Geller (1998) noted that although breath testing was popular and well received, knowledge of BAC did not deter the majority of alcohol-impaired participants from driving. In Russ and Geller’s (1986) study of feedback, drivers with higher BACs were more likely to disregard the feedback and drive anyway. BAC feedback is further complicated by the fact that sometimes the feedback becomes a “game score” as members of groups compete to see who can achieve the highest BAC (Geller, 1998). Use of nomograms is also discouraging because actual BAC varies widely from person to person. Overestimated BAC can lead to disbelief by users, and the risk of underestimation is dangerous (Geller, 1998). Commercially sold personal BAC estimators such as Guardian Angel (GA) have come under fire for underestimating and overestimating BACs. Incorrect underestimation of BAC (by the GA) in a field study

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(N ¼ 129) prompted participants to decrease impairment estimates on a perception scale (Johnson & Voas, 2004). Obviously, incorrect BAC feedback can be quite detrimental to public safety. BAC feedback is best given by a device that is routinely calibrated for accuracy (e.g., a handheld intoximeter similar to those used by police officers). Furthermore, it may only be effective in reducing impairments when it is paired with an intervention such as a low-BAC reward given by Fournier, Ehrhart, Glindemann, and Geller (2004). In this intervention, Fournier et al. (2004) only allowed persons who maintained a low BAC (<0.06) to enter a raffle for a cash prize.

4.3.2. Normative Feedback Subjective norms are central to the theory of reasoned action and are the product of a person’s perceptions of the norms among his or her peers and the person’s willingness to comply with peers (Gastil, 2000). Subjective norms, therefore, constitute a form of informal social influence because a person may adjust his or her alcohol-related behavior in order to be “normal.” Indeed, in a study by Gastil (2000), subjective norms were a more powerful predictor of driving after drinking than were personal attitudes about DUI. In the area of alcohol research, a person’s perceptions of norms are often inaccurate. Social norms feedback approaches are derived from the research on motivational interviewing (Miller, Rollnick, & Conforti, 2002) and social cognitive theory (Bandura, 1986, 1997), and they were originally used as one component of brief interventions targeting high-risk and problem drinkers (Baer et al., 1992; Marlatt et al., 1998; Murphy et al., 2001). Feedback interventions capitalize on the consistent finding that individuals overestimate how much others drink and believe that attitudes about drinking are much more permissive than reality. These inflated beliefs, in turn, are some of the strongest correlates of drinking and related risk taking (NIAAA, 2007). By presenting individuals with actual rates and attitudes, individuals’ misjudgments are corrected and it is assumed that their own levels of alcohol use and risk taking decrease to match the true norm (NIAAA, 2007). Social norms marketing is widely used by universities where mass communications (e.g., flyers and posters) inform the student population at large regarding true rates of student alcohol use and DUI on campus (NIAAA, 2007). Despite mixed reviews of early studies (Wechsler et al., 2003), later examinations have noted important limitations of the early work and have found this approach to be effective for reductions in relative risk of alcohol consumption and related consequences, especially when combined with other interventions and delivered via a highly visible and massive initiative (DeJong et al., 2006; NIAAA, 2007; Turner, Perkins, & Bauerle, 2008).

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4.4. Server Intervention and Safe Ride Programs Nearly every state and Washington, DC prohibit alcohol sales to obviously or visibly intoxicated people, but these laws are largely ignored by alcohol establishments and their staff (NHTSA, 2008a). In fact, approximately 50% of drivers arrested for driving under the influence of alcohol report having their last drink at a licensed establishment (NHTSA, 2008a). Server intervention and safe ride programs are alcohol-impaired driving initiatives instituted at bars and restaurants that serve alcohol. Server interventions typically entail training bartenders and wait staff in methods used to decrease intoxication levels among patrons and prohibit/decrease drinking by underage individuals (Russ & Geller, 1987). Methods for server intervention typically include checking identification, offering food or water, delaying service, commenting on the quantity of alcohol being consumed, making driving-related comments or suggestions, calling a taxi, and refusing service (Russ & Geller, 1987). Safe ride programs provide alternative means of transportation, such as taxis, vans, and limousines, and are typically arranged by servers to provide vouchers for discounted rides to inebriated persons (Rivara et al., 2007; Simons-Morton & Cummings, 1997). An evaluation of a safe ride program in Maryland found that 42 barroom respondents who reported using safe rides in the past year were more likely to (1) be heavy drinkers, (2) have driven when feeling intoxicated, (3) have ridden with an intoxicated driver, and (4) have been arrested for DUI (Caudill, Harding, & Moore, 2000). Another voucher program implemented in six establishments in Texas found that an average of 0.7 cab vouchers were used per month per establishment despite heavy marketing of the program within each establishment (Simons-Morton & Cummings, 1997). Therefore, it appears that safe ride programs appeal to those most at risk for DUI, but they are vastly underutilized. This may be due to the necessity to leave one’s vehicle at a public location overnight, which dissuades many individuals from utilizing taxis. In response, companies such as MetroScoot (operated out of Virginia Beach, VA) have emerged. Inebriated individuals call the service for pickup and then a driver on a scooter comes to the location, folds up the scooter and places it in the individual’s car trunk, and drives the individual home in his or her own vehicle (MetroScoot, 2010). This novel service is a viable alternative to traditional safe ride programs, but no evaluations of its use have been conducted. Russ and Geller (1987) evaluated the most prominent server program, Training for Intervention Procedures by Servers of Alcohol (TIPS), which teaches servers to look for signs of intoxication in order to prevent drunk driving and underage drinking. Russ and Geller sent pseudopatrons into two bars where approximately half of the servers had

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received TIPS training. Pseudopatrons, who were blind to condition, set the occasion for intervention by consuming six alcoholic beverages in 2 h. Sober companions who “nursed” an alcoholic drink recorded the evening’s events with a hidden tape recorder. Results indicated that trained servers initiated significantly more server interventions than did untrained servers, and pseudopatrons reached substantially lower BACs when served by trained servers than when served by untrained servers. Specifically, no pseudopatron served by trained servers exceeded the legal limit when exiting the bar (mean BACs were 0.06 vs. 0.10 for pseudopatrons served by trained servers and untrained servers, respectively). Project Alcohol Risk Management (ARM) is another server intervention program that has been evaluated favorably. This program is a one-on-one consultation program for owners and managers, which provides information on risk level, policies to prevent illegal sales, legal issues, and staff communication. Among ARM-trained bar personnel, the sale of alcohol to underage patrons and intoxicated pseudopatrons decreased by 11.5 and 46%, respectively, in comparison to that of matched nontrained bars. However, due to low numbers of participants (and, thus, low power of the test), decreases were not statistically significant and further evaluation is needed (Toomey et al., 2001). Finally, the Driving Under the Influence of Alcohol Reduction Program, launched by the Washington State Liquor Control Board (WSLCB), had mixed results. The demonstration project (consisting of 20 sites) was designed to assess the effects of the intervention on three outcome measures: (1) retailer willingness to sell alcohol to apparently intoxicated people; (2) BAC levels of drivers arrested for DUI; and (3) DUI arrestees naming establishments exposed to the program as their place of last drink. The intervention consisted of (1) letters to establishment owners notifying them that the agency had concerns about reported business practices; (2) provision of a DUI education packet to licensees; (3) an offer of free training on how to check identification and avoid overservice of alcohol; (4) unannounced premise checks by self-identified WSLCB agents, with punitive actions taken if necessary; and (5) additional premise checks and undercover operations if no progress was noted through monthly progress evaluations. Program evaluation indicated that there were no changes in retail practices; however, there were reductions in the average number of monthly DUI arrests involving drivers who had been drinking at intervention sites and reductions in average BACs among DUI arrestees (NHTSA, 2008a). On the whole, server intervention appears viable. However, its success relies on proper program implementation as well as servers’ continued use of the intervention techniques. Continued use is not always feasible

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when drinking establishments are crowded or when management does not seem supportive of the program.

4.5. Designated Driver Programs Driving alone, drinking at bars, and lack of advanced planning are all associated with drinking and driving (Morrison, Begg, & Langley, 2002). The designated driver (DD) strategy is now a well-established, widely accepted, and easily implemented strategy for decreasing alcoholimpaired driving (Barr & MacKinnon, 1997; DeJong & Winsten, 1999). The DD concept entails a group of friends selecting one person among them to abstain from drinking and to be responsible for driving the group home (DeJong & Wallack, 1992; Rivara et el., 2007). Critics of DD programs argue that these programs may encourage excessive drinking by passengers, may deflect attention from other alcohol-related problems, and may decrease needed attention on changes in public policy (Rivara et al., 2007). DeJong and Winsten (1999) analyzed self-report data from the Harvard School of Public Health College Alcohol Study. The sample included 17,592 individuals from 140 colleges in 40 states. Results indicated that 33% had been a DD in the past 30 days, and 32% had ridden with a DD in the past 30 days. Fifty-three percent of drinkers said that they did not drink at all the last time they were a DD, whereas 26% reported they had one drink, 19% had more than one drink but did not binge drink, and 2% binge drank. Forty percent of drinkers who had served as a DD in the past 30 days said they usually binge drink but did not when they were a DD. Among drinkers, 67% said they binge drank the last time they rode with a DD. Furthermore, 22% of drinkers who rode with a DD said they usually did not binge drink but did so the last time they rode with a DD (DeJong & Winsten, 1999). A study examining BAC levels of 457 DDs, non-DD drivers, and their passengers as they were leaving drinking establishments in a college town allowed for comparisons of actual BACs for these groups (Timmerman, Geller, Glindemann, & Fournier, 2003). Results indicated that BACs were higher for men (vs. women) and for non-DD drivers (vs. DDs). Female DDs had significantly lower BACs (M ¼ 0.02) than did male DDs (M ¼ 0.07). Mean BAC levels for non-DD drivers were 0.08 for men and 0.07 for women. Mean BACs of passengers were 0.08, and no difference was found between the passengers of DDs and those riding with non-DDs. In summary, the average male DD and the average non-DD driver (male or female) were substantially impaired (Timmerman et al., 2003). Rivara and colleagues (2007) conducted 917 telephone surveys with individuals aged 21e34 years living in Washington. They found that most participants drank alcohol weekly, and more than 40% had binge drank at least

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once in the past year. Twenty percent of participants reported driving after drinking too much in the last month. Two-thirds of participants had been a DD in the past year, and in most cases (84%) this was decided before the group went out drinking. Thirty-nine percent of DDs consumed alcohol, but in only 3% of cases was it more than two drinks. Passengers in cars driven by a DD drank more alcohol than they usually do, with approximately 50% reporting that they consumed at least three more alcoholic beverages than they typically consumed. It is apparent from these reviews that DD programs have advantages and disadvantages. DD use is common; however, DD use is also inconsistent, with many DD users also reporting recently having driven while impaired or ridden with an impaired driver. Many DDs do not abstain from alcohol use when serving as a DD, and some studies indicate that DD use does encourage excessive alcohol consumption among passengers. Clearly, more consistent and effective methods for decreasing alcohol-impaired driving are warranted. On balance, however, DD use is prominent enough that it probably does decrease the overall number of impaired drivers on the road.

5. POLICY AND LEGAL INITIATIVES FOR LARGE-SCALE PREVENTION OF DUI Driving under the influence is a legal term in all 50 states, and the BAC per se limit for drivers age 21 years or older is 0.08 g/dl (NHTSA, 2009c). During any given 1-year period, self-reported survey data indicate that 17e27% of people in the United States drive soon after drinking, which translates to 28e45 million people (Balmforth, 1999). Theoretically, any of them may be arrested, but most impaired drivers are not arrested (Zador, Krawchuck, & Moore, 2000). In fact, the arrest rate for DUI is 1 in every 139 licensed drivers in the United States (NHTSA, 2008c), or 1.4 million drivers annually (Voas & Fell, 2010). All states and Washington, DC have 21-year-old minimum drinking age laws (NHTSA, 2000). (NHTSA 2005b) estimates that raising the legal drinking age to 21 years has saved 700e1000 lives annually and prevented more than 22,000 alcohol-related traffic fatalities from 1976 to 2003. Basic impaired-driving laws (e.g., license revocation, lowering BAC limits to 0.08 for impaired drivers aged 21 years or older, and zero tolerance for underage impaired drivers) were adopted during the last quarter of the twentieth century and produced an estimated 10e20% reduction in alcohol-related fatalities (Dang, 2008; NCHS, 1999). This section reviews specific deterrence, general deterrence, and alcohol control strategies for decreasing alcohol-impaired driving. However, because a thorough review of such strategies has been published (Voas & Fell,

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2010), the following review merely summarizes overall findings in each category. The reader is referred to Voas and Fell (2010) for a more thorough review of policy and legal strategies.

5.1. Specific Deterrence Strategies Specific deterrence laws were created to target repeat DUI offenders in an effort to reduce alcohol-impaired crashes. These drivers are more likely to be involved than other drivers in alcohol-related crashes, constitute one-third of the drivers arrested for DUI, and constitute approximately 16% of the drivers who have positive BAC levels and are killed in traffic crashes each year (National Association of State Alcohol and Drug Abuse Directors (NASADAD), 2006). Suspending a driver’s license is effective for light and moderate drinkers, and these individuals are less likely to become repeat DUI offenders (Greenberg, Morral, & Jain, 2004). However, this penalty is not as effective for heavy drinkers, and 75% of DUI offenders will continue to drive (Mann et. al, 2003; Voas & DeYoung, 2002). Heavy drinkers who repeatedly drive impaired are often alcohol dependent (Voas & Fell, 2010). Individuals with an alcohol dependency problem are disproportionately involved in alcohol-related crashes and account for twothirds of these types of crashes (Hingson & Winter, 2003). Furthermore, drivers with BAC levels at or above 0.08 who are involved in fatal crashes are nine times as likely as drivers with no alcohol to have a prior conviction for driving while intoxicated (DWI) (NASADAD, 2006). Therefore, specific deterrence laws also include lower limits for convicted DWI offenders (from 0.08 to 0.05) as well as other methods, including impounding vehicles or license plates, DWI courts, and ignition interlock programs (NASADAD, 2006). Vehicle action such as vehicle impoundment for 1e6 months has shown promising results. License plate marking for repeat offenders is also a more effective penalty than license suspension (NASADAD, 2006; Voas & DeYoung, 2002). Unfortunately, many localities with the legal ability to apply vehicle action rarely utilize the penalty in sentencing. In DWI courts, offenders can volunteer for an intensive supervision program in which their drinking is monitored to ensure abstinence and their attendance and progress at treatment programs is closely followed by the court with monthly appearances before the judge who can either reduce or lengthen their jail sentence based on their performance in the treatment program (Marlowe et al., 2009). Vehicle interlock programs install a device on the offender’s vehicle that requires a low alcohol breath sample before the car will start. The long-term effectiveness of interlocks is unclear. Willis, Lybrand, and Bellamy (2004)

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found that interlocks reduce repeat drunk driving offenses by an average of 64%, whereas Raub, Lucke, and Wark (2003) found that one-fifth as many drivers who had the alcohol ignition interlock device were likely to DUI again compared to a comparison group during the first year, and results diminished once the interlock was removed from the treatment group’s cars. Few DUI offenders choose the interlock voluntarily, and most are sentenced to have it installed in their cars (Raub et al., 2003). It is estimated that only one out of eight convicted drunk drivers each year currently get the device, and the majority are repeat offenders (MADD, 2006). Bjerre (2003) conducted a 2-year volunteer program for the interlock device; however, only 12% of the eligible DWI offenders took part in the program, and 60% of the interlock users were diagnosed as alcohol dependent or alcohol abusers. Findings showed that while the interlock was installed, there was a noticeable reduction in alcohol consumption and in the annual accident rate while in the program. Voas, Blackman, Tippetts, and Marques (2001) increased the participation rate of the interlock device from 10 to 62% by giving DUI offenders different alternatives: They could participate in the interlock program, go to jail, or be electronically monitored under house arrest. This policy produced substantial reductions in DUI recidivism compared to that of six other counties. Overall, research shows that these devices are effective while installed, but recidivism rates increase once the interlock system is removed. In addition to the interlock device, there are several other emerging technologies to stop drunk driving. These include (1) tissue spectroscopy, which uses infrared light to measure alcohol levels in the tissue just beneath the skindthis is quicker and easier than breath testing; (2) transdermal technology, in which alcohol is detected in perspiration and used to estimate BAC using a device that is worn on the wrist or ankledthis is not as accurate as other technologies; and (3) ocular measurement, in which invehicle cameras are used to record and analyze a driver’s eye movements, including their percentage of eye closure, tunnel vision, and frequent or extended glances away from the roaddthis is intended to be used in combination with other technologies (MADD, 2007). To be effective and accepted by the public, implementation of advanced technologies must not affect or interfere with the sober driver, must be absolutely reliable and accurate, must be set at 0.08 BAC for adult drivers not convicted of drunk driving, and must be cost-effective (MADD, 2007). McInturff (2006) found that Americans support advances in smart vehicle technology to prevent drivers from driving drunk by a 4 to 1 margin. Moreover, 57% of Americans reported that they would spend $100 to have technologies that would prevent a car from operating if the driver has a BAC higher than the legal alcohol limit,

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and 69% would support the installation of these technologies if their insurance premium was reduced.

5.2. General Deterrence Strategies General deterrence strategies are not targeted to DUI offenders but, rather, are aimed at dissuading the general public from driving after drinking. General deterrence increases the public’s perception that people who violate the law will be ticketed, arrested, convicted, and punished, and it thereby persuades them to adhere to the law (NHTSA, 2007c). Research shows that first-time offenders have typically driven 87 times before they are arrested (MADD, 2008), so general deterrence is vital. Types of deterrence strategies include (1) administrative license revocation; (2) quick and specific punishment, such as mandatory jail terms and fines; (3) sobriety checkpoints and saturation patrols; (4) per se legal BAC limits of 0.08% for individuals 21 years of age or older; and (5) zero tolerance limits of 0.02% for individuals younger than age 21 years (NASADAD, 2006; Stout et al., 2000). Setting lower legal BAC limits, in particular, has a great deal of research supporting its effectiveness. For example, Japan instituted a legal BAC limit of 0.05 mg/dl in 1970, and official statistics of alcohol-involved crash fatality data gathered from 1960 to 1995 yield conclusive evidence that the 1970s legislation had a measurable and long-term effect on alcohol-related motor vehicle fatalities (Deshapriya & Iwase, 1998). In addition, Tippetts, Voas, Fell, and Nichols (2005) studied the effectiveness of adopting a 0.08% BAC law in 19 jurisdictions in the United States and found that the number of drinking drivers involved in fatal crashes declined in 16 of the jurisdictions. In the United States, lower per se limits of 0.08 were associated with a 16% reduction in the proportion of fatal crashes involving drivers with a BAC of 0.08 or higher (DeJong & Hingson, 1998). In 2004, all states implemented the 0.08 per se legal BAC limit. Establishing zero tolerance laws has also resulted in substantial reductions in alcohol-involved fatal crashes. Zero tolerance laws (e.g., 0.02% BAC limits for people younger than age 21 years) in the United States are associated with a 20% reduction in single-vehicle nighttime crashes among drivers aged 15e20 years (DeJong & Hingson, 1998). It is important to note that legal strategies need more than just passage to have an effect. States that publicly enforced zero tolerance laws found that underage drivers were more likely to be aware of these laws; however, if states did not enforce the law, then drivers were likely to be unaware of underage drinking laws and more likely to engage in this behavior (Ferguson & Williams, 2002). Research also supports other general deterrence strategies. Sobriety checkpoints and saturation patrols, in

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particular, act as effective deterrents to alcohol-impaired driving (NASADAD, 2006). Forty states and Washington, DC allow sobriety checkpoints, and all states allow saturation patrols (MADD, 2008). There are two types of sobriety checkpoints: random breath test (RBT) and selective breath test (SBT). In RBT, all drivers stopped are given BAC breath tests; in SBT, law enforcement officers must have reason to demand administering the breath test at the checkpoint. Due to constitutional rights violation issues, RBT is not permitted in the United States (NASADAD, 2006). Eighty-seven percent of Americans surveyed support sobriety checkpoints to find and deter drunk drivers, and 80% of Americans said that they would be discouraged from drinking and driving by sobriety checkpoints. Wellconducted sobriety checkpoints generally delay drivers for no more than 30 s and cause no traffic problems (MADD, 2008). A literature review on the effectiveness of sobriety checkpoints nationally conducted by Fell, Lacey, and Voas (2004) found that such programs have yielded an overall decline in alcohol-related crash fatalities of 18e24%. Recognizing the benefits of seat belts to vehicle occupants in alcohol-related crashes, NHTSA added the enactment of primary seat belt laws as a supplemental strategy. Although primary seat belt laws cannot be expected to change drinking and driving behavior, they can substantially reduce death and injuries in alcohol-related crashes (NHTSA, 2007c). When used, seat belts reduce the risk of fatal injury to front-seat passenger occupants by 45% (NHTSA, 2007c). In 2004, seat belts were used by only 28% of fatally injured drivers with a BAC of at least 0.08, compared to 57% of fatally injured drivers with no alcohol (NHTSA, 2007c). If every state with a secondary seat belt law upgraded to primary enforcement, approximately 1000 lives and $4 billion in crash costs could be saved annually (NHTSA, 2007c).

5.3. Alcohol Control Policies Alcohol control policies aim to prevent alcohol-related risk by controlling the availability of alcohol. Such policies are based on the idea that limiting the availability of alcohol will reduce drinking, which in turn should reduce alcoholrelated problems (Voas & Fell, 2010). Alcohol control policies include (1) minimum legal drinking age laws, (2) zoning changes for alcohol outlet density, (3) increased alcohol taxes, and (4) responsible beverage service mandates and laws. Outside of basic impaired-driving BAC limit legislation, minimum legal drinking age (MLDA) laws were the most effective alcohol safety program of the last quarter of the twentieth century (Voas & Fell, 2010). Between 1988 and 1995 (after adoption of MLDA laws), alcohol-related

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traffic fatalities for people aged 15e20 years declined 47% (NHTSA 2007d). Zoning changes in outlet density allow policy makers to restrict the number of outlets in a certain proximity that sell alcohol. Increased density of bars and liquor stores, as well as the presence of grocery stores that sell beer and wine, is associated with an increased probability of alcohol-impaired driving and increased sales to individuals younger than 21 years (Freisthler, Gruenewald, Treno, & Lee, 2003). The idea behind increased alcohol taxes is that an increase in alcohol prices will reduce alcohol consumption and subsequent drinking problems (NASADAD, 2006), and lower alcohol prices have been linked to heavy drinking (Wagenaar, Salois, & Komro, 2009). In a review of a variety of studies over a 20-year period, Chaloupka (2004) found that an increase in prices of and taxes on alcoholic beverages resulted in reductions in alcohol consumption, the likelihood of alcohol-impaired driving, and resultant fatal and nonfatal crashes. However, Young and Biellnska-Kwaplsz (2002) question whether the increased price is truly a deterrent because the increase in taxes translates to only a very small increase in beverage price. Historically, tax hikes have not been widely used as a public health measure to influence drinking in the United States (Voas & Fell, 2010). Responsible beverage service (RBS) includes mandated server training (reviewed previously) as well as avoiding service procedures and drink promotions that encourage intoxication (e.g., serving beer in pitchers and having “happy hours”) (Voas & Fell, 2010). The aim of RBS is to decrease sales to minors, decrease the number of intoxicated patrons, and decrease DUI. Although RBS initiatives are associated with a 23% reduction in single-vehicle nighttime fatal crashes (DeJong & Hingson, 1998), RBS legislation is weak across all states overall (Mosher et al., 2002). In addition to server training, responsible beverage service initiatives include the passage of tort liability laws. Tort liability laws put business owners at risk if an overserved customer injures a third party (Voas & Fell, 2010) and encourage servers to more thoroughly monitor their guests’ drinking behavior. Tort liability laws are bolstered by laws that prohibit sales to obviously intoxicated people, which are present in 47 states and Washington, DC (NHTSA, 2007a). Dram shop laws, a special form of tort liability, allow a person injured by an alcohol-impaired individual to sue the establishment that served the impaired individual alcohol (Stout et al., 2000). Social host responsibility laws apply to people serving alcohol in their homes and at parties, and they allow a person injured by an alcohol-impaired individual to bring a civil suit against the person who served the alcohol. In general, alcohol control policies are important in order to help decrease DUI rates among alcohol-impaired drivers.

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5.4. Local Policies Although national and state representatives set policies such as minimum drinking age laws, regulation of alcohol outlets, and legal blood alcohol levels for DUI, it is often the responsibility of the community to ensure the successful implementation of these policies in the community. For instance, communities can set local policies such as making enforcement of DUI a police priority and allocating enforcement resources to prevent alcohol sales to minors (Holder, 2000). Examples of two successful implementations at the community level are reviewed in the following section.

6. MULTICOMPONENT COMMUNITY SYSTEMS APPROACHES TO THE PREVENTION OF DUI The majority of alcohol-impaired driving interventions to date have consisted of community prevention strategies such as school education programs or media campaigns that despite being implemented on a large-scale, have targeted individual problem drinkers (Holder, 2000). There is little evidence that individually focused programs will work as long as existing economic, social, and cultural structures remain unchanged, thereby allowing for new cases of alcohol-impaired driving to be generated by the community system (Holder, 2000). Advocates of community systems approaches hold the view that alcohol problems are caused by the existing community system, not by individuals, and the focus of intervention should be on changing the system (Holder, 2000; NASADAD, 2006). For instance, rather than focusing on getting drinkers to designate a driver, systems interventions might instead focus on getting policy makers to implement zoning restrictions governing alcohol outlet densities and increased enforcement of impaired driving (Holder, 2000). The Massachusetts Saving Lives Program (Hingson, Heeren, et al., 1996) and Community Trials Project (Holder, 2000) are briefly reviewed here as examples of multicomponent systems interventions for alcohol-impaired driving. Each program combined multiple intervention approaches for maximum effectiveness and focused on changing the community system, not individual behavior.

6.1. The Saving Lives Program The Massachusetts Saving Lives Program (Hingson, McGovern, et al., 1996) was implemented in six program communities for a 5-year evaluation period between 1988 and 1993. The six communities implemented multistrategy programs to reduce (relative to the rest of Massachusetts) alcohol-impaired driving and related problems such as speeding, failure to wear safety belts, and other moving violations.

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Task forces were organized within each experimental community to decide on and implement interventions. Efforts to reduce drunk driving and speeding included (1) media campaigns, (2) business informational programs, (3) awareness days, (4) speed watch hotlines, (5) police training, (6) high school peer education, (7) SADD, (8) college prevention programs, (9) alcohol-free proms, (10) beer keg registration, and (11) increased liquor outlet surveillance. Efforts to increase pedestrian safety and safety belt use included (1) media campaigns, (2) police checkpoints, (3) posted crosswalk signs warning of fines for failure to yield, (4) added crosswalk guards, (5) preschool education programs, and (6) training for medical staff. Evaluation methods included fatal and injury crash monitoring, direct observations of safety belt use and speeding, telephone surveys, and monitoring of traffic citations. Results indicated that program cities experienced a 25% fatal crash reduction during program years relative to the rest of Massachusetts. Also, there was a 42% reduction in fatal crashes involving alcohol. The number of fatally injured drivers with positive BAC decreased 47%, and the number of fatal crashes involving drivers aged 15e25 years decreased 39%. The number of 16- to 19-year-olds reporting drinking and driving decreased 40% in experimental communities. Other findings included a 27% reduction in fatal crashes involving speeding, an 18% reduction in the number of pedestrian fatalities, and a 17% increase in safety belt use. Finally, these strides resulted in a 10- to 15-fold savings relative to program expenditures.

6.2. The Community Trials Project The Community Trials Project (Holder, 2000) was a fivecomponent trial conducted in three experimental communities (in Northern California, Southern California, and South Carolina) matched to three comparison sites in the same regions. The cities had populations of 100,000. The primary strategy was to make structural changes in the community to reduce the irresponsible use of alcohol in conjunction with risky behavior that could result in trauma. The project was implemented in five phases over 5 years and had five components: (1) community mobilization of existing coalitions and task forces; (2) responsible beverage service initiatives to reduce customer intoxication and DUI; (3) increased enforcement of drinking and driving to increase risk for DUI detection; (4) an underage drinking component with increased enforcement of sales to underage individuals, training of merchants, and media coverage; and (5) decreased alcohol access with zoning controls of outlet density. For a more complete review of project components, see Holder (2000). Findings indicate that policy initiatives for each of the components were implemented in the experimental

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communities. Community training in techniques for working with media led to significant increases in coverage of alcohol-related newspaper and television news stories in experimental versus control communities. An increased number of responsible alcohol serving policies were adopted in the experimental communities compared to the control communities. There was a significant decrease in alcohol sales to individuals younger than age 21 years. The experimental communities were half as likely to sell alcohol to minors as were the comparison communities. There was a 6% decline in self-reported alcohol consumption per sitting as well as a 49% decrease of reported instances of drinkers “having had too much to drink.” In addition, self-reported DUIs decreased by 51% and assault injuries by 43% in these intervention communities (Holder, 2000). Finally, there was a statistically significant reduction in alcohol-related traffic crashes due to drunk driving enforcement coverage on the news. It is estimated that 78 fewer crashes occurred between 1993 and 1995 in the experimental communities. This number equates to a 10% reduction in alcohol-involved crashes. The total net savings from the intervention are estimated to be $2,032,590.

7. CONCLUSION This chapter reviewed large-scale prevention approaches targeting DUI prevention. DUI is a prevalent problem, and a number of strategies have been implemented that aim to decrease alcohol-impaired driving. Although the effects of some non-policy initiatives such as designated driver use are mixed, their relative effectiveness is enhanced when multiple interventions are combined. Mass media interventions, in particular, are reinforcing when combined with other interventions. The most effective large-scale approaches from this review appear to be legal and policy initiatives and multicomponent community systems approaches that aim to change the community system as a whole, not individual behavior.

REFERENCES Augustyn, M., & Simons-Morton, B. G. (1995). Adolescent drinking and driving: Etiology and interpretation. Journal of Drug Education, 25, 41e59. Baer, J. S., Marlatt, G. A., Kivlahan, D. R., Fromme, K., Larimer, M. E., & Williams, E. (1992). An experimental test of three methods of alcohol risk reduction with young adults. Journal of Consulting and Clinical Psychology, 60, 974e979. Baker, S. P., Braver, E. R., Chen, L., Li, G., & Williams, A. F. (2002). Drinking histories of fatally injured drivers. Injury Prevention, 8, 221e226. Balmforth, D. (1999). National survey of drinking and driving attitudes and behaviors. NHTSA DOT 808 644. Washington, DC: National Highway Traffic Safety Administration.

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Bandura, A. (1986). Social foundation of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Barr, A., & MacKinnon, D. P. (1997). Designated driving among college students. Journal of Studies on Alcohol, 59, 549e554. Berger, D. E., & Marelich, W. D. (1997). Legal and social control of alcohol-impaired driving in California: 1983e1994. Journal of Studies on Alcohol, 58, 518e523. Bjerre, B. (2003). An evaluation of the Swedish ignition interlock program. Traffic Injury Prevention, 4, 98e104. Caudill, B. D., Harding, W. M., & Moore, B. A. (2000). At-risk drinkers use safe ride services to avoid drinking and driving. Journal of Substance Abuse, 11, 149e160. Centers for Disease Control and Prevention (1999). Impaired driving fact sheet [online fact sheet]. www.cdc.gov/ncipc/factsheets/driving.htm. Centers for Disease Control and Prevention. (2010). Deaths: Final data for 2007. National Vital Statistics Reports, 58(19), 1e35. Centers for Disease Control and Prevention. (2011). Impaired driving: Get the facts [online fact sheet]. http://www.cdc.gov/ MotorVehicleSafety/Impaired_Driving/impaired-drv_factsheet.html. Chaloupka, F. J. (2004). The effects of price on alcohol use, abuse, and their consequences. In R. J. Bonnie, & M. E. O’Connell (Eds.), Reducing Underage Drinking: A Collective Responsibility. Washington, DC: National Academies Press. Connor, J., Norton, R., Ameratunga, S., & Jackson, R. (2004). The contribution of alcohol to serious car crash injuries. Epidemiology, 15, 337e344. Cox, E., & Fisher, S. (2009). Drinking on the dirt roads of America: NACCHO’s impaired driving prevention in rural communities demonstration site project. Journal of Public Health Management and Practice, 15, 278e280. Cummings, P., Rivara, F. P., Olson, C. M., & Smith, K. M. (2006). Changes in traffic mortality rates attributed to use of alcohol, or lack of a seat belt, air bag, motorcycle helmet, or bicycle helmet, United States, 1982e2001. Injury Prevention, 12, 148e154. Dang, J. N. (2008). Statistical analysis of alcohol-related driving trends, 1982-2005 (NHTSA DOT 810 942). Washington, DC: National Highway Safety Administration. 1982e2005 (NHTSA DOT 810 942). DeJong, W., & Atkin, C. K. (1995). Review of national television PSA campaigns for preventing alcohol-impaired driving, 1987e1992. Journal of Public Health Policy, 16, 59e80. DeJong, W., & Hingson, R. (1998). Strategies to reduce driving under the influence of alcohol. Annual Review of Public Health, 19, 359e378. DeJong, W., Schneider, S., Towvim, L., et al. (2006). A multisite randomized trial of social norms marketing campaigns to reduce college student drinking. Journal of Studies on Alcohol, 67(6), 868e879. DeJong, W., & Wallack, L. (1992). The role of designated driver programs in the prevention of alcohol-impaired driving: A critical reassessment. Health Education Quarterly, 19, 429e442. DeJong, W., & Winsten, J. A. (1999). The use of designated drivers by US college students: A national study. Journal of American College Health, 47, 151e156. Deshapriya, E. B. R., & Iwase, N. (1998). Impact of the 1970 BAC 0.05 mg percent limit legislation on drunk-driver-involved traffic fatalities,

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Chapter 18

Speed(ing) A Quality Control Approach Thomas D. Berry,* Kristie L. Johnsony and Bryan E. Portery *

Christopher Newport University, Newport News, VA, USA, y Old Dominion University, Norfolk, VA, USA

1. INTRODUCTION Speed is an ever-present characteristic of human motion, like breathing, that often goes underappreciated. Evolutionary theory reminds us, however, of our ancestral recorddhow bipedalism (walking and running) was important to our species survival. The ability to move with speed from point A to point B has been theorized to have played a role in our evolution, such as increased brain size, muscular structure, innervations of organs, and brain and body thermoregulation (Bramble & Lieberman, 2004). The importance of speed was critical for the continued existence of early Australopithecus, Homo erectus, and Homo sapiens as a means to locomotor away from predators and to run down and wear out prey. Fast forward 4.4 million years, to the origins of civilization and culture, speed is no longer just an essential operant for hunting and avoidance of predators. The establishment of tribal groups, city-states, and empires necessitated the need to cross landscapes and coastal seas for the purpose of conducting trade, the bartering of goods, and the sharing of information. With the evolution of civilization and trade, warring plunderers and empire builders saw the need for transporting large armies, weapons, and accoutrements. The need to get from point A to point B (founded on a trade and military imperative) resulted in important land-based innovations that we continue to enjoy todaydthe roadways and highways. For instance, by 200 CE, the Roman Empire had constructed approximately 53,000 miles of highways and another 200,000 miles of secondary roadways (Richard, 2010). These highways, built over 500 years, began with the Appian Way (312 BCE, from Rome to Brindisi) until more than 29 highways radiated out from Rome and the Roman Forum to the edges of an extensive empire. Today, roadways and rates of speed continue to play an ever-increasing role in human travel and function. As a consequence of the industrial revolution, travel innovations

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10018-9 Copyright Ó 2011 Elsevier Inc. All rights reserved.

from horses and carriages to trains, automobiles, and trucks have developed in concert with a myriad of other emergent technologies of speed, from airplanes, jets, and rockets to computers, the Internet, and ever-faster microprocessors (Jorgensen & Stiroh, 2000). Both as a goal and as an outcome, new and evolving sciences and technologies have advanced the speed of human mobility. However, as contemporary vehicular speed technologies synergize into a post-combustible engine epoch, traffic safety experts remain humbled by the human costs associated with vehicular speed(ing) and the difficulty reducing these costs (Elvik, 2010). The purpose of this chapter is threefold. First, we recognize that explaining driver preferred speed(ing) is inherently complex. A review of the speed(ing) literature during past decades indicates a remarkably deep and broad data set, with many specific fields of study, theoretical orientations, levels of analysis, and lines of programmatic research throughout the world. Thus, we offer a quality control approach to this rich literature as an organizing heuristic for understanding the many and multifaceted influences on driver-selected vehicular speed. Second, we conduct a brief literature review as a means of demonstrating the usefulness of the quality control approach to (1) organize the rich literature, and (2) illustrate the science of speed(ing) as a big picture made up of a multitude of specific research questions and goals. In general, we hope that the quality control approach outlined in this chapter inspires others to conduct the necessary elaboration and refinement expected of an evolving science. Third, we discuss how government leaders, researchers, and safety and traffic experts may use the quality control approach to manage the qualities that influence driver speed and the associated consequences of speed (e.g., crashes and fatalities). Obviously, a major goal of traffic safety scientists is to better create interventions to reduce crashes and 249

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costs, innovate new technologies to avoid injury and death, and implement public policies to govern the many variables related to driver speed (e.g., roadway construction, collision warning systems, and posted speed limits). In addition, our approach is compared and integrated with other approaches to understanding the complexity of drivers, crash prediction and control, and driver preferred speed.

2. SPEED(ING) RESEARCH AS A QUALITY CONTROL INITIATIVE When building a car, auto manufacturers must address the complexities of design, planning, resources, processes, assembly, testing, delivery, sale, and customer satisfaction. The car being built can be considered a nexus because there are thousands of parts that make up many components, and these components are then assembled into a vehicle that has functionality and identity. Today, auto manufactures realize that cars of low quality are not likely to be appreciated by owners. The ability to manage multiple “assembly lines of causation” that come together to build a quality car is a daunting task. From engineers to assembly line workers, each plays a role in the building of a car, and thus each contributes to the car’s quality as revealed in some variance measure (e.g., number or rate of defects and errors). To help control variance and increase quality in the assembly of a vehicle, manufacturers turn to quality control initiatives. One quality control practice, used by manufactures to conceptualize a complex multideterministic outcome, is the Ishikawa diagram (Mears, 1995). The Ishikawa process is a method of discovery in which subject matter experts (SMEs) across different levels and domains of product manufacturing come together to discuss, identify, and integrate the many variables influencing product outcomes. Here, the aim of the Ishikawa effort is for SMEs to assess how different variables control large and small portions of the total variance, with the goal being that as increasingly more portions of the variance come under scrutiny and manufacturing control, increasingly less overall error variance is produced. In addition, as SMEs gather to discover and conceptualize the different and varied factors influencing the quality of an outcome, SMEs begin to appreciate the big picture that reveals the interconnection between and within variables controlling outcome variance. The Ishikawa method is scale invariant and can be applied to any system and system outcome. The Ishikawa method provides (1) an understanding of the emergent complexity from conducting a system outcome analysis (e.g., building an automobile and becoming cognizant of all of its subsystems), (2) analysis and integration of how variance is produced and how different subsystems might interact (e.g., how overall vehicle weight impacts breaking, steering, and tire performance), (3) insight into possible

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solutions to increase quality and control over variables producing unwanted variance, (4) a schema for mapping and visualizing the overall system of cause-and-effect chains and connections, and (5) information to manage and regulate different sources of variance (for a review of quality control tools and background, see Mears, 1995). Figure 18.1 shows an example of an Ishikawa diagram as applied to the manufacturing of a car. This generic Ishikawa diagram is a template to foster SMEs to conceptualize and analyze obvious and non-obvious cause-andeffect relations. By bringing together different SMEs responsible for specific factors (Enarsson, 1998; Stalhane, Dingsoyr, Hanssen, & Moe, 2003), they begin to see the overall outcome (the car) as a nexus, where each SME’s work makes a contribution to the whole. The Ishikawa diagram makes visible the multideterministic nature of the nexus (car as outcome), allowing SMEs to discuss and share ideas about how to address ways to control, manage, and change processes to better improve the outcome, the effect. Since the invention and popularization of the motor vehicle, traffic safety, accident analysis, and vehicle systems literature has become a broad and diverse field (evidenced by a Google Book search using as key words “traffic safety” that produced 918,000 hits; an Amazon book search with the same key words produced 5,009 hits). This broad field includes many subdisciplines with specific research interests; however, most researchers in these areas include vehicle speed as foundational to traffic, vehicle, and driver science, albeit speed as a variable may exist only as a background or assumed variable within the scope of the investigation. For example, the formal analysis of vehicle seat belt reminders (the warning lights and sounds) does not necessarily include vehicle speed as a studied parameter (Berry & Geller, 1991). However, a vehicle in motion with unbelted occupants is the assumed reason for investigating optimal signals to remind people to fasten their seat belts. A review of the traffic safety literature applied to driver speed(ing) documents the many methods and frameworks used to assess cause-and-effect relations. A typical behavioral approach to a specific problem response is to describe the behavioral contingency that includes the antecedent, behavior, and consequent conditions. The goal is then to discover the cause-and-effect relation that best predicts and controls the response studied. However, as the traffic safety literature reveals, few specific problem responses can be simply or easily described, let alone predicted and controlled (for a discussion concerning this difficulty, see Elvik, 2010). A driver’s speed(ing) is such a response. We refer to speed(ing) as a nexus variable because of the manifold causes and effects related to speed(ing); in other words, speed(ing) has many causes and many effects. The challenge of traffic safety investigators is not how to

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FIGURE 18.1 A basic Ishikawa diagram allows for assessment of primary and secondary causeeeffect relations across six major causal categories. The outcome or problem is assumed to be dependent on a system of causeeeffect parameters, each playing a role in the quality of the effect.

conduct empirical tests concerning specific variables that may influence or explain in part a driver’s speed(ing). On the contrary, the traffic safety literature appears healthy, displaying a progressive accumulation of empirical demonstrations and conclusions. Instead, the challenge is how to make sense of all the empirical lines of programmatic research that (directly or indirectly) connect to the nexus of speed(ing). Figure 18.2 shows an Ishikawa diagram in which vehicle speed(ing) is the nexus variable for two antecedent causal factors that break out into five main fields of study. Specifically, (1) driver factors include three primary causal factorsdperson, behavior, and culturedand (2) environmental factors include two primary causal factorsdroadway dynamics and vehicle systems and controls. Thus, these antecedent factors, as revealed in the literature, have been partitioned into five different lines of programmatic research that (in some empirically demonstrated way) influence driver speed(ing). Likewise, the foregoing antecedent factors are followed by numerous consequent factors, such as rates of vehicle crashes, occupant and pedestrian injuries and deaths, traffic fines and penalties, and influences on driver efficacy and selfidentity. These consequent factors are directly or indirectly associated with the speed of the vehicle. In other words, speed as a nexus variable placed in the context of traffic safety literature mediates the relation between antecedent and consequent factors. Quality control initiatives often begin with a thorough review of system outcomes or consequences. Sometimes

referred to as postmortem analysis (PMA), this review allows SMEs to evaluate the extent of the problem and the number and rate of defects, rework, loss, and errors (Stalhane et al., 2003). In addition, the PMA will also acknowledge positive qualities of the outcome. This aspect of a quality control initiative uses the PMA as a means of working backwards through the Ishikawa diagram, starting with the consequences (the effects) and ending with the antecedents (the causes). Obviously, the main focus for traffic, vehicle, and driver science typically rests on the serious issues of driver/passenger/pedestrian death and injury, collision costs, employee hours lost, transportation delays, as well as efficient and effective use of travel and energy. The following section begins our literature review by considering the traffic safety statistic related to the consequences of driver speed(ing).

3. THE CONSEQUENCES OF SPEED(ING): A POSTMORTEM ANALYSIS 3.1. Cost of Vehicle Crashes The right side of Figure 18.2 illustrates the many possible negative and positive outcomes related to a driver’s speed. From an Ishikawa perspective, the outcomes are indicators of quality control problems or achievements. When translated into data, these outcomes are foundational to national statistical summaries documenting the risks and costs of speed(ing). Agencies such as the National Highway Traffic Safety Administration (NHTSA), the Swedish National

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FIGURE 18.2 Application of the Ishikawa diagram to a driver’s speed(ing) behavior. Speed(ing) is considered a nexus variable because it stands between the antecedent causes (the driver and environment factors) and the consequential effects of a speed(ing) driver.

Road and Transport Research Institute, and the World Health Organization (WHO) rely on the physical and observable nature of data collected by patrolling officers and traffic safety researchers. Thus, governments and researchers can propose evidence-based policies, recommendations, and laws to remedy the risks and costs of speed(ing). In addition, agencies cross-tabulate the risk and cost data with demographic and circumstance measures and use this information to recommend and fund targeted interventions and investigations on the antecedent conditions to speed(ing). In general, driver speed contributing to a crash is typically noted when an officer charges the driver with a speeding offense. Speeding offenses include racing, speed due to recklessness, speed associated with not heeding environmental circumstances (e.g., snow, rain, and ice), and exceeding posted speed limits. Figure 18.3 shows that in the United States between 2000 and 2009, 33,000e44,000 people were involved in a fatal crash annually, albeit fatalities have declined each of the past 4 years. WHO estimates that worldwide, 1.2 million people die each year because of a fatal crash, with an annual cost of $518 billion (Peden et al., 2004). WHO also estimates that the cost of vehicle crash fatalities is regressivedthat is, the burden on

poorer nations is greater than that on more wealthy nations. From a quality-of-life perspective, the cost of vehiclerelated crashes to a nation’s gross national product (rich or poor) means a budgetary loss that cannot be allocated to other meaningful pro-social, health, infrastructural, or technological projects to improve the lives of their citizens. In terms of driver speed(ing), Figure 18.3 shows that approximately 30% of fatal crashes were related to the speed of the vehicle (NHTSA, 2010). U.S. data indicate that speedrelated crashes were associated with more than 10,000 fatalities per year, with an economic cost estimated to be approximately $40 billion. Figure 18.4 shows the relative frequency of crash injuries versus fatalities for roadways with different posted speed limits. Posted speed limits between 30 and 50 mph are associated with more injuryrelated crashes, whereas posted speed limits greater than 50 mph are associated with more crash-related fatalities than injuries. In addition, Figure 18.5 provides insight into crash fatality dynamics. This figure shows that across posted speed limits, more vehicle crashes ending in a fatality are more likely to involve a single-vehicle crash rather than a multiple-vehicle crash. These PMA statistics underscore the importance of traffic safety efforts to seek empirical answers and make evidence-based recommendations to

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FIGURE 18.3 Total and percentage of fatal crashes due to driver speed(ing) across 10 years.

remedy the costs of speed(ing). One recommendation has been to legislate social and financial consequences.

3.2. Traffic Fines and Penalties Another category of consequence for driving while speeding above posted speed limits is traffic tickets and legal penalties. These consequences are socially provided and legally mandated by society as a deterrent. In the United States, each state’s government determines the extent of penalties given to drivers who exceed the posted speed limits, and thus a range of penalties exists throughout the nation. One novel method in the United States, but not in European countries, is the introduction of speed cameras with radar detectors that capture and record license plates, and speeding fines are then mailed to owners of vehicles caught speeding (Tay, 2009). In the European Union, not only do speeding penalties differ across individual nations

but also some nations practice a conditional speeding penalty. For instance, in 2007, Switzerland passed a law that made traffic fine amounts based on a person’s income. One repeat speed offender was caught driving 60 mph in a 30-mph speed zone; his fine was $290,000 because his assets were estimated at $20 million (Jeremy, 2010).

3.3. Social and Economic Consequences Although much research and interest are focused on the negative consequences of speeding, investigators also note the positive effects of speeding. Specifically, economists attempt to calculate the financial cost to local and national economies due to traffic-related delays and even speeding fines. In other words, speed is related to corporate efficiency, effectiveness, growth, and development. In the United States, the transportation of goods within the trucking industry is reliant on predictable movements of FIGURE 18.4 Percentage of 2009 fatal and injury crashes by posted speed limit.

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FIGURE 18.5 Percentage of 2009 speed-related single- and multiple-vehicle crashes by posted speed limit.

trucks to their destinations. Variance in the system, such as traffic delays due to crashes, road construction, and overcrowding can often motivate drivers to speed or compensate in other ways, such as sleep-deprived driving. How a driver gets to a destination may depend on how a driver perceives the importance of when he or she will arrive (Fuller et al., 2009). Speeding as a consequence may provide a driver selfrelevant and incentive information. The social cognitive approach to traffic safety and driving is still a relatively new but fruitful field. We discuss this area in more detail as it relates to antecedents to speed(ing); however, Elliot and Thomson (2010) have reported interesting results applying the theory of planned behavior. Basically, speed(ing) as a behavior becomes for some people a critical self-identify feature, important to their overall self-concept. Thus, those drivers with speeding self-identities are motivated to confirm this identity with the act of speed within the context of other drivers and vehicles on the road. Furthermore, for a driver for whom speed is self-referent, the act of not speeding is disconfirming and thus requires selfregulation to correct the disconfirming information. From this perspective, driver destination is not as critical a consequence as the immediate state and the subsequent feedback presented to the driver’s self-identity concerning his or her vehicle’s speed. Speculation concerning the causes of road rage has considered the construct of selfidentity as one feature of this troubling outcome. Here, conflict occurs when one driver who is driving slower than another driver who “needs” to drive fast feels prevented from acting in accord to his or her own sense of self and skill level (Lajunen, Parker, & Strading, 1998).

4. ANTECEDENTS TO SPEED(ING) 4.1. Driver Factors On reviewing studies that focus on the driver as an antecedent cause to vehicular speed(ing), three broad categorical

factors emerge: person, behavior, and cultural variables. Figure 18.6 presents our Ishikawa diagram showing how these three main variables extend from the central spine leading to the speed(ing) nexus. Following the Ishikawa quality control process, we organize the “driver factor” literature according to the previously mentioned three factors. Thus, each empirical effect related to driver speed(ing) becomes a branch or subbranch extending from one of the three main factors. Note again that driver factor studies that demonstrate relative empirical control over driver speed(ing) are assumed to influence the consequences of speed(ing). In terms of the quality control process, changing causal system parameters ought to be reflected in ultimate ends of the manufacturing process (e.g., building a car with a better process creates a better car, which creates better sales and profit). This was the point of conducting a PMA.

4.1.1. Person Factors Research on driver characteristics can be divided into the following causal branches: demographic, personality, and information processing. For person factors, Figure 18.6 displays three subbranches, as mentioned previously, and each of these subbranches is linked to variables identified by empirical demonstrations or lines of programmatic research, and whose results contribute to the explanation of a driver speed(ing). Here, each of the three person factors is briefly reviewed. First, as discussed previously, a PMA of the speed(ing)related consequences identifies the clues and motivation to seek causal antecedent conditions. One set of PMA data is based on official national and state traffic records (e.g., annual speed-related fatalities). Using officer-based crash scene data and police traffic pullovers, a host of demographic data have been correlated with speed(ing). In terms of U.S. demographic factors descriptive of a driver’s speed(ing), there appears to be a strong sex difference: Males across all age groups (15 to 75þ years old) are

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FIGURE 18.6 The top half of the Ishikawa diagram displaying driver factors as causal to the speed(ing) nexus. Note that the consequences of speed(ing) are not shown here as in Figure 18.2.

associated with more speed-related fatalities than are females. A person’s age is another strong predictor of driver speeding and crash-related fatalities. Generally, 50-yearold or older drivers (men and women) account for 15% or less of speed-related crash fatalities. In contrast, male drivers between 15 and 24 years of age account for 37% of speed-related crash fatalities. These data are consistent with WHO data (Peden et al., 2004) and the broader psychological literature reviewing gender-based risk taking across behaviors and attitudes (Byrnes, Miller, & Schafer, 1999). Unfortunately, descriptive of driver risk taking is the use of alcohol and subsequent impaired driving. According to NHTSA (2008), 41% of all drivers who were speeding and involved in a fatal crash had a blood alcohol content (BAC) of 0.08 g/dl or higher. In contrast, only 15% of speeding drivers with a BAC of 0.00 g/dl were involved in a fatal crash. Second, research that focuses on the personality variables relevant to driver speed(ing) have expanded our understanding of the individual differences of driver behavior. Personality assessments involve collecting selfreport data away from the natural behavioral setting of the driver (e.g., questionnaires given to college students in a classroom). Champions of this approach seek theoretical and methodological tools for analyzing driver speed(ing).

For instance, Furnham and Saipe (1993), using Zuckerman’s Sensation Seeking Questionnaire and Eysenck’s Personality Questionnaire, discovered that drivers convicted of speeding offenses were correlated with high Psychoticism, low Neuroticism, and were high on Thrill and Boredom susceptibility scores. Adopting Rotter’s (1966) locus of control theory, investigators have shown how people’s driving behavior and traffic accident involvement may be related to their internal versus external beliefs about the control of traffic ¨ zkan & Lajunen, 2005; events (Montag & Comrey, 1987; O ¨ Warner, Ozkan, & Lajunen, 2009). Factor analyzing the traffic locus of control (T-LOC), Warner et al. (2009) were able to explain 64% of the variance of driver speed across five factors. The authors emphasize two factors that are representative of LOC: own behavior and vehicle/environment factors. The factor labeled “own behavior” is indicative of an internal LOC whereby items ask participants to rate how they personally drive (e.g., “I often drive with too high a speed”). The factor labeled “vehicle/environment” is indicative of an external LOC whereby items ask participants to rate the influence of driving circumstances (e.g., “bad weather and lighting conditions”). Findings showed that these two factors not only related to preferred speed, intention to comply to speed limits, and time spent in compliance but also differentiated across

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roadway posted speed limits (50 km/h (31 mph) vs. 90 km/h (56 mph)). Specifically, they found that driver preferred speed was negatively related to external beliefs that vehicle or environment factors may play a role in their driving, whereas driver preferred speed was positively related to internal beliefs about one’s own behavior. However, these findings were only true for roadways with the higher posted speed limit (i.e., 90 km/h) and not the lower speed limit. This empirical finding is more noteworthy when juxtaposed with NTHSA’s data indicating that more fatalities occur at higher posted speed limits than at lower posted speed limits. Another line of research attempts to identify constructs that better predict a person’s disposition toward speed(ing). Here, investigators develop numerous items thought to load on specific factors as related to an outcome. Thus, inventories are created out of factor analytic methods and tested for internal consistency and validity. For example, Gabany, Plummer, and Grigg (1997) developed their Speeding Perception Inventory inductively by asking students to list reasons why drivers exceed the speed limit. Out of 72 items, the students generated five factors that emerged as tapping into driver speed(ing): ego gratification, risk taking, time pressures, disdain of driving, and inattention. Not surprisingly, the factors did show gender differences, such that males agreed more strongly than females concerning the gains in ego gratification. The Gabany et al. (1997) methodology and results offer traffic safety literature insights and clues into the troubling consequences of speed(ing). In addition, personality research based on dispositional assessment can give direction regarding what to target for intervention, such as driver personality types or tendencies and their likes and dislikes about driving. Although this empirical evidence is less than objective because it is based on self-report, such data can be linked to behavior, allowing criterion-related validity procedures to substantiate the value of these driver traits and states to speed(ing). Thus, personality research can offer our quality control initiative clues to how to understand and control individual difference producing variation found in our more behavioral-based measures. Third, person-centered approaches to speed(ing) have explored how driver behavior is determined by how and what information is processed; such information includes societal norms, deployment of new vehicle technologies, and degree of self-knowledge and self-efficacy. Two theoretical approaches considered by speed(ing) investigators are risk homeostasis theory (RHT) (Wilde, 1994) and the theory of planned behavior (TPB) (Ajzen, 1985). In general, these two approaches assume that drivers process multiple types and levels of information. Each theory attempts to describe how people self-regulate their behaviors, but RHT and TPB differ on the potential and direction for behavioral change. RHT suggests that people become

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adapted to a certain level of estimated risk given specific activities, such as eating, smoking, sports, sex, and driving. Given a specific activity, a person’s risk estimation that becomes acceptable is based on his or her knowledge and configuration of the activity-relevant variables. RHT predicts that a change in any one activity-relevant variable (i.e., increase or decease risk) will not change the overall level of risk because the person will adjust his or her behavior (increase or decrease response) to maintain the original acceptable level of risk. Peltzman (1975) was one of the first to report that traffic safety regulations and the deployment of new vehicle safety technologies did not result in expected fatality reductions. Peltzman suggested that as safety regulations and vehicle equipment were acknowledged (i.e., cognitively processed) by drivers, they offset these risk-reduction strategies by “taking greater accident risk” by adjusting the manner of their driving (p. 717). For instance, a direct test of RHT showed that drivers of go-carts drove at higher speeds when seat belts were provided than when seat belts were not available (Streff & Geller, 1988). Later, additional evidence is provided that seems to support RHT. RHT is not without criticism concerning its validity as an explanation to why drivers behave badly given a safer world (Vaa, 2001). However, RHT is considered as a description of a driver’s apparent adjustment or reallocation of risky behaviors and adds nuance to a quality control initiative in that changes in one factor or variable may influence the parameter in another factor or variable branch in the Ishikawa diagram and thus influence the quality of speed(ing) and speed(ing) consequences. In contrast to RHT as a compensatory model, TPB predicts that risky behaviors are amendable to change, from higher to lower levels of riskiness. The determinants of behavior change for TPB involve the adjustments of three principal variables that influence a person’s intention to behave and then subsequent behavior (Figure 18.7). The three main antecedent variables are attitudes, subjective norms, and perceived behavior control. The TPB model assumes that human behavior is essentially a rational process whereby people make decisions by considering their likes and dislikes (attitudes), how others might judge them (subjective norms), and whether or not they believe they have the proper ability (perceived behavior control). Here, TPB is theoretically instrumental in providing three identifiable intervention targets for modifying the intentions and behaviors that place people at risk. For instance, intervention agents can target attitudes through public service announcements, formal education, or other attitude change and social influence techniques (Cialdini, 2001). Lawmakers, societies, and families can change the rules and practices that are expected of the people who make up these social bodies. Changing the rules and expectations establishes new norms and changes how

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FIGURE 18.7

people believe others will evaluate them (Rivis & Sheeran, 2003). In addition, teachers, educators, and parents can mentor, inform, and coach skills, and they can give gradual guidance and practice new and novel ways to behave. Such experiences are thought to increase a person’s belief in his or her ability to accomplish a task, what Bandura (1997) called self-efficacy. Evidence supporting TPB usefulness as a model accounting for variance related to drivers’ intention to speed as well as subsequent speed(ing) behavior ranges from 20 to 65% (Conner et al., 2007; Elliott & Armitage, 2009; Elliott, Armitage, & Baughan, 2003, 2005; Newnam, Watson, & Murray, 2004). Elliot and Thomson (2010) remarked that this evidence provides the possibility for selective intervention for speed limit offenders. However, TPB as a method for selective intervention has been little examined (for an exception, see Elliot & Armitage, 2009) with regard to how each predictor might be used to change speeding behavior (e.g., changing offender’s attitudes compared to conducting self-efficacy training). Independent of the TPB, research does exist concerning the effectiveness of driver education, attitude change, and efficacy training on driving and driver speed(ing). Thus, these cognitive components (albeit independent) can be compared to their relative effectiveness in changing a driver’s speed. Both RHT and TPB present models for how people process different types of information concerning their driving and associated risks. In the context of this chapter, we are not concerned about testing or arguing the merits of each theory. Instead, we see the purpose of RHT and TPB as representing different lines of programmatic research, each contributing to what might help explain the qualities of speed(ing). In light of a quality control initiative, RHT and TPB can be viewed as a set of parameters that when adjusted change the quality and dynamics of a driver’s speed(ing). Here, the emphasis is not on testing the merits of each

Theory of planned behavior.

theory through some experimental-deductive method but, rather, on serving a practical functiondincreasing our understanding of the different qualities of speed(ing) and improve related consequences.

4.1.2. Behavior Factors Driving is not a singular and serial routine of responses that allow a driver to travel from point A to point B. Instead, driving can be characterized as a multimodal and multilevel collection of response routines and subroutines. These driving routines and subroutines are relatively free from constraints and are often concurrently arranged by driver choice or habit. For instance, a driver operating his car at a preferred speed can turn on the FM radio, open a bag of corn chips, grab a few chips to eat, and then, while chewing on the chips and listening to music, check his cell phone for new text messagesdall while operating his car “safely.” In other words, normal “competent” driving no longer requires vigilance and explicit attention; that is, drivers feel free and competent to engage in tangential driving responses (TDRs), such as eating, drinking, choosing and listening to music or talk radio, and engaging in cell phone conversations, while maintaining the vehicle’s safety, integrity, and forward motion. Researchers have begun to describe drivers capable and proficient at switching between driving and TDRs, as well as monitoring the relative demands of these concurrent responses (Shinar, Tractinsky, & Compton, 2005). The complexity of this multitasking environment has shown that drivers may engage in compensatory adjustments when demands of one set of responses become more critical than demands of another (Haigney, Taylor, & Westerman, 2000). The extent to which drivers engage in TDRs that reduce or nullify the safe operation of vehicles has become a common definition of the driver distraction

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literature (Streff & Spradlin, 2000). How TDRs become critical distractions from safe driving and thus impair performance has been conceptualized as involving (1) dualtask interference (Strayer & Johnston, 2001), (2) lack of self-regulation and compensation (Poysti, Rajalin, & Summala, 2005), and (3) overloading cognitive capacity (Engstrom, Johansson, & Ostlund, 2005; Patten, Kircher, Ostlund, & Nilsson, 2004). Research indicates that drivers often adjust vehicle speed when engaged in TDRs that become expressive of driver distraction. Complexity of distraction has been categorized into four modes: visual, auditory, physical, and cognitive. However, these four modes are abstractions that describe and make up the “things” and “events” that are distracting, such as vehicle dashboard and consoles (e.g., vehicle FM radio), other personal devices or things (e.g., cell phone, food, or a drink), and other sources of information and communication (e.g., passengers, billboards, and roadway signage). For instance, Horberry, Anderson, Regan, Triggs, and Brown (2006) showed that drivers operating radios and CD players reduced their overall mean vehicle speed. Many simulator studies have found that drivers using cell phones decrease vehicle speeds and increase the headway distance from vehicles in front of them (Haigney et al., 2000; Strayer & Drews, 2004; Strayer, Drews, & Johnston, 2003). In addition, reduced speed was associated with drivers engaged in taxing cognitive tasks that simulated cell phone conversations (Haigney et al., 2000; Rakauskas, Gugerty, & Ward, 2004).

4.1.3. Cultural Factors Driving does not occur in a cultural, economic, or political vacuum. In fact, the cultural context we drive within is a complex dialectda tension between our desire to travel quickly and our need to maintain our safety. On the one hand, speed(ing) can be conceptualized as having symbolic and sociological importance demonstrating our identity, instrumentality, and membership to a community. Here, speed is related to how auto manufacturers design and style vehiclesdhow the manufacturers market vehicles in terms of horsepower, performance, and image. Vehicle styling and performance are idiomatic to our cultural ethos; thus, we seek style and performance that fit our own sense of self-identity. As a community of drivers, we must share the roadways as a sort of “commons” (Hardin, 1998; Porter & Berry, 2004). Our behavior on the roads is a public if not more of a cultural activity, which includes how we see ourselves as drivers (self-interest) and how others see us as drivers (collective interest). Driving on the roadway commons is akin to a cultural practice with agreed upon manners, etiquette, and rules. For instance, in the United States, drivers in vehicles with slower speeds should travel in right

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lanes, allowing vehicles of higher speeds to pass in left lanes. In terms of speed, the image of increasingly faster vehicles, with increasingly greater horsepower and performance, is symbolic of the twentieth-century space age, our modern existence and expression of freedom, where quicker is better than slower (Gleick, 1999). It remains to be seen in the twenty-first century, as postcombustible engines come online, whether “faster is better” is replaced by a different or revised organizing principle. One the other hand, speed(ing) requires public, private, and governmental institutions to coordinate our behaviors, resources, and laws in the building of the whole driving system (i.e., transportation and travel of people, freight, and information). Here, government agencies note the cost, injury, and fatality statistics and respond by passing laws and regulations to correct deficiencies and improve the safety of drivers. However, economists, government officials, business leaders, makers of vehicles, and the public realize the double-edge cost of legislating speed limits. Typically, faster speeds are thought to decrease travel times, consequently increase free time, and decrease driving fatigue, stress, and strain while increasing productivity and efficiency. These are argued as sufficient economic and personal reasons for legislating higher speed limits (Jorgensen & Polak, 1993). In contrast, safety experts note that increasing speed limits reduces the time drivers have to respond to unpredictable driving events and emergencies; increases stopping distances; and increases the probabilities of crashes, injuries, and fatalities (Farmer, Retting, & Lund, 1999; Joksch, 1993). In addition, in contrast to the economic reasons supporting higher speed travel, traffic safety investigators have shown that drivers are faulty estimators of “saved time” when considering travel at lower or higher speeds (Fuller et al., 2009). Legislating and posting speed(ing) limits is one direct way in which governments attempt to influence driver behavior. However, legislating speed limits is one part of the overall system of governing driver speed, driver choices, and driver norms. According to Lave and Elias (1994), setting speed limits and delegating officer enforcement and patrols likely influence driver choices concerning a driver’s roadway selection, perceptions of enforcement risk and avoidance, and the posted speed limits. In the United States, the federal government passed legislation to remove the national maximum speed limit of 55 mph. Currently, 33 states have raised speed limits to 70 mph or higher; typically, those highways identified as “rural interstates” have been given the higher speed limits compared to “urban interstates” or “other” roadways. Farmer et al. (1999) noted that the significance of increasing speed limits is not necessarily the designated speed limit but, rather, in how drivers distribute their choice of vehicular speed around and above the posted speed limit. Numerous investigations have shown that increases in the

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posted speed limit result in minor mean travel speed increases (2 or 3 mph). However, the more important finding was how drivers disperse themselves above the posted speed limit (Retting & Greene, 1997). These studies suggest that changes in speed limits not only shift the mean travel speed of a community of drivers but also influence the distribution of speeds reached around these means. For instance, after the repeal of the national maximum speed limits, Retting and Greene discovered that 40e50% of drivers in two localities in California and Texas were exceeding 70 mph. One possible mechanism for drivers shifting toward higher or lower speeds may be the result of modeling (i.e., driver observations of how other drivers choose a speed preference). As mentioned previously, the roadways can be considered a commonsda space shared by other drivers. What is shared is not only the space on the roadways but also the actual choices made by fellow drivers. Research indicates that drivers may use social comparison to gauge ˆ berg, Larsen, Glad, & Beilitheir own preferred speed (A ˆ berg, nons, 1997; Connolly & Fishbein, 1993; Haglund & A 2000). Perhaps the underlying bases for the Retting and ˆ berg found that more Greene (1997) result, Haglund and A variance is explained (41%) by the social comparison of drivers exceeding the speed limit at higher speeds (90 km/h) than by drivers at lower speeds (50 km/h). This research suggests that driving is conducted in a social environment, whereby drivers learn what is normal and acceptable speed(ing) by watching how others drive. Because drivers influence one another as they share the roadway commons, they also share the larger cultural context that may or may not be similar to other cultures. Cross-culture research attempts to compare people in different locations typically defined by nationality and culture (Triandis, 1989). According to WHO, substantial risk, injury, and fatality differences exist among the world’s nations. Fatality disparities across nations appear linked to overall national incomes, with lower and middle-income nations suffering the greatest incidence of death and injury (Peden et al., 2004). Such national differences associated with the correlation between national incomes and fatalities are likely due to a myriad of factors, such as the quality of roads, enforcement strategies, education levels, types of vehicles, national traffic laws, and driver compliance with laws and norms. From a social psychology perspective, Warner et al. (2009) investigated potential cultural differences between Swedish and Turkish drivers’ speed(ing) choice. Using TPB (Ajzen, 1985), Warner et al. showed that intentions to comply with speed limits could be revealed through cultural differences. These differences between Swedish and Turkish drivers’ choice of speed are essentially differences in attitudes, subjective norms, and perceived behavioral control. Specifically, drivers in Sweden report more positive attitudes toward speed

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compliance, are more positive about subjective norms, and feel more in control of their speed(ing). Consistent with fatality statistics as associated with increased speed(ing), Turkey reports more than four times the incidence of traffic-related deaths compared to Sweden (i.e., 38 vs. 8.5 fatalities per 100,000, respectively, as reported in Warner et al., 2009).

4.2. Environmental Factors In Figure 18.8, the second set of antecedent factors illustrated in the Ishikawa diagram map the influence of roadway dynamics and vehicle systems and controls. In general, environmental factors document the conditions that surround the driver in both proximate and contextual ways. Although environmental factors may surround the driver, the driver must process this source of external information to operate the vehicle; thus, the environment factors interact with the driver factors as discussed previously.

4.2.1. Roadway Dynamics Roadway construction budgets and responsibilities rest with multiple organizations, agencies, safety experts, engineers, and planners. How societies’ stakeholders plan, construct, and maintain (or not) the roadways influences the entire driveretrafficetravel system (Elvik, 2008; Wong et al., 2006). As Lave and Elias (1994) noted, driver selection of roadways and how they drive depend on a system of interacting variables (e.g., enforcement resources, speed limits, and the inherent risk of certain roads). Roadway management is critical to speed control, flow dynamics, and safe travel. As discussed previously, roadways can be considered a shared resourceda commons area or activity-focused environment (Porter & Berry, 2004). As drivers travel and operate their vehicles, their speed can change (slow to fast), and the conditions of the roadways can change. In terms of traffic fatality data, speed-related fatalities are often composed of two subsets: fatalities due to “exceeding posted speed limit” (EPSL) and those due to “driving too fast for conditions” (DTFFC). Road environments act as moderator to driver speed where unsafe speed is attributable to driver’s poor choice in speeding preference or not heeding the environmental cues that current vehicular speed has become increasingly riskier. Liu and Chen (2009) examined fatal crash statistics for both EPSL and DTFFC. Their analysis indicated that 55% of all fatal crashes were due to EPSL, and the other 45% were due to DTFFC. However, examination of overall speeding-related crashes indicated that 74% were due to DTFFC, suggesting driver inattention and inability to adjust vehicle speed so as to better navigate the conditions.

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FIGURE 18.8 The bottom half of the Ishikawa diagram displaying environment factors as causal to the speed(ing) nexus. Note the consequences of speed(ing) are not shown here as in Figure 18.2.

Interestingly, DTFFC was more likely to occur at higher posted speed limits (þ50 mph) than EPSL, whereas EPSL crashes were more likely to occur on lower posted speed limit roadways. During the past decade, speed compliance investigators have shown a pattern of driver disregard of posted speed limits (Mannering, 2009). Some suggest that this pattern of disregard has emerged because drivers perceive posted speed limits as political contrivance with cynical ends (agencies producing revenue streams; a similar public complaint has been made about red light running cameras). However, Mannering indicates that when posted speed limits and police patrols are linked to roadway safety, drivers decrease their self-reported estimates of how much over the speed limit they are willing to drive. Others have shown a “halo effect” given a previously speed limitenforced area: Average driver speeds are reduced for a few weeks after the completion of an enforcement period (Vaa, 1997). Speed limit science has consistently demonstrated that speed compliance is determined by the extent of driver belief in and an expectation of the probabilities concerning enforcement, tickets, and/or risk of crashing. For instance, Tay (2009) studied driver speed compliance on two- and four-lane roads adjacent to school and playground zones. His results indicate driver sensitivity to roadway placement next to school and playground zones.

Specifically, drivers in school zones compared to those in playground zones were less likely to be traveling above the speed limit (53 vs. 62%, respectively), and their actual speed above the posted limit was less (31.86 vs. 33.43 km/ h, respectively). In addition, driver speed differences with regard to two- and four-lane roads were not significantly different. Research has shown how explicit roadway information (posted speed limits and information signs) influences preferred speed. However, non-obvious roadway environments, especially the context of a roadway (rural vs. urban), appear to play a role in speed(ing) behavior. Using a simulator approach, Antonson, Mardh, Wiklund, and Blomqvist (2009) showed how different roadway landscape configurations influence driver choice of vehicle speed. Roadways placed in open-terrain settings as opposed to forested or varied surroundings were more likely to exhibit greater speeding behavior. Antonson et al. also found through self-report that participants felt more relaxed and safe on roadways within a context that was clear and open rather than forested and varied. Consistent with Antonson et al.’s research, Garber and Kassebaum (2008) showed that crash rates on Virginia roads were greater on roadways that were considered rural and urban two-lane highways. Rural and urban landscape driving and preferred speed, as studied by Antonson et al., may signal the inherent risks of these

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roadways and thus participants adjusted their speed accordingly. In another simulator study, investigators examined the effects of roadways on driver choices and fatigue. Oron-Gilad and Ronen (2007) hypothesized that drivers on straight roads compared to drivers on winding roads might engage in fatigue-coping strategies. Their results showed that drivers were more likely to increase speed as a function of time and fatigue on straight roads compared with winding roads. In addition to increased speed, drivers on straight roads were more likely to show performance decrements in steering quality and lane maintenance.

4.2.2. Vehicle Systems and Controls During the past 50 years, truck and automobile manufacturers have continued to improve the quality of vehicle engineering and assembly. As evolving vehicle and roadway technologies have advanced, enhancing vehicle design, safety, and performance, a kind of Gibsonian affordance has been created for drivers to perceive the viability of speed(ing) (Gibson, 1977, 1979). Research shows that vehicle systems important to the performance of the automobile, truck, or motorcycle can influence a driver’s speed(ing). Quality Planning (2010), an auto insurance analysis company, published a report suggesting that vehicle makes and models are related to how owners drive them. Their report shows that “spirited vehicles” are more likely to receive moving violations compared to “cautious vehicles.” The difference between these two types of vehicles indicates that “spirited vehicles” are more likely to be high-performance sedans, roadsters, coupes, and convertibles, whereas “cautious vehicles” tend to be family oriented vehicles such as SUVs, minivans, and sedans. The most obvious factors influencing speed include engine size, vehicle weight, and driver’s sense of vehicle integrity (Yannis, Golias, & Papadimitriou, 2005). Also, many of these vehicle factors are affected by age and use (Wasielewski, 1984) and thus can influence driver speed(ing) and speed-related crashes (e.g., tire wear, age of vehicle, and other vehicle system parameters). Non-obvious factors have also shown possible influences on driver speed. Safety features in general have been regarded as facilitating drivers to seek higher speeds, including driver side air bags, antilock braking systems (ABS), and electronic stability control (ESC). As discussed previously, risk compensation has been offered to explain the influence of these vehicle systems on speed(ing). In a naturalistic study, Aschenbrenner and Biehl (1994) compared taxi drivers with and without ABS and found through interviews that drivers with ABS were more likely to drive with greater risk and higher speeds. One possible reason why the risk-compensation effect occurs is that drivers believe that such devices enhance performance and

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safety. Given this understanding, drivers have an increased sense of perceived behavioral control. Vadeby (2009) evaluated risk compensation using a planned behavior theory approach. The results showed robust explanatory power, linking driver’s intention to speed as a function of driver’s believing that his or her vehicle was outfitted with ABS or ESC. However, for vehicles equipped with devices designed to provide warning information about the certainties of risk, drivers have been shown to adjust and decrease their vehicular speed. Both collision warning systems and intersection warning collision systems signal (auditory or voice) drivers of the immediate danger of a possible crash (Chang, Lin, Hsu, Fung, & Hwang, 2009). Simulator studies have shown that collision warning system devices condition drivers to decrease speed and increase reaction times to situations (e.g., urban intersection) that are inherently riskier than other situations (e.g., open, uncongested interstates; Chang et al., 2009). Although they have not been studied with regard to their influence on speed(ing), Young et al. (2008) have shown the utility of smart seat belt reminder systems that warn unbelted drivers and passengers when the vehicle is traveling at an operationally defined risky speed or when speeding over the posted official limit. Perhaps an ancillary part of vehicle systems and design of trucks and cars, Rudin-Brown (2006) showed that when speedometers are not available for driver reference, the driver’s relative height above the road influenced speed choice. This simulator approach attempted to model the height configuration provided to drivers of small sports cars versus drivers of SUVs. Drivers across vehicle types were asked to find a comfortable and safe speed to operate the vehicle sans the speedometer. Results suggested that SUV driver eye height was associated with increased speed choice. Such data reveals the complex nature of speed(ing) and underscores the basic theme of this chapterdthat speed(ing) is a behavior related to obvious and non-obvious causal variables. Furthermore, we suggest that to make sense of and resolve the overwhelming list of factors and variables (large and small, major and minor), investigators should treat speed(ing) as a quality control problem.

5. USING BIG PICTURES IN PRACTICE: IS IT POSSIBLE TO IMPROVE THE QUALITY OF SPEED(ING)? No single theoretical approach or model has emerged to organize the science of speed(ing). This is likely because of the complexity and multicausal nature of speed(ing) and not due to any inherent weaknesses of the science. One purpose of this chapter was to conduct a brief literature review as representative of the breadth and depth of the

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science of speed(ing). However, other investigators have offered theoretical frameworks to organize the evidencebased literature, typically with specific goals or approaches. Often, these theoretical frameworks apply a systems approach to traffic safety in general and specifically to problem responses or outcomes. One of earliest system approaches was by Haddon (1968), who conceptualized a three-by-three factor matrix system for treating the complexities of vehicle crashes. He partitioned causal factors relevant to vehicle crashes and injury into three categories: human, vehicle and equipment, and environment. He then related each of these three causal categories according to three crash stages (precrash, crash, and postcrash). This nine-cell matrix offered a big picture view of the system involved and recommendations to reduce crash injuries and fatalities. A more recent approach used by Garber and Kassebaum (2008) documented and evaluated causal factors associated with high-risk locations on urban and rural highways in Virginia. Their use of a fault tree analysis was indicative of systems approach seeking qualitative and quantitative details in statistical crash reports to better refine remedies and interventions. Most germane to this chapter was their focus on details and their precision in revealing the complexities of urban and rural highway risks. Similar to Haddon’s matrix, Garber and Kassebaum’s fault tree analysis captured the numerous and diverse details that could be subsumed under Haddon’s big picture of human, vehicle, and environment causal factors. Another way to appreciate the shear complexity of traffic safety issues is to review a body of work focused on theory and models. During approximately the past 10 years, Elvik has amassed a body of work dedicated to organizing the literature and framing different and possible ways to make sense of the traffic safety evidence. Briefly, he has sought to reveal the complexity by (1) creating conceptual frameworks to aid in the assessment of evaluation studies (Elvik, 2004), (2) exploring statistical regularities in accident databases that may reveal “laws of accident causation” (Elvik, 2006), (3) enumerating nine road safety characteristics (Elvik, 2008), (4) applying a taxonomic approach to difficult-to-solve road safety problems (Elvik, 2010), and (5) documenting the strengths and weaknesses of multivariate models applied to accident causation (Elvik, 2011). As we have stated, researchers in the field of traffic safety are busy generating important empirical evidence about factors that influence a driver’s speed(ing). However, as Elvik has demonstrated, the field is in need of conceptual frameworks and models to organize the vast body of evidence generated each year. In this chapter, we have offered a quality control initiative approach to this vast body of evidence concerning driver-selected speed. Using an Ishikawa diagram as our

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organizing process, we attempted to model an approach implemented by manufacturers and engineers that build highly refined and complex products (e.g., automobiles, computers, and software) that are sensitive to errors, mistakes, or defects. By applying the Ishikawa diagram, and viewing the diagram from an SME’s perspective, investigators of speed(ing) might better see the big picture. Natural to the use of an Ishikawa diagram is its heuristic value for visualizing the myriad of connections, major and minor causeeeffect relations, and allowing complexity to emerge as the primary focus rather than applying a complexity reduction approach. This chapter illustrated speed(ing) as a nexus variable, where speed(ing) mediates the relation between a multidimensional field of antecedent causes and a divergent array of positive and negative consequences. Although speeding is only one part of the driving dynamic that explains crash-related injuries and death, speed(ing) is no doubt a common factor assumed to play a necessary or sufficient role in most crash events. Consistent with findings of other investigators (Elvik, 1996, 2010, 2011; Haddon, 1968; Garber & Kassebaum, 2008) is how approaches such as Ishikawa diagrams aid in reducing crash events and improve safety. At first glance, an Ishikawa diagram’s strength is its prompting of generative discussion and examination of causes and outcomes. Using the Ishikawa diagram’s visual display, investigators and safety experts can begin to combine their ideas, thoughts, and efforts to initiate new empirical examinations. By revealing the complexity in an Ishikawa diagram to governing and funding agencies, the allocation of support might be more wisely targeted to important traffic safety problems or primary research. In addition, by noting in an Ishikawa diagram the numerous lines of research, interdisciplinary-based research might emerge, such as that involving personality psychology and intelligent speed adaptation equipment (Carsten & Tate, 2005). A second glance of an Ishikawa diagram bares its weaknessdthe basic lack of quantitative expressions, unlike path analysis techniques, multivariate statistics, or even meta-analytic-based literature reviews. However, advances in Ishikawa diagramming apply mathematical expression to the factors and variables in each branch, allowing for weighting and partitioning of variance (the “weighted Ishikawa diagram” process; Gwiazda, 2006). If such advances continue to progress, Ishikawa diagramming may prove to be a formidable tool, both as an organizing heuristic and as a measurement practice similar to a metaanalytical statistic (e.g., Cohen’s d). However, given the Ishikawa diagram’s original purpose as a qualitative tool, the addition of metrics will only add to its value as a quality control initiative, not replace it. Thus, we hope that traffic safety researchers might apply this tool in brainstorming

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laboratory or conference meetings as a function of PMA of traffic safety problems. In addition, we note the many reasons Elvik (2010) gives outlining the difficulties in solving the “speeding problem.” He may be correct that speeding is a social dilemma, in which speeding (1) is widespread and tolerated, (2) has a history and media support in film and sport, (c) has underestimated risks, and (d) is linked to the biology of young drivers. Overcoming these barriers and solving the effects of speeding will require a monumental effortdthe coordination and collaboration of the world’s engineers, designers, investigators, and theorists. This effort will no doubt need a framework and an organizing process. Perhaps the framework or initiative chosen should begin with the qualities of problem and process rather than the quantities. Ishikawa diagramming is just one approach that has met with industrial success, and it continues to solve a different sort of social dilemmadthe mass production of goods that are inherently complex to make but are reliable, remarkably safe, and regarded by the public as valuable.

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Chapter 19

Running Traffic Controls Richard Retting Sam Schwartz Engineering, Arlington, VA, USA

1. CRASHES: THE BASIS FOR CONCERN Traditional intersections place vehicles on a collision course. Conflicts between road users and associated collisions are controlleddbut not eliminateddthrough the use of traffic signals and stop signs, which regulate the entry of road users into intersections. High levels of voluntary compliance with traffic control devices such as traffic signals and stop signs are essential to maintain safe and orderly flow at intersections. Drivers who either deliberately or inadvertently disregard requirements to stop for red lights or fail to comply with stop sign requirements put themselves and other road users at risk of serious injury crashes. According to the National Highway Traffic Safety Administration (NHTSA), large numbers of fatal and injury crashes occur at traffic signals and stop signs. In 2009, 4861 fatal crashes and an estimated 578,000 injury crashes occurred at traffic signals and stop signs (NHTSA, 2010). A number of studies have examined the extent to which police-reported intersection crashes are associated with drivers running traffic controls. The following are examples: l

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In 2009, 676 people were killed and an estimated 113,000 were injured in crashes that involved red light running (Insurance Institute for Highway Safety (IIHS), 2010). Approximately half of the deaths in red light running crashes are pedestrians and occupants in other vehicles who are hit by the red light runners. In addition, IIHS research has found that motorists are more likely to be injured in urban crashes involving red light running than in other types of urban crashes. Retting, Williams, Preusser, and Weinstein (1995) conducted a detailed study of urban crashes based on a systematic sample of 4526 police crash reports from four urban communities. Of 14 crash types identified (which accounted for 76% of all crash events and 83% of injury crashes), running red lights, stop signs, and other traffic controls was the most common cause of all crashes (22%). Injuries occurred in 39% of crashes involving running a traffic control; this was the highest proportion of any crash type.

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10019-0 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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A nationwide (US) study of fatal crashes at traffic signals in 1999 and 2000 estimated that 20% of the drivers involved failed to obey the signals (Brittany, Campbell, Smith, & Najm, 2004). A detailed analysis of 1788 police-reported crashes at stop sign-controlled intersections in four U.S. cities found that stop sign violations accounted for approximately 70% of all crashes (Retting, Weinstein, & Solomon, 2003). Crashes involving drivers who failed to stop were more likely to cause injuries than crashes in which drivers stopped and then proceeded. Preston and Storm (2003) reviewed details of 768 rightangle crashes occurring at typical rural stop signcontrolled intersections in Minnesota. Of these, 26% involved a vehicle that ran through the stop sign. An additional 17% could not be identified relative to vehicle actions (either vehicle action prior to the crash was not documented or there was conflicting information). Right-angle crashes caused by vehicles running the stop sign were more severe than those in which drivers had stopped and then proceeded.

2. FREQUENCY OF VIOLATIONS 2.1. Red Lights The development and widespread use of red light camera enforcement technology in the United States during the past two decades has generated significant interest in quantifying the frequency of red light violations, particularly in communities contemplating or planning the use of automated traffic enforcement. This interest has produced a number of published studies that provide estimated violation rates under a range of traffic volumes and roadway settings. Although the available data are drawn from just three statesdCalifornia, Texas, and Virginiadthe range of communities and settings provides fairly solid evidence of the frequency with which red light violations occur absent strict and consistent enforcement. Porter and England (2000) observed red light running behavior at six urban intersections in three cities located in 267

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FIGURE 19.1 Red light violation rates in five Texas cities. Source: Bonneson and Son (2003).

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FIGURE 19.2 Red light violation rates in Fairfax, VA. Source: Retting et al. (1999a).

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FIGURE 19.3 Red light violation rates in Oxnard, CA. Source: Retting et al. (1999b).

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southeast Virginia. The study included 5112 observations of drivers entering the intersections. Overall, 35.2% of observed traffic signal cycles had at least one red light runner prior to the onset of opposing traffic. This rate represented approximately 10 violators per observation hour. Higher red light running rates were observed in cities with larger intersections and higher traffic volumes. Between the hours of 3 and 6 pm, during which driver behavior was observed, red light violators were more prevalent earlier in this period. As part of a project to develop and calibrate a tool to quantitatively determine if a red light running problem existed at specific intersections, Bonneson and Son (2003) collected red light running data in five Texas communities: Mexia, College Station, Richardson, Corpus Christi, and Laredo. The selection of study sites was based on a search for typical intersections that were not previously identified as having a problem with red light running. The rate of red light violations per 1000 vehicles entering each study approach ranged from 0 to 10.8, as shown in Figure 19.1. Approaches with higher traffic speeds were likely to have higher frequencies of red light running. Retting, Williams, Farmer, and Feldman (1999a, 1999b) collected red light running data in two citiesdFairfax, Virginia and Oxnard, Californiadprior to implementation of red light camera enforcement in both cities in 1997. In both studies, a red light violation was described as a vehicle entering the intersection after the light had been red a minimum elapsed time of 0.4 s and the measured vehicle speeds were at least 15 mph. The rate of red light violations per 1000 vehicles entering each study approach ranged from 1.4 to 5.6 in Fairfax and from 0.07 to 2.7 in Oxnard, as shown in Figures 19.2 and 19.3. For the Texas, Fairfax, and Oxnard studies that measured red light violation rates per 1000 entering vehicles, a fairly narrow range of violation rates was identified (Figure19.4). This relatively narrow range was identified despite the fact that observations were conducted under a wide range of environmental conditions, intersection geometry, signal timing, driver behavior and norms, police enforcement practices, traffic speeds, and other factors.

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FIGURE 19.4 Average violation rates from three studies

2.2. Stop Signs The vast majority of intersections in the United States are controlled by stop signs, at which drivers are legally required to come to a full stop before entering the intersection and may proceed only after yielding the right-of-way to road users lawfully in the intersection. Behavioral issues related to crashes at stop signcontrolled intersections and driver response to stop signs are addressed here. Pietrucha, Opiela, Knoblauch, and Crigler (1989) addressed the fundamental question of why drivers violate stop sign controls. Using data from field studies conducted at 142 urban intersections over 528 h of observation, the authors found a 67.6% stop sign violation rate. More than one-third of the drivers who violated the stop signs stated they did so because cross-street traffic volumes were low. For major roadway volumes of less than 6000 vehicles per day, stop sign violation rates decreased with increasing traffic volumes on the major roadway (note that major roadways generally must be crossed or turned onto by drivers after having approached from stop sign-controlled side roads).

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Stokes, Rys, Russell, Robinson, and Budke (2000) analyzed characteristics and circumstances of 139 crashes resulting from failure to yield the right-of-way at rural twoway stop sign-controlled intersections in Kansas. The majority of the crashes did not appear to be directly related to stop sign violations. Rather, most crashes appeared to be due to drivers who entered the major roadway and did not (or could not) accelerate quickly enough to avoid being struck by vehicles on the major roadway. This suggests that drivers on the minor roadway either did not see oncoming vehicles or failed to accurately estimate the speeds of oncoming vehicles on the major roadway. As part of a study to evaluate strategies to improve motorist compliance and caution at three stop signcontrolled intersections with a history of motor vehicle crashes, Van Houten and Retting (2001) scored from videotape the percentage of drivers coming to a complete stop and the percentage of drivers looking right before entering the intersection. Observational data were collected on the percentage of right-angle conflicts. During the baseline period, the proportion of motorists who came to a complete stop at three Florida study sites ranged from 46 to 66%. The average number of right-angle conflicts at the three study sites during the baseline period ranged from 6.2 to 10.2 per 100 vehicles. The baseline period was followed by introduction of a light-emitting diode (LED) sign that featured animated eyes scanning left and right to prompt drivers to look left and right for approaching traffic. The LED sign was associated with an increase in the percentage of vehicles coming to a complete stop at all three sites. On average, the percentage of vehicles coming to a complete stop before entering the intersections across all three sites increased from 55 to 77%.

3. DRIVER CHARACTERISTICS In addition to field studies that have quantified the frequency of traffic control violations, a number of studies have examined characteristics of drivers observed running red lights. In contrast to the notion that red light running and stop sign violations are caused by poorly timed traffic lights and illegible stop signs, the findings of driver characteristics studies reveal that running of traffic controls is associated with drivers who have distinct demographic and behavioral patterns. Distinguishing characteristics of traffic control violators, as a group, include relatively young driver age (e.g., younger than 30 or 35 years), lack of seat belt use, and poor driving records (e.g., multiple prior speeding convictions).

3.1. Age One of the earliest known studies that examined characteristics of red light runners was conducted in the United

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Kingdom. Lawson (1991) compared characteristics of crash-involved red light running drivers with those of nonviolating drivers in these crashes. As a group, the red light runners were more likely to be younger than 35 years of age. A similar analysis of U.S. red light running crashes was conducted by Retting, Ulmer, and Williams (1999), who compared characteristics of crash-involved red light running drivers with those of nonviolating drivers in these crashes. As a group, the red light runners were more likely to be younger than 30 years of age. Retting and Williams (1996) created a profile of red light runners by studying driver behavior at an Arlington, Virginia intersection equipped with a “test” red light camera that was not in use for actual traffic enforcement. The study compared characteristics of drivers observed running red lights with those of motorists who had an opportunity to run a red light but did not. As a group, red light runners were younger. Yang and Najm (2007) examined red light running behavior using almost 50,000 violation records that were captured by red light cameras at 11 intersections in the city of Sacramento, California during a 4-year period. This study identified factors with strong correlation to red light running behavior. With regard to driver demographics, key findings were that younger drivers showed a higher tendency to run red lights and were more likely to commit such a violation at speeds higher than the posted speed limit. Approximately 30% of red light runners were younger than 30 years of age. Driver characteristics associated with the running of stop signs were examined in two studies. In crashes at stop sign-controlled intersections in which drivers failed to stop, young drivers were notably represented; 33% of the at-fault drivers in these crashes were younger than 21 years of age (Retting et al., 2003). Preston and Storm (2003) compared the age of drivers involved in crashes at stop signcontrolled intersections in which drivers failed to stop to the expected distribution based on statewide crash totals. Drivers between the ages of 25 and 40 years were significantly overrepresented in “Ran Thru STOP” crashes.

3.2. Gender Several observational and crash-based studies have examined the role of gender in red light running. Whereas observational studies do not show a consistent gender pattern, crash-based studies indicate overinvolvement of male drivers. Martinez and Porter (2006) conducted a beforeeafter field evaluation of red light camera enforcement at eight intersections in southeast Virginia. The study included five phases: Phases 1 and 2 took place before red light cameras were installed, phase 3 occurred when one intersection had a camera that was issuing warning notices, phase 4 occurred when one camera was in the citation phase and

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one camera was in the warning phase, and phase 5 occurred when three cameras were fully operational. Overall, 12.7% of last drivers entered intersections on red. Demographics were recorded for 1433 drivers who entered on yellow or red. Overall, men had higher raw red light running rates than women; however, the only significant difference between men and women occurred during phase 1. Red light running rates for both men and women declined from baseline levels and reached their lowest levels during phase 4. The observational study conducted in Arlington, Virginia by Retting and Williams (1996) found no gender differences between red light runners and drivers who did not run red lights. Males comprised 71% of both groups of driversdred light runners and those who complied with red lights. Two crash-based studies found that crash-involved red light running drivers were more likely than nonviolating drivers in these same crashes to be male. These findings are from Lawson’s 1991 UK study, which compared characteristics of crash-involved red light running drivers with those of nonviolating drivers in the same crashes, and Retting, Ulmer, et al.’s 1999 U.S. study, in which characteristics of crash-involved red light running drivers were compared with those of nonviolating drivers in the same crashes.

3.3. Ethnicity There is little evidence from the available literature that ethnicity plays a significant role in red light running behavior. In the Porter and England (2000) study in which red light running behavior at six urban intersections in southeast Virginia was observed, participants were defined as drivers who were last to enter the intersections during observed light cycles. Specifically, the last driver crossing the intersection bar prior to the onset of opposing traffic was identified for data collection. After controlling for city and time differences, non-Caucasian drivers were more likely to run red lights. In a separate field observation study also conducted in southeast Virginia, Martinez and Porter (1996) did not find ethnicity to be a predictor of running red lights. The only significant difference in red light running as a function of ethnic group classification was during phase 2, when non-whites were more likely to run red lights than whites. During phases 2 and 3, unbuckled drivers ran more red lights than did buckled drivers. One crash-based study was identified. Using 1990e1996 data from the Fatality Analysis Reporting System, Romano, Voas, and Tippetts (2006) analyzed racial/ethnic information for driver fatalities in crashes that occurred at stop signcontrolled intersections. The crashes involved one or more drivers coded as failing to obey the traffic device. Overall, the study found no direct difference among African American, white, and Hispanic drivers regarding stop sign

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running. However, the authors found that race/ethnicity does play an indirect role in this problem through its impact on drinking and driving as well as its interaction with age and gender.

3.4. Safety Belt Use and Other Driving Behaviors A number of field observation studies and crash-based analyses conducted in the United States during two decades have found that drivers who run red lights, as a group, are less likely to wear seat belts and have poorer driving records than law-abiding drivers observed in the same settings. The earliest such study by Deutsch, Sameth, and Akinyemi (1980) examined the relationship between safety belt usage by drivers and red light running behavior. At two urban intersections in Baltimore, Maryland, observations were made of shoulder belt use and whether corresponding drivers ran red lights. During 70% of the traffic light changes observed, at least one driver went through a red light. Of the drivers who went through red lights, only 1% were wearing shoulder belts, compared with 8% of drivers of the other vehicles observed. Retting and Williams (1996) found red light runners were less likely to use safety belts than were drivers who stopped for red lights: 67% of violators were observed wearing shoulder harnesses compared to 74% of compliers. Red light runners had poorer driving records than drivers who stopped for red lights, including the finding that violators were more than three times as likely as compliers to have multiple speeding convictions on their driver records. Martinez and Porter (1996) and Porter and England (2000) examined characteristics of drivers observed running red lights at intersections in southeast Virginia. The first study collected observations at eight intersections in two cities, whereas the latter focused on six intersections in three cities. Both studies found that unbuckled drivers were more likely to run red lights than drivers who were wearing seat belts. In a similar field observation study that examined speeding rather than running traffic controls, Preusser, Lund, Williams, and Blomberg (1987) observed seat belt use among drivers traveling on limited-access highways before and after enactment of a mandatory seat belt use law. The results showed that high-speed drivers had lower belt use rates before the law and increased their belt use less in response to the law. Belt use rates before the law were 25, 29, and 28% for the high-, medium-, and low-speed groups, respectively. After the law, belt use rates among these groups were 51, 64, and 57, respectively. The crash-based study by Retting, Ulmer, et al. (1999), in which characteristics of crash-involved red light running drivers were compared with those of nonviolating drivers in the same crashes, found that crash-involved red light

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running drivers were more likely than nonviolating drivers in these crashes to have prior moving violations and convictions for alcohol-impaired driving and to have an invalid driver license.

4. DRIVER ATTITUDES Two studies were identified that analyzed public opinion survey data to assess self-reported red light running behavior and driver attitudes toward red light running. These research efforts indicate both widespread public recognition of the dangers of running traffic controls and yet an alarming rate of self-reported red light running. In a nationwide (US) survey of self-reported red light running behavior, approximately one in five respondents reported running red lights when entering the last 10 signalized intersections (Porter & Berry, 2001). Younger drivers were more likely to admit to red light running. Despite the prevalence of self-reported red light running behavior, the majority of drivers indicated that red light running was dangerous. Drivers reported being more likely to run red lights when alone, suggesting the presence of a passenger might cause drivers to behave more responsibly. Drivers generally perceived few consequences for running red lights, with fewer than 6% having received a red light running ticket and most believing that police would catch fewer than 20% of violators. In 2002, NHTSA conducted a national survey of speeding and unsafe driving attitudes and behavior (NHTSA, 2004). To explore public attitudes and behaviors related to unsafe driving practices, the national sample of drivers was asked how safe or dangerous they believed a set of driving behaviors usually were. The behaviors included driving through a stop sign without slowing and driving through a traffic light that was already red before entering the intersection. Driving through stop lights and stop signs (along with railroad crossings with flashing red lights) were rated among the most dangerous activitiesdequal to or worse than driving just below the legal alcohol limit.

5. DRIVER RESPONSE TO ENFORCEMENT Deterrence theory assumes that individuals will be deterred from undertaking a particular action by the threat of punishment, either real or perceived (Fildes, 1995). Sanctions are effective in modifying behavior to the extent they are perceived as being certain, swiftly applied, and sufficiently severe. Much of the knowledge base on deterrence effects of traffic enforcement derives from studies of alcohol-impaired driving and, to a lesser extent, speeding. Published evidence suggests increasing the threat and/or certainty of punishment is critical to deterring alcoholimpaired driving (Ross, 1982; Shinar & McKnight, 1984).

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Likewise, Ostvik and Elvik (1990) found that increasing the perceived risk of detection is one of the most important objectives of all speed enforcement strategies. On the other hand, the severity of punishment is thought to have far less of a deterrent effect than the certainty of detection and punishment, as evidenced by evaluations of anti-drunk-driving legislation. Efforts directed mainly at increasing potential drunk drivers’ perceived certainty of punishment frequently have a deterrent effect in the short term. In the long term, however, indexes of drunk driving return to prior levels (Ross, 1984). According to Ross, this phenomenon may be explained by the very low actual probability of punishment. Efforts directed principally at increasing the perceived severity of punishment have not had the desired deterrent effects, perhaps because of the low levels of certainty that these punishments will be applied. Therefore, the literature suggests traffic enforcement will generally be effective if the individual believes there is a high risk of being detected, with the severity of punishment generally having less influence on driver behavior and decision making. Unfortunately, traditional police enforcement of red lights, stop signs, and other violations is infrequent compared with the number of traffic control devices and frequency of driver violations. Thus, traditional enforcement methods have the distinct disadvantage of generally projecting a low risk of being detected. A comparison can be drawn to traditional police efforts to enforce speed limits. Field studies by Barnes (1984) and Hauer, Ahlin, and Bowser (1982) found that speed reductions associated with traditional speed enforcement lasted only several kilometers after police were encountered. Socalled “spillover” and “distance halo effects” are a key advantage of automated traffic enforcement that generally are not achieved by traditional police speed enforcement (Zaal, 1994). Red light cameras have been found to substantially reduce red light violations. Evaluations in Fairfax (Retting et al., 1999a) and Oxnard (Retting et al., 1999b) reported reductions in red light violation rates per 10,000 vehicles of approximately 40% at camera-enforced sites. In addition, reductions in both communities carried over to signalized intersections not equipped with cameras, indicating communitywide changes in driver behavior. Following implementation of red light cameras in Raleigh and Chapel Hill, North Carolina, Cunningham and Hummer (2004) found a significant reduction in the percentage of drivers entering more than 2 s after onset of the red signal. The beforeeafter field evaluation of red light camera enforcement at eight intersections in southeast Virginia found that implementation of camera enforcement was associated with a 78% reduction in targeted violations (Martinez & Porter, 1996). Fitzsimmons, Hallmark, McDonald, Orellana, and Matulac (2007) compared violation rates at camera-enforced

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intersections and comparison sites in Clive, Iowa. They found that on average, approaches without cameras experienced 25 times more violations than approaches with cameras. Based on prior research indicating the duration of yellow timing affects the frequency of red light violations, and thus should be set to appropriate levels (Bonneson & Zimmerman, 2004), Retting, Ferguson, and Farmer (2008) examined incremental effects of first lengthening yellow signal timing followed by the use of red light cameras. This study was conducted in conjunction with the introduction of Pennsylvania’s first red light cameras in Philadelphia. Increasing yellow timing by approximately 1 s, to levels associated with engineering criteria, reduced red light running by 36%. After accounting for effects of longer yellow timing, the activation of red light cameras was associated with an additional 96% reduction in violations.

6. SUMMARY Running traffic controls is a substantial contributing factor to large numbers of fatal and serious injury crashes that occur at signalized and stop sign-controlled intersections. Numerous field observational studies provide evidence of the extent to which drivers run traffic lights and stop signs at both urban and rural intersections. Several studies have documented characteristics of drivers either observed running traffic signals and stop signs or recorded on police crash reports as having run these types of traffic controls. Although not completely consistent, the findings generally indicate that drivers who run red lights and stop signs are more likely than law-abiding drivers to be relatively young, more likely (for crash-involved drivers) to be male, and less likely to use safety belts. The finding of young driver involvement is consistent with survey research that shows younger drivers are more likely to self-report red light running behavior. The association between young drivers and running traffic controls may be related to factors noted by Shinar (2007), including problems with risk perception, high-risk lifestyle, and susceptibility to peer pressure to assume more risk from passengers/friends. Evaluations of driver ethnicity show no association between ethnic classification and running of traffic controls. In terms of modifying behavior, traffic enforcement is generally effective if the individual believes there is a high risk of being detected, with the severity of punishment generally having less influence on driver behavior and decision making. Unfortunately, traditional enforcement methods have the distinct disadvantage of generally projecting a low risk of being detected. On the other hand, automated traffic enforcement, which conveys a high risk of being detected, has been shown to dramatically modify driver behavior.

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REFERENCES Barnes, J. W. (1984). Effectiveness of radar enforcement. Wellington, New Zealand: Road Traffic Safety Research Council. Bonneson, J. A., & Son, H. J. (2003). Prediction of expected red-light running frequency at urban intersections. Transportation Research Record, 1830, 38e47. Bonneson, J. A., & Zimmerman, K. H. (2004). Effect of yellow-interval timing on the frequency of red-light violations at urban intersections. Transportation Research Record, 1865, 20e27. Brittany, N., Campbell, B. N., Smith, J. D., & Najm, W. G. (2004). Analysis of fatal crashes due to signal and stop sign violations. (Report No. DOT HS-809-779). Washington, DC: National Highway Traffic Safety Administration. Cunningham, C. M., & Hummer, J. S. (2004). Evaluating the use of red light running photographic enforcement using collisions and red light running violations. Raleigh, NC: North Carolina Governor’s Highway Safety Program. Deutsch, D., Sameth, S., & Akinyemi, J. (1980). Seat belt usage and risktaking behavior at two major traffic intersections. In Proceedings of the 24th Conference of the American Association for Automotive Medicine. Fildes, B. (1995). Driver behavior and road safety. In N. Brewer, & C. Wilson (Eds.), Psychology and policing. Hillsdale, NJ: Erlbaum. Fitzsimmons, E. J., Hallmark, S., McDonald, T., Orellana, M., & Matulac, D. (2007). The effectiveness of Iowa’s automated red light running enforcement programs. (Report No. CTRE Project 05-226). Ames, IA: Iowa State University, Center for Transportation Research and Education. Hauer, E., Ahlin, F. J., & Bowser, J. S. (1982). Speed enforcement and speed choice. Accident Analysis and Prevention, 14, 267e278. Insurance Institute for Highway Safety. (2010). Q&As: Red light cameras. http://www.iihs.org/research/qanda/rlr.html. Accessed January 13, 2011. Lawson, S. D. (1991). Red-light running: Accidents and surveillance cameras. (AA/BCC-3). Basingstoke, UK: AA Foundation for Road Safety and Birmingham City Council. Martinez, K. L., & Porter, B. E. (2006). Characterizing red light runners following implementation of a photo enforcement program. Accident Analysis and Prevention, 38, 862e870. National Highway Traffic Safety Administration. (2004). National survey of speeding and unsafe driving attitudes and behavior: 2002; Volume 2: Findings report. (No. DOT HS-809-730). Washington, DC: U.S. Department of Transportation. National Highway Traffic Safety Administration. (2010). Traffic safety facts 2009 early edition. (Publication No. DOT HS 811 402). Washington, DC: U.S. Department of Transportation. Ostvik, E., & Elvik, R. (1990). The effects of speed enforcement on individual road user behavior and accidents. Proceedings of the International Road Safety Symposium in Copenhagen, Denmark, September 1990. Pietrucha, M. T., Opiela, K. S., Knoblauch, R. L., & Crigler, K. L. (1989). Motorist compliance with standard traffic control devices final report. (FHWA RD-89-103, Ncp 3A1c00222). Washington, DC: U.S. Department of Transportation. Porter, B. E., & Berry, T. D. (2001). A nationwide survey of self-reported red light running: Measuring prevalence, predictors, and perceived consequences. Accident Analysis and Prevention, 33(6), 735e741. Porter, B. E., & England, K. J. (2000). Predicting red-light running behavior: A traffic safety study in three urban settings. Journal of Safety Research, 31, 1e8.

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Preston, H., & Storm, R. (2003). Reducing crashes at rural thru-stop controlled intersections. (Report No. MN/RC-2003-15). In K. A. Harder, J. Bloomfield, & B. J. Chihak (Eds.), Crashes at controlled rural intersections. St. Paul, MN: Local Road Research Board, Minnesota Department of Transportation. Preusser, D. F., Lund, A. K., Williams, A. F., & Blomberg, R. D. (1987). Belt use by high-risk drivers before and after New York’s seat belt use law. Accident Analysis and Prevention, 20(4), 245e250. Retting, R. A., Ferguson, S. A., & Farmer, C. M. (2008). Reducing red light running through longer yellow signal timing and red light camera enforcement: Results of a field investigation. Accident Analysis and Prevention, 40, 327e333. Retting, R. A., Ulmer, R., & Williams, A. F. (1999). Prevalence and characteristics of red light running crashes in the United States. Accident Analysis and Prevention, 31, 687e694. Retting, R. A., Weinstein, H. B., & Solomon, M. G. (2003). Analysis of motor vehicle crashes at stop signs in four U.S. cities. Journal of Safety Research, 34(5), 485e489. Retting, R. A., & Williams, A. F. (1996). Characteristics of red light violators: Results of a field investigation. Journal of Safety Research, 27, 9e15. Retting, R. A., Williams, A. F., Farmer, C. M., & Feldman, A. (1999a). Evaluation of red light camera enforcement in Fairfax, Virginia. ITE Journal, 69(8), 30e34. Retting, R. A., Williams, A. F., Farmer, C. M., & Feldman, A. (1999b). Evaluation of red light camera enforcement in Oxnard, California. Accident Analysis and Prevention, 31, 169e174.

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Retting, R. A., Williams, A. F., Preusser, D. F., & Weinstein, H. B. (1995). Classifying urban crashes for countermeasure development. Accident Analysis and Prevention, 27, 283e294. Romano, E., Voas, R., & Tippetts, S. (2006). Stop sign violations: The role of race and ethnicity on fatal crashes. Journal of Safety Research, 37, 1e7. Ross, H. L. (1982). Deterring the drinking driver. Lexington, MA: Lexington Books. Ross, H. L. (1984). Social control through deterrence: Drinking-anddriving laws. Annual Review of Sociology, 10, 21e35. Shinar, D. (2007). Traffic safety and human behavior. Oxford: Elsevier. Shinar, D., & McKnight, A. J. (1984). The effects of enforcement and public information on compliance. In L. Evans, & R. C. Schwing (Eds.), Human behavior and traffic safety. New York: Plenum. Stokes, R. W., Rys, M. J., Russell, E. R., Robinson, R. K., & Budke, B. (2000). Analysis of rural intersection accidents caused by stop sign violation and failure to yield the right-of-way. (Report No. K-TRAN: KSU-98-6). Manhattan, KS: Kansas State University. Van Houten, R., & Retting, R. A. (2001). Increasing motorist compliance and caution at stop signs. Journal of Applied Behavioral Sciences, 34, 185e193. Yang, C. Y., & Najm, W. G. (2007). Examining driver behavior using data gathered from red light photo enforcement cameras. Journal of Safety Research, 38, 311e321. Zaal, D. (1994). Traffic law enforcement: A review of the literature. (Report No. 53). Victoria, Australia: Monash University Accident Research Centre.

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Chapter 20

Driver Distraction Definition, Mechanisms, Effects, and Mitigation Michael A. Regan and Charlene Hallett French Institute of Science and Technology for Transport, Development and Networks, Lyon, France

1. INTRODUCTION Driving is a complex activity performed in an environment that is constantly evolving and involves the simultaneous performance of multiple subtasksdroute finding, route following, velocity control, collision avoidance, rule compliance and vehicle monitoring (Brown, 1986). Despite this complexity, however, drivers often engage in additional activities that can take both their mind and their eyes off the road and their hands off critical vehicle controls (e.g., the steering wheel and gear controls). There is accumulating evidence that driver distraction is a significant contributing factor in crashes and critical incidents (Craft & Preslopsky, 2009; Gordon, 2008; Olson, Hanowski, Hickman, & Bocanegra, 2009; Stutts et al., 2005), and there is converging evidence that driver inattention is also a major contributing factor in critical incidents, including crashes (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). The past decade has witnessed an explosion in research on driver distraction that has led to the publication of the first book on the topic (Regan, Lee, & Young, 2008), a biannual international conference series on driver distraction and driver inattention (Regan & Victor, 2009), and a national summit on distracted driving (i.e., driver distraction) held by U.S. Transportation Secretary Ray LaHood (Department of Transportation, 2009). This chapter provides a general overview of the term “driver distraction”dwhat it means, how it relates to driver inattention, types of driver distraction, sources of driver distraction, factors that moderate the effects of distraction on driving, the interference that can derive from distraction, theories that seek to explain this interference, the impact of distraction on driving performance and safety, and strategies for mitigating the effects of driver distraction. In addition, this chapter explains the current thinking on driver inattention and how it relates to driver distraction. Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10020-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

The chapter starts by considering what is meant by the term driver distraction.

2. DRIVER DISTRACTION: DEFINITION The term driver distraction has been widely discussed and studied, implying that people understand what driver distraction actually means (Regan, Lee, et al., 2008). However, as a scientific concept, driver distraction has been inconsistently defined. In addition, many research papers on driver distraction fail even to define the very construct they set out to investigate. The lack of an agreed definition is problematic because it can make interstudy comparisons difficult and can lead to vastly different estimates of the role of distraction in crashes and critical incidents (Gordon, 2008). Dictionary definitions of distraction vary somewhat but are consistent in suggesting that distraction involves a diversion of attention away from something toward something else. The New Oxford American Dictionary (computer edition, 2000), for instance, defines distraction as something “that prevents someone from giving full attention to something else.” Definitions of distraction, in the context of driving, are similarly diverse. The following list is a small sample of definitions, drawn from the literature, that illustrates this point. Definitions 1 and 2 were derived by a group of driving safety experts, definitions 3e5 were derived from a systematic review and analysis of definitions cited previously in the literature, and definition 6 was derived from the categorization of human failures observed as contributing factors in in-depth studies of crashes: 1. “Distraction involves a diversion of attention from driving, because the driver is temporarily focusing on an object, person, task or event not related to driving, which reduces the driver’s awareness, decision-making ability, and/or performance, leading to an increased 275

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risk of corrective actions, near-crashes, or crashes” (Hedlund, Simpson, & Mayhew, 2005, p. 2). “Diversion of attention away from activities required for safe driving due to some event, activity, object, or person, within or outside the vehicle” (Basacik & Stevens, 2008, p. 6). “Driver distraction is the diversion of attention away from activities critical for safe driving toward a competing activity” (Lee, Young, & Regan, 2008, p. 34). “Driver distraction: l Delay by the driver in the recognition of information necessary to safely maintain the lateral and longitudinal control of the vehicle (the driving task) (Impact) l Due to some event, activity, object, or person, within or outside the vehicle (Agent) l That compels or tends to induce the driver’s shifting attention away from fundamental driving tasks (Mechanism) l By compromising the driver’s auditory, biomechanical, cognitive, or visual faculties, or combinations thereof (Type)” (Pettitt, Burnett, & Stevens, 2005, p. 11). “A shift of attention away from stimuli critical to safe driving toward stimuli that are not related to safe driving” (Streff & Spradlin, 2000, p. 3). Driver distraction occurs “whenever a driver is delayed in the recognition of information needed to safely accomplish the driving task, because some event, activity, object, or person within [or outside] his vehicle, compelled or tended to induce the driver’s shifting of attention away from the driving task” (Treat, 1980, p. 21).

Despite some inconsistency among these definitions, there are some key elements that emerge which characterize driver distraction (Regan, Hallett, & Gordon, 2011; in press). For instance, driver distraction would seem to involve the diversion of attention away from driving, or away from activities critical for safe driving, toward a competing activity. This competing activity may come from a source inside or outside the vehicle, and it may be driving-related or non-driving-related. Furthermore, this competing activity may compel or induce the driver to divert attention toward it. Lastly, in thinking about what driver distraction is, there is an assumption, whether implicit or explicit, that when a driver is distracted, driving is negatively affected. For instance, Drews and Strayer (2008, p. 169) defined driver distraction as “any event or activity that negatively affects a driver’s ability to process information that is necessary to safely operate a vehicle.” It has been asserted in the literature that driver distraction is a subset of driver inattention (Ho & Spence, 2008; Senders, 2010; Victor, Engstrom, & Harbluk, 2008).

Key Problem Behaviors

In defining and understanding driver distraction, therefore, it is important to understand how it relates to driver inattention.

3. DRIVER INATTENTION: DEFINITION Driver inattention and driver distraction are related concepts. However, there is great diversity of thinking in the literature about the nature of the relationship between them. Few definitions of driver inattention exist, and those that do vary widely in meaning. The following is a small sample of definitions, drawn from the literature, that illustrates this point: l

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“. whenever a driver is delayed in the recognition of information needed to safely accomplish the driving task, because of having chosen to direct his attention elsewhere for some noncompelling reason” (Treat, 1980, p. 21). “. improper selection of information, either a lack of selection or the selection of irrelevant information” (Victor et al., 2008, p. 137). “. diminished attention to activities critical for safe driving in the absence of a competing activity” (Lee, Young, et al., 2008, p. 32). “. low vigilance due to loss of focus” (Talbot & Fagerlind, 2009, p. 4). “. when the driver’s mind has wandered from the driving task for some noncompelling reason. In this circumstance, the driver is typically focusing on internal thoughts (i.e., daydreaming, problem solving, worrying about family problems, etc.) and not focusing attention on the driving task” (Craft & Preslopsky, 2009, p. 3). “Driver distraction is a subset of driver inattention, a situation in which the primary task is performed without complete, focused attention for that driving task. Whereas inattention can also occur without a distracter just by no longer paying attention, distraction is related to something (a task, object, or person) that draws attention that is needed to perform the driving task adequately” (Schaap, van der Horst, van Arem, & Brookhuis, 2009, p. 3).

As can be seen, various definitions of driver distraction and driver inattention have been coined. Some researchers consider driver distraction and driver inattention to be distinct constructs (Caird & Dewar, 2007; Lee, Young, et al., 2008; Stutts et al., 2005), whereas others argue that driver distraction is a form of driver inattention (Klauer et al., 2006; Schaap et al., 2009; Victor et al., 2008) or that distraction can give rise to inattentive driving (Pettitt et al., 2005). Noteworthy is a tendency for researchers to code distraction and inattention as the same thing (Bunn, Slavova, Struttmann, & Browning, 2005).

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4. A MODEL OF DRIVER INATTENTION Regan et al. (2011, in press) have proposed a model of driver inattention that derives predominantly from the analysis of in-depth crash data (particularly the work of Van

Elslande and Fouquet (2007) and Treat (1980)) but also from other lines of thinking that emerge from the fields of human factors and cognitive psychology. This model is shown in Figure 20.1. Regan et al. define driver inattention

FIGURE 20.1 Model of driver inattention. Source: From Regan et al. (2011, in press).

Driver inattention

Driver restricted attention (DRA)

Driver misprioritized attention (DMPA)

Driver neglected attention (DNA)

Driver cursory attention (DCA)

Driver diverted attention (DDA)

Non-driving related (DDA-NDR) (i.e., between driving and non-driving-related activities)

Driving related (DDA-DR) (i.e., between drivingrelated activities)

Internal competing activities

● Task-unrelated thoughts: ● Internal/intentional ● Internal/ unintentional ● External/intentional ● External/unintentional ● Daydreams

● Task-related thoughts

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as “insufficient or no attention to activities critical for safe driving,” and they argue that driver inattention arises from (or can be induced by) the following mechanisms: l

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Driver restricted attention: “Insufficient or no attention to activities critical for safe driving brought about by something that physically prevents (due to biological factors) the driver from detecting (and hence from attending to) information critical for safe driving.” Driver misprioritized attention: “Insufficient or no attention to activities critical for safe driving brought about by the driver focusing attention on one aspect of driving to the exclusion of another, which is more critical for safe driving.” Driver neglected attention: “Insufficient or no attention to activities critical for safe driving brought about by the driver neglecting to attend to activities critical for safe driving.” Driver cursory attention: “Insufficient or no attention to activities critical for safe driving brought about by the driver giving cursory or hurried attention to activities critical for safe driving.” Driver diverted attention (DDA): “The diversion of attention away from activities critical for safe driving toward a competing activity, which may result in insufficient or no attention to activities critical for safe driving.” This category of inattention was further decomposed into the following subcategories of DDA: l

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DDA non-driving-related (DDA-NDR; between driving- and non-driving-related tasks): “The diversion of attention away from activities critical for safe driving toward a competing non-driving-related activity.” DDA driving-related (DDA-DR; between drivingrelated tasks): “The diversion of attention away from activities critical for safe driving toward a competing driving-related activity.”

Although labeled by Regan et al. (2011, in press) as “driver diverted attention,” the definition for this form of inattention is similar to that previously coined for driver distraction by Lee, Young, et al. (2008, p. 34) and carries the following assumptions: l

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It includes competing activities that can be driving and non-driving related. Driver engagement in competing activities can be selfinitiated or can occur involuntarily. Competing activities can derive from inside or outside the vehicle. Competing activities can include “internalized” sources of distraction, such as daydreaming and “task-unrelated thought” (Smallwood, Baracaia, Lowe, & Obonsawin, 2003).

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Engagement in the competing activity can give rise to interference that is “manifest” and observable (e.g., lane excursion) or “intrinsic” and unobservable (e.g., loss of situational awareness) (Hancock, Mouloua, & Senders, 2008).

The definition for DDA proposed by Regan et al. (2011, in press) differs from that proposed by Lee, Young, et al. (2008, p. 34; that is, “diminished attention to activities critical for safe driving in the absence of a competing activity”) in two important ways. First, Regan et al. refer to “insufficient or no attention” to activities critical for safe driving instead of “diminished” attention to activities critical for safe driving because the authors suggest that the term “diminished attention” does not include instances in which the driver gives full attention to an activity (or activities) that is not critical for safe driving. Second, the definition proposed by Regan et al. does not include the words “in the absence of a competing activity.” They argue that these additional words are necessary only if one is comparing driver distraction and driver inattention in isolation. However, as shown in Figure 20.1, a driver can become inattentive to driving without the presence of a competing activity. The following are examples of driver distraction and other forms of inattention that derive from the model proposed by Regan et al. (2011, in press): l

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Driver dozes off momentarily, with closed eyes, and almost hits a pedestrian crossing the street ahead (driver restricted attention). Driver looks over her shoulder for too long while merging and fails to see a lead vehicle rapidly braking (driver misprioritized attention). Driver neglects to scan to the left for approaching trains at a railway level crossing because he does not expect trains to be there (because they are rarely or never seen) (driver neglected attention). Driver in a hurry does not complete a full head check when merging onto a highway and collides with a merging car (driver cursory attention). Driver looks at cell phone while dialing a friend (driver diverted attention, non-driving-related). Driver looks at unexpected flashing fuel warning light (driver diverted attention, driving-related). Driver thinks about what needs to be done when she gets to work (driver diverted attention, non-driving-related). Driver thinks constantly about where to find nearest petrol station because the fuel tank is almost empty (driver diverted attention, driving-related). Driver daydreams about a romantic holiday in Paris (driver diverted attention, non-driving-related).

The model proposed by Regan et al. (2011, in press) assumes that it is not necessary for the driver to have control over the factors that give rise to inattention. Biological factors

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(e.g., when the eyes of a drowsy driver close), beyond the control of the driver, may make it difficult or impossible for a driver to attend to activities critical for safe driving. For this reason, they include the “driver restricted attention” category within the model. Regan et al. also consider the relationship between driver inattention and driver conditions (e.g., young or inexperienced) and between driver inattention and driver states (e.g., bored, tired, lacking vigilance, sleepy, fatigued, drunk, drugged, medicated, or emotionally upset). They argue that driver conditions and states are factors that can either (a) give rise to different forms of inattention (e.g., the young inexperienced driver who fails to effectively prioritize attention when timesharing between competing activities critical for safe driving [DMPA]; the tired driver who experiences moments of vision loss due to blinking [DRA]; ... or (b) moderate the impact of a given form of inattention when it is manifest (e.g., the young driver who, as a result of inexperience, is affected more by a competing activity [DDA] because he or she has less spare attentional capacity to devote to the competing activity).

Finally, within the model proposed by Regan et al., inattention can mean either insufficient attention or no attention to activities critical for safe driving. From the model, it is apparent that this depends on the form of inattention to which one is referring. Driver cursory attention, for example, can result in insufficient attention to safe driving. Driver restricted attention, on the other hand, may result in no attention to activities critical for safe driving.

5. SOURCES AND TYPES OF DISTRACTION In the previous discussion, a distinction was made between driver distraction and other forms of driver inattention. Driver distraction itself can be further decomposed into subcategories and can derive from various sources. Different sources of distraction, which can give rise to competing activity, have been identified in the literature (Regan, Young, Lee, & Gordon, 2008). These can be distilled into the following broad categories: l

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Objects (e.g., cell phone, advertising billboard, and apple) Events (e.g., a crash scene and lightning) Passengers Other road users (i.e., cyclists, pedestrians, other vehicles, etc.) Animals Internal stimuli (e.g., that trigger thoughts and the urge to cough or sneeze)

These sources of distraction will be distracting only if drivers interact with them or succumb to them, deliberately or involuntarily. For instance, an apple inside the vehicle is

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not necessarily a distracting object. However, if the driver is hungry and can see and/or smell the apple, this may motivate the driver to reach over for the apple and start eating it or to start thinking about how hungry he or she is. The same source of distraction can induce different types of distraction. An advertising billboard, for example, will induce visual distraction if the driver looks at it. If the driver thinks about the message that it conveys, this will generate internal distraction (i.e., internalized thought). Similarly, “use” of a cell phone can mean many thingsdlooking at it, using it to dial a number or send a text message, using it to read a text message, listening to it, etc. Each of these different modes of interaction will generate different types of distraction, individually or in combination, which will in turn generate different patterns of interference. Regan (2010) has distinguished among six different types of distraction, which differ according to the sensory modality via which the diversion of attention toward a competing activity is initiated: l

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Diversion of attention toward things we see (visual distraction) Diversion of attention toward things we hear (auditory distraction) Diversion of attention toward things we smell (olfactory distraction) Diversion of attention toward things we taste (e.g., a rotten apple; gustatory distraction) Diversion of attention toward things we feel (e.g., a spider on one’s leg; tactile distraction) Diversion of attention toward things we think about (internal distraction) (often referred to as “cognitive distraction”)

Tijerina (2000, p. 2), on the other hand, differentiates among three types of driver distraction that are relevant for traffic safety. First, he describes a general withdrawal of attention whereby the driver removes his or her eyes from the roadway (including eyelid closure due to fatigue). This type of distraction has the potential to degrade vehicle control (e.g., lane keeping and speed maintenance) and object and event detection depending on the period of time the driver spends not looking at the roadway. The second type of distraction, which is considered more dangerous than a general withdrawal of attention, is called the selective withdrawal of attention. Here, attention to thoughts, as opposed to eyes off the road, is thought to be the underlying mechanism involved. In this type of distraction, vehicle control is asserted to be barely affected; however, object and event detection can be degraded. Lastly, Tijerina coined the term “biomechanical interference,” which refers to the body shifts drivers make when they move from their neutral seated position or take their hands off the steering wheel. This includes instances in which the driver moves to reach over for a cell phone or to manipulate an in-vehicle

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device. This movement can delay or obstruct the driver from making a successful maneuver, such as a steering maneuver, and can degrade driving performance. Other researchers have delineated the following types of distraction: visual, cognitive, biomechanical, and auditory (Ranney, Mazzai, Garrott, & Goodman, 2000). However, this decomposition misses some of the potential types proposed by Regan (2010), described previously. In early 2007, the UK Department for Transport assembled experts to discuss the impact of driver distraction and noted how the different types of distraction suggested by Ranney et al. have mostly been studied in isolation (Basacik & Stevens, 2008). The experts agreed that these four types of distraction are not mutually exclusive and suggested that future studies should attempt to measure these different types of distraction inclusively (Basacik & Stevens, 2008). In a simulator-based experiment, Liang and Lee (2010) attempted to fill this void in the literature by investigating the effects of visual and cognitive distraction, in both isolation and combination. Cognitive distraction alone was found to make steering movements less smooth yet improved lane maintenance. The authors speculated that this improved lane maintenance was due to a combination of increased gaze concentration and a more cautious driving strategy, which led to an increase in drivers’ sensitivity to small changes in the driving environment ahead. In addition, both visual distraction and combined distraction impaired vehicle control and hazard detection and resulted in frequent, long off-road glances. Importantly, visual distraction alone was found to be more detrimental to driving performance overall than combined distraction. What Tijerina (2000) calls “biomechanical interference” is not what we would regard as driver distraction. If the driver, while driving, reaches with his right hand to get a cell phone out of the glove box, and thus diverts his attention (and perhaps vision) to the item he is retrieving, this is driver diverted attention (non-driving-related) as defined in this chapter. The biomechanical interferencedfor instance, steering the vehicle in the direction of the glove boxdwhich may result from this diversion of attention is not of itself distraction; rather, it is a consequence of driver diverted attention.

6. MODERATING FACTORS Whether distraction, when it occurs, impacts driving performance and safety depends on four main factors (Young, Regan, & Lee, 2008): Driver characteristics, driving task demand, competing task demand, and the ability of the driver to self-regulate in response to the competing activity. Driver characteristics include age, gender, driving experience, driver state (e.g., drowsy, drunk, angry, and

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upset), familiarity with and amount of practice on the competing task, personality (e.g., risk taking and succumbing to peer pressure), and one’s vulnerability to distraction (Young et al., 2008). An inexperienced driver, for example, will have less attention available to divert to a competing task than an experienced driver who, through practice and experience, has learned to automate many driving subtasks and hence requires less attention to perform them. For instance, Shinar, Meir, and Ben-Shoham (1998) conducted an on-road study investigating driving performance in manual and automatic transmission vehicles for inexperienced and experienced drivers. Their study revealed that for inexperienced drivers, manual gear shifting significantly impaired sign detection performance compared with using an automatic transmission. In contrast, there were no differences in performance between the two transmission types for experienced drivers. These results suggest that for young drivers, manual gear shifting was an attention demanding task that may impact other aspects of driving performance. Factors that influence driving task demand include traffic conditions, weather conditions, road conditions, the number and type of vehicle occupants, the ergonomic quality of cockpit design, and vehicle speed (Young, et al., 2008). Generally, the lower the demand of driving, the greater will be the residual attention available to attend to competing activities. A well ergonomically designed vehicle cockpit, for example, which minimizes workload, will give the driver more capacity to attend to competing tasks and hence reduce interference among the tasks. Given, however, that modern driving does not require complete and continuous attention to maintain safe driving performancedthat it is a “satisficing task” (Hancock et al., 2008, p. 12)dthe often low demands of driving may encourage drivers to allocate attention to information irrelevant to safe driving. The demands of the competing task will have a critical bearing on the degree of interference it brings about (Young et al., 2008). Factors that influence competing task demand include how similar it is to driving subtasks (e.g., whether it requires vision or control actions similar to those required to control the car), its complexity, whether it can be ignored, how predictable it is, how easily it can be adjusted, how easily performance of it can be interrupted and resumed, and how long it takes to perform. The longer a driver is exposed to a source of distraction that interferes with safe driving, the greater will be its impact. Finally, the ability of the driver to self-regulate his or her behavior in the face of a competing activity (i.e., to compensate for its adverse effects) will have a critical bearing on whether it distracts the driver (Young et al., 2008). Self-regulation at the strategic, tactical, and operational levels of driving control can be exercised by drivers to control exposure to competing activities, to regulate the

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timing of the engagement, and to control resource investment (Lee, Regan, & Young, 2008). There are times, however, when self-regulation might not be possible, even though it is what the driver wants. Social and business imperatives that require a driver to use a cell phone at times when the driver would not otherwise choose to do so is such an example. Nelson, Atchley, and Little (2009), for example, found that drivers will receive an incoming call or initiate a call if they believe that their conversation is important, even if they believe that there may be an associated risk with conversing on a cell phone while driving. Although these factors moderate the effects of distraction, they are rarely controlled for in experimental studies. However, they are important independent variables in any study of driver distraction. This makes it difficult, and often impossible, to compare across studies the impact of different sources of distraction on behavior and performance.

7. INTERFERENCE AND THEORIES OF INTERFERENCE If a driver is distracted, performance of the competing task will interfere in some way with driving. This interference can be minimal or significant. The four moderating factors, described previously, can be seen as regulating the amount of interference between the competing task and activities critical for safe driving. The effects of the interference, as noted, may be manifest and observable (as in a lane excursion) or intrinsic and unobservable (as in a loss of situation awareness) (Hancock et al., 2008). Currently, little is known about intrinsic interference, but one can imagine that it can lead to errors in the minds of drivers at different stages of information processing (from perception to action, which may or may not result in manifest interference; W. J. Horrey, personal communication, May 2010). Distraction is a problem for drivers because their ability to divide attention between competing tasks is fundamentally limited by their biology. Psychological accounts of the mechanisms that give rise to this interference vary and include multiple resource theory, single channel theory, control theory, and action-oriented models of attention selection and task interference in driving. Multiple resource theories of attention (Wickens, 1992) propose that a competing activity will interfere with tasks critical for safe driving if the two activities share certain properties: l

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Share common processing stages (e.g., if both activities are perceptual rather than one being primarily perceptual and the other being primarily cognitive) Share common modalities of output (e.g., if both activities are manual as opposed to one activity being manual and the other being vocal) Share common “visual channels” (e.g., if two activities are focal rather than one activity being focal and the other ambient). Are mutually demanding

According to this class of theory, attention can be divided between competing tasks provided that they are sufficiently different from one another in their structural characteristics and do not demand more attention than is available. Single channel theories (Broadbent, 1958; Welford, 1967) imply that attention cannot be divided between competing tasks. If two tasks compete for attention at the same time, or very close in time, they must be performed one at a time. Simultaneous performance of the tasks can only be accomplished by rapid switching of attention between them. According to this theory, competing activity will interfere more with tasks critical for safe driving under the following conditions (not all of which are mutually exclusive): l

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If the two activities share the same stage of information processing (e.g., response selection) If they cannot be interdigitated (if aspects of one activity cannot be accomplished during time gaps left vacant by the other) If they cannot be coordinated in time (e.g., when one rubs one’s stomach while patting one’s head) If information from the competing task cannot be “chunked” into smaller units of information If the competing task is high in task demand If the competing task is unpredictable If the competing task is unpracticed

Control theory asserts that drivers actively control the level of distraction they experience. This control is assumed to occur at three levels of driving control (strategic, tactical, and operational), each with different time horizons, and is achieved by three types of control (feedback, feed-forward, and adaptive) (Lee, Regan, et al., 2008). The limits of control at each level, and the interactions between failures at these levels cause distraction-related mishaps. According to this class of theory, the key mechanisms that mediate the degree of interference between driving and competing tasks are the “ignorability,” “predictability,” “interruptibility,” and “adjustability” of the task(s) that compete for the driver’s attention (Lee, Regan, et al., 2008). These task attributes are similar to those proposed by advocates of the single channel hypothesis to explain the ability of people to “do two things at once.”

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Engstrom, Markkula, and Victor (2009) argue that traditional models of attention are unable to provide a unified account of the different bottom-up and top-down factors that govern attention selection and task interference in real-world driving situations. They present an alternative, action-oriented, model of attention selection and task interference in driving that they believe better accounts for “proactive, goal- and context-driven attention allocation in the real world” (p. 5). Their proposed model contains three key components: “(1) Sensory and effector systems interacting with the environment; (2) competing and cooperating schemata, implementing routine actions and action patterns; and (3) supervisory control, which may be used to bias the schemata when demanded by the task at hand” (p. 5). Based on this conceptual model, they propose three general types of task interference, related to the three model components: (1) interference between sensory and/or effector systems, (2) cross-talk interference among schemata, and (3) competition for supervisory top-down bias. Although it is beyond the scope of this chapter to review this model in detail, it provides an important complementary perspective to that proposed by Lee, Regan, et al. (2008) on the manner in which drivers self-regulate in response to distraction and on the factors that govern attention selection and task interference in real-world driving.

8. IMPACT ON DRIVING PERFORMANCE Having defined driver distraction, and the mechanisms that give rise to interference when a driver is distracted, it is appropriate to consider the impact that this interference may have on driving performance. Various driving performance deficits have been reported for different competing activities using laboratory tests, driving simulators, and instrumented vehicles on test tracks. Reported deficits vary and include degraded lane keeping, degraded speed control, increased reaction time, missed traffic signals, shorter or longer intervehicle following distances, unsafe gap acceptances, reduced situation awareness, poorer visual scanning, reduced horizontal field of view, and missed checks (e.g., mirror checks) (Bayly, Young, & Regan, 2008; Horberry & Edquist, 2008). The nature and magnitude of the performance deficits that arise depend on the moderating factors already described (i.e., driver characteristics, driving task demand, competing task demand, and the ability of the driver to self-regulate in response to the competing activity). Certain characteristics of the competing task are particularly important in this respect (Victor et al., 2008). Tasks that are primarily visually distracting and hence take the eyes (and, to a lesser extent, the mind) off the road tend to degrade to a greater extent lane keeping performance and event detection. Tasks that primarily take the mind off the road (e.g., a complex cell phone conversation using a hands-free device) tend to

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increase road-center viewing time, spatially concentrate gaze on the forward road center at the expense of peripheral glances, and may sometimes even improve lane keeping performance. Generally, delays in event detection are greater for tasks that are visually distracting than for those that are primarily cognitively demanding (Victor et al., 2008). Driving performance deficits have been reported in the presence of competing activities deriving from use of cell phones, iPods, DVD players, navigation systems, e-mail systems, radios, and CD players. Driving performance deficits have also been reported for drivers who engage in everyday activities such as eating, drinking, smoking, reading, writing, reaching for objects, grooming themselves, and interacting with passengers (Bayly et al., 2008). There are certain difficulties in making sense of the performance deficits deriving from these kinds of studies (Ranney, 2008; Regan, 2010). First, it is difficult to rank competing activities according to how relatively more or less distracting they are, given the considerable variability across studies in methods, measures, and competing tasks investigated. Second, within studies, it is difficult to judge whether a driving performance deficit yielded for a given competing activity is acceptable. This is because there is currently no agreement on what is an “acceptable” level of driving performance degradation deriving from the performance of any given competing activity. This is exacerbated by the fact that there is currently no agreement within the road safety community about what are “activities critical for safe driving.” Third, the presence and magnitude of any performance decrement depends critically on the different moderating factors discussed previously, particularly the amount of freedom the driver has to interact in a natural way with the competing task(s). As noted previously, advocates of control theory assert that drivers actively control the mechanisms that give rise to the distraction they experience. Hence, forcing a participant to interact with a cell phone in an experimental setting in a manner that he or she would not adhere to in the real world may produce a performance deficit in the laboratory that, in reality, might not occur on the road. Perhaps the greatest difficulty in interpreting driving performance deficits, however, is in knowing to what extent a given reduction in driving performance (e.g., a 20% increase in lateral lane excursions) translates into increased crash risk. Valid algorithms for linking the two remain to be developed. Until these and other related issues are resolved, policy makers cannot be blamed for relying on crash and critical incident data to guide countermeasure development (Regan, 2010).

9. IMPACT ON SAFETY It is beyond the scope of this chapter to review in detail all of the literature pertaining to the impact of driver distraction and driver inattention on driver safety. Other reference

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sources exist for this purpose (Ranney, 2008; Regan et al., 2008; Young & Regan, 2007). A few key points are made here. Gordon (2008) reviewed a number of studiesdin the United States and New Zealanddthat used police reports or findings from crash investigation teams to provide information on a wide range of inside- and outside-thevehicle distractions believed to have contributed to crashes. The studies reviewed by Gordon consistently identified driver distraction as a contributing factor in 10e12% of crashes, and approximately one-fifth of these crashes involved driver interaction with technology. It must be noted, however, that the role of driver distraction in crashes depends critically on how broadly distraction is defined (Gordon, 2008). For instance, using the same crash database, Stutts et al. (2001) and Wang, Knipling, and Goodman (1996) estimated different percentages of crashes in which distraction was a factor. Stutts et al., when coding distraction separately from driver inattention, concluded that 8.3% of crashes were the result of driver distraction, whereas when Wang et al. coded driver distraction and driver inattention separately, driver distraction was found to be involved in 13% of crashes. Thus, depending on how driver distraction and driver inattention are defined, the role of these factors involved in crashes differs. One thing that is clear, however, is that it is very likely that police-reported data underestimate the true magnitude of the distraction problem (Gordon, 2008). Data from “naturalistic driving studies” (Klauer et al., 2006; Olson et al., 2009) present a more accurate picture of the role of distraction and inattention in crashes and incidents. In such studies, instrumented vehicles, equipped with video cameras and other sensors, are used to record driver and driving behaviors continuously over periods of weeks, months, and even years. Episodes of driver distraction, which are observable on video, can then be identified, characterized, and counted. These studies suggest that up to 22% of car crashes and 71% of truck crashes involve as a contributing factor distraction from non-driving-related activities (Klauer et al., 2006; Olson et al., 2009). Epidemiological studies enable researchers to calculate estimates of increased risk. Such studies, for instance, have investigated the associated crash risk of engaging in cell phone “use” (mostly conversing via a handheld or handsfree device) while driving. These studies have discovered that using a cell phone while driving increases the risk of being involved in a collision or fatality by between four and nine times (McEvoy et al., 2005; Redelmeier & Tibshirani, 1997; Violanti, 1998; Violanti & Marshall, 1996). In addition, the mere presence of a cell phone inside the vehicle has been shown to increase the chances of being involved in a vehicle collision fourfold (Redelmeier &

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Tibshirani, 1997) and to double the risk of a fatality occurring (Violanti, 1998). The data that emerge from naturalistic driving studies that also utilize epidemiological methods provide the most insightful indications of changes in risk and exposure associated with driver engagement in different distracting activities. Olson et al. (2009), from the Virginia Tech Transportation Institute, investigated the prevalence of driver distraction in 4452 safety-critical events (defined as crashes, near-crashes, crash-relevant conflicts (i.e., less severe near-crashes), and unintentional lane deviation) involving commercial trucks instrumented with video and other vehicle sensors and recording devices. Safety-critical events were recorded in a data set that included 203 drivers and 3 million miles (approximately 4,828,000 km) of data. Truck drivers were found to be engaged in “tertiary” (i.e., non-driving-related) activities in 71% of crashes, 46% of near-crashes, and 60% of all safety-critical events. Drivers were more likely to be involved in a safety-critical event while performing the following activities: text messaging (23 times more likely), using a dispatching device (9.9 times), writing (9 times), using a calculator (8.2 times), looking at a map (7 times), reaching for an electronic device (6.7 times), dialing a handheld cell phone (5.9 times), personal grooming (4.5 times), and reading (4 times). Overall, tasks that drew the driver’s eyes away from the forward roadway had the highest risk of a safety-critical event occurring. Noteworthy in this study is that talking or listening on a hands-free phone did not significantly increase the risk of being involved in a safety-critical event. In fact, using a CB radio or talking or listening on a handsfree phone was found to reduce riskdthat is, it had a protective effect. This is a controversial finding that is at odds with previous epidemiological studies. One must be cautious, however, when comparing the distractive effects of such activities across different driving populations. For instance, truck drivers drive for long periods of time in one shift, often during the night, and therefore it is likely that these drivers engage in distracting activities to keep them alert. Text messaging, although it had a very high risk estimate, was a task performed infrequently by truck drivers. However, as texting while driving a truck becomes a more prevalent activity, the frequency of safety-critical events is likely to increase, and so too will risk. A precursor and seminal study by Klauer et al. (2006) involved 100 instrumented cars and 241 drivers. They collected 2 million vehicle miles (approximately 3,218,600 km), or 43,000 h, of data over a 12- to 13-month period. Inattention was found to be a contributing factor in 78% of crashes and 65% of near-crashes. Distraction (defined as driver engagement in non-driving-related activities) was a factor in 22% of crashes. In this study, drivers were more likely to be involved in a crash or near-crash while performing the following activities: reaching for a moving

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object (8.8 times more likely), looking at an external object (3.7 times), reading (3.4 times), applying makeup (3.1 times), dialing a handheld device (2.8 times), and listening to conversation on a handheld cell phone (1.3 times) (although, interestingly, the latter increase was not significantly different from 1.0, suggesting that there is no increased risk associated with listening to a conversation on a handheld cell phone). The naturalistic driving study is still a relatively new research method, and the limitations of the data that derive from such studies must be understood (see Chapter 6). McEvoy and Stevenson (2008), for example, have highlighted certain limitations pertaining to Klauer et al.’s (2006) study: The relatively small, nonrepresentative, volunteer sample; the difficulty in reliably capturing some types of secondary distracting tasks, such as drivers‘ level of cognitive attention, the role of passengers (for privacy reasons), and some outside distractions; issues with inter-rater reliability in coding distracting activities and assigning fault for crashes and near-crashes; and a lack of data on the role of driver distractions in more serious crashes resulting in driver injury. (p. 316)

A common criticism of naturalistic driving studies is that the outcome measures are almost entirely critical incidents. Very few crashes, for example, occurred in the two studies described previously, and for those that did, most were minor. It is currently unknown whether the increased risk associated with a distraction-related critical incident in which a crash was avoidable is comparable to that for a critical incident in which the crash was unavoidable. This is an important empirical issue that remains to be resolved. A much larger naturalistic driving study currently underway in the United States under the auspices of the Transport Research Board (involving more than 3000 volunteer drivers; see www.TRB.org/SHRP2), as part of the second U.S. Strategic Highway Research Program (SHRP2), is designed to overcome these kinds of limitations. It is clear that regardless of how broadly driver distraction is defined, it plays a significant contributing role to collisions, near-crashes, and crash-related conflicts.

10. MANAGING DISTRACTION It is not possible to eliminate distraction or driver inattention. At most, it can be well managed. Regan, Young, et al. (2008) have estimated that 55% of all known sources of distraction are avoidable (61% of sources from within the vehicle and 31% of sources outside the vehicle), implying that there is ample scope for countermeasure development. Countermeasure development for distraction is still in its infancy, even in countries such as Sweden with relatively good safety records. This is not surprising because systems

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for accurately and reliably collecting and analyzing data on the role of distraction (and inattention) in crashes to inform countermeasure development do not exist in most countries. Regan, Lee, et al. (2008) have recommended numerous countermeasures for preventing distraction or mitigating its effects, under each of the following general categories: data collection, education, company car fleet management, legislation, enforcement, driver licensing, road and traffic design, driver training, and vehicle design. Ultimately, the goal of the road safety community should be to design a distraction-tolerant road system in which no one involved in a distraction-related crash is killed or seriously injured (Tingvall, Eckstein, & Hammer, 2008). This requires countermeasures that support drivers at all stages of the crash sequencedthat support them, for example, to drive normally (e.g., appropriate training and education and intelligent speed adaptation); to warn them if they deviate from normal driving (e.g., real-time distraction warnings and rumble strips on the sides of freeways); to support them in emerging situations (e.g., lane keeping assist); to help them, and the car, avoid a crash (e.g., automatic brake assist); and, where a crash is unavoidable, to ensure that the speed of the vehicle and the legal speed limit are in accordance with the capacity of the vehicle and the infrastructure to protect vehicles and their occupants. Real-time, vehicle-based, distraction countermeasures have perhaps the greatest potential to manage distraction (Regan, Lee, et al., 2008). They can adaptively prevent or limit driver exposure to competing activities when the concurrent demands of driving are estimated to be high (realtime distraction prevention; e.g., “workload managers”), and they can mitigate the effects of distraction once it occurs by providing feedback and warnings to drivers that redirect their attention back to relevant aspects of the driving task (real-time distraction mitigation; e.g., “distraction warning systems”) (Victor et al., 2008). These systems can detect whether a driver is distracted, regardless of the competing activity (driving- or non-driving-related); regardless of whether driver engagement in the competing activity is voluntary or involuntary; regardless of whether the competing activity derives from inside or outside the vehicle; and regardless of whether the distraction is visual, internal, or of some other type (e.g., auditory) (Regan, Young, et al., 2008). Furthermore, these systems can be optimized so that they are adaptive to factors that moderate the effects of distraction (e.g., driver state) by, for example, issuing earlier warnings if the driver is drunk. Systems can also be used to prime and activate the operation of other active and passive safety systems at different stages of the crash chain to optimize driver safety during all stages of the crash sequence. Through the provision of real-time feedback to drivers, these systems can also serve to train drivers automatically to know when they are distracted.

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Driver Distraction

11. CONCLUSION This chapter provided a general overview of the terms “driver distraction” and “driver inattention.” In particular, the chapter attempted to characterize driver distraction, to distinguish it from other forms of driver inattention, and to characterize its effects. Although major scientific advances in our understanding of distraction and its effects have been made during the past decade, there remain many unresolved issues, some of which have been alluded to in this chapter. One of the most difficult challenges for the traffic psychologist is to convince policy makers in some jurisdictions that driver distraction is a significant enough road safety problem to warrant countermeasure development. There is plenty of converging evidence from well-designed studiesdundertaken in simulators, on test tracks, and on real roads (e.g., in naturalistic driving studies)dthat interference generated by the diversion of attention away from activities critical for safe driving toward a competing activity can degrade driving performance and increase crash risk. However, many sources of distraction that are known from these studies to degrade performance and increase crash risk fail to materialize as contributing factors in crash data. Why this is so is perhaps the most pressing issue to resolve in the immediate future. Until it is resolved, there will remain policy makers who refuse to believe that driver distraction and inattention more generally are road safety problems. For example, in Sweden, which has one of the best road safety records in the world, there is currently no ban on the use of either handheld or hands-free cell phones for any functional purpose. We must ask ourselves: Why is this so?

ACKNOWLEDGMENT We thank Dr. Craig Gordon, from the Alcohol Advisory Council of New Zealand, for his insightful comments on an earlier version of the manuscript.

REFERENCES Basacik, D., & Stevens, A. (2008). Scoping study of driver distraction. (Road Safety Research Report No. 95). London: Department for Transport. Bayly, M., Young, K. L., & Regan, M. A. (2008). Sources of distraction inside the vehicle and their effects on driving performance. In M. A. Regan, J. D. Lee, & K. L. Young (Eds.), Driver distraction: Theory, effects, and mitigation (pp. 191e213). Boca Raton, FL: CRC Press. Broadbent, D. E. (1958). Perception and communication. London: Academic Press. Brown, I. (1986). Functional requirements of driving. Paper presented at the Berzelius Symposium on Cars and Causalities, Stockholm, Sweden. Unpublished manuscript. Bunn, T. L., Slavova, S., Struttmann, T. W., & Browning, S. R. (2005). Sleepiness/fatigue and distraction/inattention as factors for fatal

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Driver distraction: Theory, effects, and mitigation (pp. 305e318). Boca Raton, FL: CRC Press. McEvoy, S. P., Stevenson, M. R., McCartt, A. T., Woodward, M., Haworth, C., Palamara, P., et al. (2005). Role of mobile phones in motor vehicle crashes resulting in hospital attendance: A caseecrossover study. British Medical Journal, 331(7514), 428. Nelson, E., Atchley, P., & Little, T. D. (2009). The effects of perception of risk and importance of answering and initiating a cellular phone call while driving. Accident Analysis and Prevention, 41(3), 438e444. Olson, R. L., Hanowski, R. J., Hickman, J. S., & Bocanegra, J. (2009). Driver distraction in commercial vehicle operations. (Report No. FMCSA-RRR-09-042). Washington, DC: U.S. Department of Transportation. Pettitt, M., Burnett, G., & Stevens, A. (2005). Defining driver distraction. In Proceedings of the 12th ITS World Congress. San Francisco: ITS America. Ranney, T. A. (2008). Driver distraction: A review of the current state-ofknowledge. (Report No. DOT HS 810 787). Washington, DC: National Highway Traffic Safety Administration. Ranney, T. A., Mazzai, E., Garrott, R., & Goodman, M. J. (2000). NHTSA driver distraction research: Past, present, and future. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration. Redelmeier, D. A., & Tibshirani, R. J. (1997). Association between cellular-telephone calls and motor vehicle collisions. New England Journal of Medicine, 336(7), 453e458. Regan, M. A. (2010, January). Driven by distraction. Vision Zero International 4e12. Regan, M.A., Hallett, C., & Gordon, C. P. (2011, in press). Driver distraction and driver inattention: Definition, relationship, and taxonomy. Accident Analysis and Prevention. Regan, M. A., Lee, J. D., & Young, K. L. (2008). Driver distraction: Theory, effects and mitigation. Boca Raton, FL: CRC Press. Regan, M.A., & Victor, T. (Eds.). (2009). Electronic proceedings of the First International Conference on Driver Distraction and Inattention, Gothenburg, Sweden, September 28e29, 2009. http://www.chalmers. se/safer/driverdistraction-en. Regan, M. A., Young, K. L., Lee, J. D., & Gordon, C. P. (2008). Sources of distraction. In M. A. Regan, J. D. Lee, & K. L. Young (Eds.), Driver distraction: Theory, effects and mitigation (pp. 191e214). Boca Raton, FL: CRC Press. Schaap, T. W., van der Horst, A. R. A., van Arem, B., & Brookhuis, K. A. (2009, September 28e29). The relationship between driver distraction and mental workload. Paper presented at the First International Conference on Driver Distraction and Inattention, Gothenburg, Sweden. http://document.chalmers.se/doc/589106931. Senders, J. W. (2010). Driver distraction and inattention: A queuing theory approach. Paper presented at the First International Conference on Driver Distraction and Inattention, Gothenburg, Sweden. http://document.chalmers.se/doc/589106931. Shinar, D., Meir, M., & Ben-Shoham, I. (1998). How automatic is manual gear shifting? Human Factors, 40(4), 647e654. Smallwood, J., Baracaia, S. F., Lowe, M., & Obonsawin, M. (2003). Task unrelated thought whilst encoding information. Consciousness & Cognition, 12(3), 452e484.

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Streff, F., & Spradlin, H. (2000). Driver distraction, aggression, and fatigue: A synthesis of the literature and guidelines for Michigan planning. Ann Arbor: University of Michigan Transportation Research Institute. Stutts, J. C., Feaganes, J., Reinfurt, D., Rodgman, E., Hamlett, C., Gish, K., et al. (2005). Driver’s exposure to distractions in their natural driving environment. Accident Analysis and Prevention, 37(6), 1093e1101. Stutts, J. C., Reinfurt, D. W., Staplin, L., & Rodgman, E. A. (2001). The role of driver distraction in traffic crashes. (Report No. 202/6385944). Washington, DC: AAA Foundation for Traffic Safety. Talbot, R., & Fagerlind, H. (2009). Exploring inattention and distraction in the SafetyNet accident causation database. Paper presented at the First International Conference on Driver Distraction and Inattention, Gothenburg, Sweden, September 28e29. http://document.chalmers. se/doc/589106931. Tijerina, L. (2000). Issues in the evaluation of driver distraction associated with in-vehicle information and telecommunications systems. Washington, DC: National Highway Traffic Safety Administration. Tingvall, C., Eckstein, L., & Hammer, M. (2008). Government and industry perspectives on driver distraction. In M. A. Regan, J. D. Lee, & K. L. Young (Eds.), Driver distraction: Theory, effects, and mitigation (pp. 603e618). Boca Raton, FL: CRC Press. Treat, J. R. (1980). A study of precrash factors involved in traffic accidents. HSRI Research Review, 10, 1e35. Van Elslande, P., & Fouquet, K. (2007). Analyzing “human functional failures” in road accidents: Final report (Deliverable D5.1, WP5 “Human Factors”). TRACE European project. Victor, T. W., Engstrom, J., & Harbluk, J. L. Y. (2008). Distraction assessment methods based on visual behaviour and event detection. In M. A. Regan, J. D. Lee, & K. L. Young (Eds.), Driver distraction: Theory, effects, and mitigation (pp. 135e165). Boca Raton, FL: CRC Press. Violanti, J. M. (1998). Cellular phones and fatal traffic collisions. Accident Analysis and Prevention, 30(4), 519e524. Violanti, J. M., & Marshall, J. R. (1996). Cellular phones and traffic accidents: An epidemiological approach. Accident Analysis and Prevention, 28(2), 265e270. Wang, J.-S., Knipling, R.R., & Goodman, M. J. (1996). The role of driving inattention in crashes: New statistics from the 1995 Crashworthiness Data System. Paper presented at the 40th Annual Proceedings of the Association for the Advancement of Automotive Medicine, Vancouver, British Columbia, Canada. Welford, A. T. (1967). Single-channel operation in the brain. In A. F. Sanders (Ed.), Attention and performance I (pp. 5e22). Amsterdam: North-Holland. Wickens, C. D. (1992). Engineering psychology and human performance (Vol. 2). New York: HarperCollins. Young, K., & Regan, M. (2007). Driver distraction: A review of the literature. In I. J. Faulks, M. Regan, M. Stevenson, J. Brown, A. Porter, & J. D. Irwin (Eds.), Distracted driving (pp. 379e405). Sydney: Australasian College of Road Safety. Young, K. L., Regan, M. A., & Lee, J. D. (2008). Factors moderating the impact of distraction on driving performance and safety. In M. A. Regan, J. D. Lee, & K. L. Young (Eds.), Driver distraction: Theory, effects, and mitigation (pp. 335e352). Boca Raton, FL: CRC Press.

Chapter 21

Driver Fatigue Jennifer F. May Old Dominion University, Norfolk, VA, USA

1. INTRODUCTION Fatigue in the context of driving is considered a psychological/mental type of fatigue (as opposed to physical/ muscle fatigue) characterized by subjective feelings of a disinclination to continue driving, sleepiness, weariness, and reduced motivation (Desmond & Hancock, 2001; Johns, 2000; Shen, Barbera, & Shapiro, 2006). Lal and Craig (2001a) additionally define fatigue as the transition from wake to sleep. If fatigue is allowed to continue, it may lead to episodes of sleep (Lal & Craig, 2001a). Subjective sleepiness, a symptom of fatigue, is defined by the feelings and physical symptoms related to the desire or need to sleep, such as yawning, head nodding, and eye drooping, during this transitional period between wake and sleep (Shen et al., 2006, p. 64). Physiologically, fatigue produces changes in brain activity and a reduction in heartrate and eye movements (Lal & Craig, 2001a). Driver fatigue can result in cognitive and psychomotor performance impairments such as increased weaving and reaction time (George, 2003; Jewett, Dijk, Kronauer, & Dinges, 1999), which can lead to crashes (Lal & Craig, 2001a). Unfortunately, because of the variability in driver susceptibility to fatigue and how symptoms of fatigue can vary among individuals, there are no firm numbers or standard measurements when trying to objectively define fatigue (Desmond & Hancock, 2001; Soames-Job & Dalziel, 2001). There are multiple causes to driver fatigue. Environmental factors such as trip duration, time of day, and roadway/weather conditions can impact fatigue. This is considered task-related fatigue. The quality and quantity of sleep will also impact driver fatigue. This is considered sleep-related fatigue and also known as “drowsy driving.” Task-related and sleep-related driver fatigue can interact and compound the feelings of fatigue and the subsequent performance decrements. Driver fatigue is a hazard to which any driver is potentially susceptible, and it is especially dangerous because drivers do not view it as a hazardous condition and often do not realize how sleepy or fatigued they are (Reyner Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10021-9 Copyright Ó 2011 Elsevier Inc. All rights reserved.

& Horne, 1998a). A survey indicated that although most drivers experience symptoms of sleepiness (yawning and difficulties keeping eyes open), the problem appears to be that drivers do not take these symptoms seriously (Nordbakke & Sagberg, 2007). This same survey also found that most drivers continue to drive even when they recognize they are sleepy or feel too tired to drive. Adding to the complexity of drowsy driving is the variability in how sleepiness affects driving performance. Major theories of sleepiness focus on sleep duration and circadian rhythms as factors contributing to sleepiness and performance decrements. This chapter reviews current crash statistics and survey results to highlight the impact and prevalence of driver fatigue. It reviews causes of driver fatigue, and these are divided into task-related fatigue and sleep-related fatigue. At-risk populations are identified. Finally, countermeasures to fatigue are summarized.

2. CRASH STATISTICS AND NATIONAL SURVEYS The reported number of fatalities due to fatigue has remained relatively stable throughout the years. Knipling and Wang (1994) analyzed data from the Fatal Accident Reporting System and the General Estimates System for police-reported crashes occurring from 1989 to 1993 and found that an annual average of 56,000 crashes resulting in 40,000 nonfatal injuries and 1357 fatalities were attributed to drowsiness. The early edition report of 2009 crash statistics from the National Highway Traffic Safety Administration (NHTSA) attributed 1202 fatalities (2.7% of total fatalities) to fatigue, sleepiness, and illness (NHTSA, 2011). The cost of sleep-related crashes is estimated at $12.5 billion per year (National Sleep Foundation, n.d.). These statistics may underestimate the problem because unlike alcohol impairment detection, there are currently no standardized procedures for the police to detect fatigue or sleepiness, and as such, sleep-related crashes are often 287

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attributed to other factors, such as inattention. In the State of the States Report on Drowsy Driving (National Sleep Foundation, 2007), only 9 out of 27 responding states reported training police officers on how fatigue impacts driving performance. This report gave specifics for each state in how each deals with sleepy driving. In Virginia, for example, a driver can be charged with reckless driving and manslaughter in the event of a fatality resulting from drowsy driving or sleep-related crash. Virginia has provisions limiting a driver’s right to drive based on medical conditions such as seizure disorder, but sleep disorders are not mentioned. On the Virginia police report, driver fatigue is listed as a check-box option under Driver Distractions, and in the Condition of Driver section, the officer can choose Driver Fatigued or Apparently Asleep. However, there is no training for police on the impact of fatigue and sleepiness on driving performance. The state does mandate that sleep and drowsy driving is included in the driver education curricula. In the 2009 Sleep in America Poll (National Sleep Foundation, 2009), 28% of drivers admitted to falling asleep or “nodding off” while driving at least once per month during the year. One percent of drivers participating in the survey admitted to having an accident or near-crash due to sleepiness within the past year. Compared to participants who admitted to drowsy driving less frequently, drowsy drivers were more likely to admit that their sleep needs were not being met, they slept less than 6 h at night during the workweek, they used a “sleep aid,” and they experienced symptoms of insomnia and snored. According to results from the 2003 Omnibus Sleep in America Poll (National Sleep Foundation, 2003), 60% of adults aged 18e54 years reported feeling drowsy while driving at least once during that year. The results of this poll indicate that drowsy driving is more prevalent than what the crash statistics show. This makes sense given that the latest NHTSA crash statistics report that there is one automobile crash per 479,293 vehicle miles traveled (NHTSA, 2006). This indicates that crashes are statistically infrequent relative to an individual driver regardless of sleep influence; however, crash statistics do not take into account unreported crashes or close calls. Results of a naturalistic driving study showed that drowsy driving increases nearcrash or crash risk by four to six times that of alert driving (Klauer, Dinges, Neale, Sudweeks, & Ramsey, 2006). Sleep-related crashes are usually reported as such because either the driver admitted to falling asleep or the crash characteristics were typical of a sleep-related crash. According to George (2005), sleep-related crashes are typically more severe, driver-only, off-road crashes with no skid marks or evidence of an attempt to prevent the crash. Smith, Cook, Olson, Reading, and Dean (2004) analyzed trends of behavioral risk factors in hospitalizations and fatalities due to car crashes in Utah. They found that

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Key Problem Behaviors

fatigued drivers were approximately two times more likely to be hospitalized or die following the car crash. Driving simulators have provided researchers with a safe way to study driver fatigue beyond crash statistics and self-report. Researchers can manipulate levels of sleepiness and driver fatigue in a controlled environment to safely test their impact on driving performance without the risk of crash or injury. Driving scenarios created to examine the effects of sleepiness are typically long (30 or more minutes), monotonous highway conditions with few passing cars and slight curves. This type of scenario can be thought of as a vigilance task, which lends itself to the unmasking of fatigue and sleepiness (Thiffault & Bergeron, 2003). George (2003) stated that steering wheel movements and lane position variability are the most commonly used measures of driving performance, and that both measures are sensitive to long periods of driving and circadian rhythm effects. Line crossings, speeding, crashes, and reaction times are also performance measures sensitive to driver fatigue. As demonstrated in the studies reviewed in the next section, these driving measures are sensitive to causal factors of driver fatigue. Decrements in driving performance have been correlated with reliable measures of sleepiness such as electroencephalogram (EEG) activity, sleep latency on the Multiple Sleep Latency Test (Carskadon et al., 1986), and scores on subjective sleepiness scales. These performance measures are also sensitive to task demands, task duration, and task environment.

3. CAUSAL FACTORS OF DRIVER FATIGUE The general, broad term of drowsy driving has been very difficult to define, predict, and regulate. It is proposed that one can divide these variables into task-related factors and sleep-related factors, and that these causal factors influence each other (May & Baldwin, 2009). Task-related causes of fatigue include task demands, duration of the drive, and monotony of the environment. Research into sleep-related causes of fatigue indicates that circadian rhythms (time of day), sleep quality, sleep quantity, and duration of wakefulness significantly impact driving performance.

3.1. Task-Related Fatigue Task demands can produce fatigue when either the task is too taxing or it is too boring/monotonous (Desmond & Hancock, 2001; Gimeno, Cerezuala, & Montanes, 2006). Examples of high task demand situations include highdensity traffic, poor weather conditions, or completing a secondary task (i.e., searching for an address) in addition to driving (May & Baldwin, 2009). These conditions can lead to active fatigue (Gimeno et al., 2006). Active fatigue is related to resource theories for attentional processing (Wickens, 1984). When task demands increase beyond

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Driver Fatigue

a person’s attentional capacity, such as during dual-task paradigms, performance decrements are noted (Wickens, 1984). In addition, fatigue has been shown to reduce attentional resources so that fatigue-related impairments should worsen as task demands increase (Desmond, 1998; Desmond & Matthews, 1997). Passive fatigue is produced when the driving task is monotonous, automated, or predictable (Gimeno et al., 2006). During these conditions, the driver is mainly monitoring the driving environment. This passive fatigue is most likely to occur when the roadway is monotonous, there is little traffic, or the route is very familiar. Thiffault and Bergeron (2003) used a simulated driving task to show how performance is worse in a monotonous scenario, as indicated by a increase in large steering wheel movements or overcorrections. Another attributing factor to task-related fatigue that interacts with monotony is time on task. Driving performance deteriorates over the duration of a drive, especially in monotonous conditions. Over time, drivers have a greater number of overcorrections (Thiffault & Bergeron, 2003) and a greater amount of weaving (van der Hulst, Meijman, & Rothengatter, 2001). Studies comparing driving performance and EEG have shown that alpha bursts and/or theta activity increase over the drive (Brookhuis & Waard, 1993; Lemke, 1982; Risser, Ware, & Freeman, 2000; Schier, 2000). In addition, Risser et al. found a strong correlation between driving measures of lane position variability and crash frequency and the frequency of 3-s alpha bursts during the drive.

3.2. Sleep-Related Fatigue Sleep influences driving performance in several different ways. First, the body’s circadian rhythm causes drivers to be more susceptible to sleepiness or fatigue during certain times of the day. How long a driver has been awake prior to a crash and the amount of sleep deprivation/restriction the driver has accumulated also impact performance. Sleep quality, how well someone sleeps during the night, will also influence sleepiness and impact driving performance. The major theory of sleepiness is the two-process model (Borbely, Achermann, Trachsel, & Tobler, 1989; Kleitman, 1963). This theory states that sleepiness is determined by two different mechanisms in the brain. One mechanism is the pressure to sleep (i.e., the sleep drive). The sleep drive peaks between 10 p.m. and midnight, influencing bedtimes. This sleep drive includes the homeostatic factor of wake duration. The longer a person is awake, the more pressure he or she feels to sleep. The other drive is the ability to stay awake (i.e., the wake drive). This drive includes the circadian rhythm and core body temperature. The peak of the wake drive typically occurs at 7 to 9 p.m. and is at its lowest between 4 and 5 a.m. Not only is performance at risk

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during lows in the circadian rhythm but also performance degrades as time awake increases. Homeostatic influence on sleepiness refers to the duration of wakefulness. The longer a person remains awake, performance decrements and sleepiness will increase. Incorporated in this duration of wakefulness is sleep deprivation and sleep restriction. Performance and sleepiness are also affected by sleep deprivation. Sleep restriction, or not obtaining adequate sleep over time, will also result in increased sleepiness and a decline in performance. Baulk, Biggs, Reid, van den Heuvel, and Dawson (2008) demonstrated this homeostasis effect on driving performance and performance on the psychomotor vigilance test. Participants underwent 26 h of wakefulness and completed driving and psychomotor vigilance tasks (PVTs) at 4, 8, 12, and 24 h of wakefulness. They were also tested after an 8 h recovery period. Lane drifts and speed deviation increased as time awake increased. For the PVTs, reaction time and number of lapses also increased as wakefulness increased. All measures were improved by the opportunity of an 8 h recovery. Lenne, Triggs, and Redman (1998) showed that compared to a full night (8 h) of sleep, complete sleep deprivation results in greater lane position variability (weaving), speed variability, and reaction time in a simulated driving environment. Philip et al. (2005) demonstrated that drivers with 2 h of sleep had a greater number of inappropriate line crossings, an increase in reaction time, and a higher perception of sleepiness compared to drivers who slept 8.5 h. Sleep-deprived drivers also show a higher frequency of attention lapses and eye closures during simulated driving tasks (Kozak et al., 2005). Pizza, Contardi, Mostacci, Mondini, and Cirignotta (2004) also demonstrated the impact of sleep deprivation on driving performance. A group of healthy adults completed a series of driving simulator tests, with objective and subjective measures of sleepiness after a night of sleep deprivation and a night of normal sleep. Results showed that lane position variability, speeding, and mean reaction time were greater in the sleep deprivation condition and that performance worsened throughout the day. Sleepiness was also greater for the sleep deprivation condition. Sleep latency on the Multiple Sleep Latency Test (MSLT) was shorter in this condition and reports of subjective sleepiness were greater. Ware et al.(2007) tested the effects of sleep deprivation on performance of a critical tracking task, the PVT, and driving performance in a simulator task. Participants also completed a one-nap MSLT after the performance tests. These tests were completed after a night of 8 h of sleep, 4 h of sleep, and 0 h of sleep. Lane position variability in the driving simulator was able to significantly discriminate between all three sleep deprivation conditions.

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The circadian rhythm defines one’s sleep/wake pattern. It is considered the internal body clock, which drives humans to sleep during the night and be awake during the day. The circadian rhythm also has an alertness dip in the early afternoon during which people are sleepier (Monk, 1991). Performance decrements are evident during the troughs in the circadian rhythm. Statistics show that there is an increased amount of sleep-related crashes between 2 and 6 a.m. and also between 2 and 4 p.m., which correspond to these troughs (Pack et al., 1995). NHTSA (2011) stated that midnight to 3 a.m. on Saturdays/Sundays was the deadliest driving period in 2009. Circadian effects have also been demonstrated during a driving simulator tasks. Lenne, Triggs, and Redman (1997) showed that fluctuations in speed and reaction times are greatest at 6 a.m., 2 p.m., and 2 a.m. Akerstedt et al. (2010) also found time-of-day effects on driving performance, with lane position variability, line crossings, and subjective sleepiness higher during the nighttime drives. The two-model theory was mathematically modeled and used to predict road crashes (Akerstedt, Connor, Gray, & Kecklund, 2008). Time of day, time awake, and total sleep time were factors used to predict crash risk. These were combined to create the sleep/wake predictor (SWP). To test the model, these researchers fit the model to data of serious injury crashes and matched random controls. They called drivers of these crashes to obtain sleep data. The SWP was a significant predictor of crash occurrence. After controlling for covariates, each 1-unit increase in the sleep/wake predictor increased the odds of a crash by 1.72. Covariates accounted for were level of education, ethnicity, age, gender, and blood alcohol level.

3.3. Causal Interactions Task- and sleep-related factors can interact to further impact driver fatigue. Johns (1998) elaborated on the sleep/ wake drive theory to include a secondary sleep drive and a secondary wake drive by incorporating the influence of motivation and environment. The secondary wake drive may be influenced by sensory inputs from the environment, including posture, lighting, and workload. Performance may be influenced to a greater degree by the ability to stay awake. Although a person may complain of sleepiness, if he or she is interacting with the environment, this might help him or her stay awake. The secondary sleep drive is related to the duration of wakefulness. The longer a person stays awake, the stronger the secondary sleep drive becomes. During sleep, this secondary sleep drive is reduced or discharged. This suggests that as sleep loss increases, effects of the environment and motivation may not be enough to keep a person awake, and sleep will prevail. This

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theory integrates well environmental factors influencing sleepiness, circadian factors, and homeostatic factors. A comprehensive literature review (Williamson et al., 2011) examined how sleep homeostasis, circadian factors, and task effects impact accidents, injuries, and performance. Their review supports a direct impact of time awake/sleep deprivation, sustained attention, and monotony on performance. They suggest that circadian factors do not directly influence performance but, rather, interact with other factors to make drivers more susceptible to driver fatigue.

4. HIGH-RISK POPULATIONS One of the major benefits of the driving simulator test is to safely determine performance decrements in high-risk populations. Studies have shown that driving performance is worse for sleep disorder patients and participants undergoing sleep deprivation than for control participants. Other studies have shown that treatment for sleep disorders improves driving performance in these patients. Commercial drivers, shift workers, and teenagers are also at risk for crashes due to driver fatigue.

4.1. Untreated Sleep Disorders Some states in the United States require physicians to report diagnoses of a sleep disorder, such as sleep apnea or narcolepsy, to the department of motor vehicles. California is one example. This state requires the reporting of sleep disorders when they present a possible cause of lapse of consciousness while driving (Janke, 2001). Patients with untreated sleep apnea have fragmented sleep and fluctuations in their blood oxygen level, causing morning headaches, irritability, and fatigue. Risser et al. (2000) compared driving simulator performance of sleep apnea patients with performance of normal, healthy control participants. They found that the sleep apnea patients had increased lane position variability, steering rate variability, speed variability, and crash frequency. Lane position variability and crash frequency increased during the 60-min drive in the sleep apnea group, indicating a vigilance decrement during the drive. The sleep apnea patients overall had greater lane position variability and crash frequency compared to controls. George, Boudreau, and Smiley (1996) also demonstrated that sleep apnea patients have greater tracking errors on a divided attention driving test, and some of the apnea patients even performed worse than healthy controls under the influence of alcohol. They also found that drivers with shorter sleep latencies on the MSLT had more tracking errors. Treatment for sleep apnea, continuous positive airway pressure, has been shown to improve driving simulator

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performance. Turkington, Sircar, Saralaya, and Elliot (2004) compared sleep apnea patients undergoing treatment with those not yet receiving treatment over a period of 7 days. The driving test was given at the same time each day and was a 20-min drive using a divided attention driving simulation test. There was no significant difference in driving performance measures at baseline between the two groups. The treatment group showed significantly lower tracking error (lane position variability), faster reaction time, and fewer off-road events compared to the nontreatment group. One study compared driving simulator performance in untreated sleep disorder patients, sleep-deprived participants, treated sleep disordered patients, participants consuming alcohol, and normal, healthy controls (Hack, Choi, Vijayaplalan, Davies, & Stradling, 2001). Driving performance measures included lane position variability, number of off-road events, and length of drive completed. Sleep-deprived participants had significantly poorer driving performance compared to non-sleep-deprived controls. Participants consuming alcohol performed significantly worse compared to their driving performance when sober. Untreated sleep apnea patients experienced greater lane position variability than participants who consumed alcohol, but they experienced better lane position variability than sleep-deprived participants.

4.2. Commercial Drivers Truck drivers are at risk because of the long distances, long irregular work schedules, and high demands to get to their destination on time. They are at a higher risk of crashes in general because of their high amount of miles driven compared to noncommercial drivers. Truck drivers have been in the spotlight because of their work schedules, crash rates, and potential for driver fatigue. Arnold and Hartley (2001) surveyed 84 transport companies about their policies concerning driver fatigue. The majority of the companies did not have a formal fatigue policy. They reported that only 31% of the companies had driving hour restrictions of 14 h or less (14 h being the national suggested limit). Forty percent of the companies did not have a weekly hour restriction for driving. Six percent of the companies admitted to sending drivers out without adequate rest when an urgent load needed transport. On a more positive note, approximately 50% of the companies stated they monitored driver fatigue, and 90% reported sending drivers home or giving them time off if they seemed unfit to drive (including fatigue). The Federal Motor Carrier Safety Administration (2008) has set hours of service rules for commercial drivers (49 CFR Part 395). Property-carrying drivers may drive up to 11 h after having 10 consecutive h off duty. Passengercarrying drivers may drive up to 10 h after 8 consecutive

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off-duty hours. There is a maximum threshold of 14 consecutive hours of driving (15 h for passenger-carrying drivers). There is also a weekly maximum: Drivers cannot drive more than 60 h in 7 consecutive days or 70 h in 8 consecutive days. Furthermore, if the driver has a sleeping berth, 8 of the 10 h off duty must be taken in the sleeper berth. N.J.S.2C:11-5 (New Jersey General Assembly, 2003), a law concerning vehicular homicide due to reckless driving, was updated to include fatigue because of a deadly crash involving a sleep-deprived commercial trucker. New Zealand truck drivers involved in a reported crash were asked to complete a questionnaire about their sleep habits, sleep history, and work history 72 h before the crash (Gander, Marshall, James, & Quesne, 2006). Based on these self-reports, 11% of drivers had at least two risk factors for sleepiness/fatigue. Risk factors included wake duration of greater than 12 h, less than 6 h of sleep in the 24 h preceding the crash, more than 1 week since the driver had two consecutive nights of good sleep, and crashes occurring between midnight and 8 a.m. Fatigue was identified as a factor in 5% of the crash reports, as indicated by a check-box option for fatigue on the report. Results indicate that some truckers are not obtaining adequate sleep before driving but also that the “check box” method is not the best way to identify fatigue as a cause of a crash. Mitler, Miller, Lipsitz, Walsh, and Wylie (1997) studied 80 truck drivers in four driving conditions. The driving conditions were (1) five 10-h day drives, (2) five 10-h night drives, (3) four 13-h day drives, and (4) four 13-h night drives. EEG and driving performance were measured during all the drives. Results showed that drivers averaged 4.75 h of sleep, as determined by the EEG. Sleep time ranged from 3.83 h (subjects in the 13-h night condition) to 5.83 h (subjects in the 10-h day condition). Subjects’ ideal amount of sleep was 7.1 1 h of sleep. Forty-four percent of the drivers took naps to augment their sleep. This suggests that truck drivers obtain less sleep than required to maintain alertness on the job. The drivers had a greater vulnerability to sleep in the late night and early morning. Hanowski, Wierwille, and Dingus (2003) conducted a two-phase study examining the extent to which fatigue impacts driving performance among short-haul truckers. Phase 1 consisted of focus groups in which drivers provided their input about safety in the industry, including driver fatigue. The second phase was an on-road driving study in which the trucks were instrumented with devices to record the roadway ahead of the trucks, drivers’ facial expressions, and eye closures. The trucks were instrumented with side and rear cameras as well. Episodes of at-fault near-crashes were analyzed to determine to what degree fatigue was considered to be part of the event. Results indicated that fatigue was present immediately preceding the events captured in the study. Researchers theorize that driver

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behaviors while off work (e.g., not sleeping during their rest periods) were part of the reason for the fatigue.

4.3. Shift Workers Statistics from the U.S. Department of Labor (2002) indicate that 14.5 million full-time workers have alternative schedules. Night-shift workers represent 3.3% of the working population. One of the most dangerous aspects of working night shift is driving home after the shift. Sleepiness during night work has also been associated with an increase in single-car accidents (Akerstedt, 1985). In the 2008 Sleep in America Poll (National Sleep Foundation, 2008), 48% of shift workers admitted to driving drowsy at least once a month in the past year. In addition, 58% of shift workers in the poll reported spending less than 6 h in bed on workdays. Night-shift workers are disadvantaged because the sun is up when they leave work in the morning. This can reset their circadian rhythm and make it more difficult for them to fall asleep during the day. Society also runs on a circadian rhythm schedule, so while the night-shift worker is trying to sleep, most other people are awake. Ringing telephones, noisy traffic, and family interruptions negatively impact daytime sleep. Total sleep time for nightshift workers is typically reduced by 2e4 h, usually because they cannot continue to sleep as long as necessary (Akerstedt, 1985). Ingre et al. (2006) studied shift workers after a night of work and a night of sleep, having these participants complete a driving simulator task and subjective sleepiness scales. Driving performance was measured by lane crossings. They counted the number of incidents (two tires crossing the outside lane marker), accidents (two wheels off the road or four wheels in the opposite lane), and crashes (four wheels off the road). Driving post-shift, time on task, and subjective sleepiness increased the odds of having an incident, accident, or crash.

4.4. Teenagers and Young Adults Teenagers are at risk because they often drive when sleep deprived. This is due to the demands of school, work, extracurricular activities, and socializing late at night (Lyznicki, Doege, Davis, & Williams, 1998). In the 2006 Sleep in America Poll (National Sleep Foundation, 2006), 56% of adolescents believed they did not obtain sufficient amounts of sleep at night, and 51% of teenage drivers reported driving drowsy within the past year. High school students usually stay up late and then have to get up early for school, decreasing their sleep time. Several studies have shown that crash rates are lower for teens when school start times are later (Danner & Phillips, 2008; Dexter, Bijwadia, Schilling, & Applebaugh, 2003; Vorona, Szlo-Coxe, Wu, Dubik, & Zhao, accepted for publication). Teens also do

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not have much experience in driving and are more prone to risk taking, also putting them at an increased risk for crashes (Ferguson, 2003; Williams, 2003). According to 2009 statistics, people ages 21e24 years had the highest fatality crash rate, and the injury rate was highest for people ages 16e20 years (NHTSA, 2011). Based on results of a survey on distracted and drowsy driving between 1998 and 2002, NHTSA reported that drivers under the age of 30 are approximately 6 times more likely to report involvement in a drowsy driving crash and that 20% of drowsy driving crashes involved a driver age 21 or younger (NHTSA, 2002). It is suggested that graduated licenses that limit nighttime driving and passenger limits may mitigate drowsy driving among teenagers (Ferguson, 2003).

5. COUNTERMEASURES AND DETECTION/ WARNING TECHNOLOGIES Several articles and reports have concisely reviewed countermeasures (Gershon, Shinar, Oron-Gilad, Parmet, & Ronen, 2011) and detection technologies (Barr, Popkin, & Howarth, 2009; May & Baldwin, 2009) in relation to driver fatigue. This section reviews driver-initiated countermeasures, summarizing which are perceived as effective and which are proven effective. Effective roadway designs and current technology in use or on the market to detect and warn drivers about their fatigue or performance decrement are also discussed.

5.1. Driver-Initiated Countermeasures Drivers use various techniques to keep them awake while driving. A national survey of Swedish licensed drivers revealed that the most common techniques used for staying awake were stopping to take a walk, listening to the radio, opening a window, drinking coffee, and asking passengers to converse (Anund, Kecklund, Peters, & Akerstedt, 2008). Opening a window and listening to the radio are perceived as effective by both professional and nonprofessional drivers (Gershon et al., 2011). Nonprofessional drivers also use interactive techniques such as talking on a cell phone (Gershon et al., 2011). Past research, however, does not support these perceptions (Reyner & Horne, 1998b). Reyner and Horne conducted a study in which 16 young adults were subjected to cold air, music, or no intervention while driving, after reducing their nighttime sleep to 5 h. Results showed that there was no significant difference between treatments for the number of lane drifts or accidents, although the music seemed to have a better and longer effect than the cold air. Effective countermeasures include stopping for a break, napping, and using caffeine. Maycock (1996) demonstrated that drinking coffee and taking a less than 15-min nap (or both) during a half-hour break was very effective in

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countering driver fatigue. Horne and Reyner (1996) found similar results, with caffeine and napping improving driving performance, subjective sleepiness, and EEG indications of sleepiness. Professional drivers are more likely to use these techniques and perceive them as effective (Gershon et al., 2011).

5.2. Roadway Warning Systems Rumble strips on the side of the road or along the centerline are designed to alert drivers when they drive off the road. Rumble strips are grooved designs along the side of the roadway or at the centerline that produce a loud noise when a driver crosses or drives along the strip (May & Baldwin, 2009). An advantage of rumble strips is that they are available for all drivers. Milled rumble strips are the most common and can be constructed on both existing and new asphalt shoulders (Perrillo, 1998). Research examining the effectiveness of centerline rumble strips showed a reduction of crashes along rural two-lane roads (Persaud, Retting, & Lyon, 2004). Off-road rumble strips have also demonstrated effectiveness in reducing the amount of offroad crashes (Perrillo, 1998). In a driving simulator study, Anund, Kecklund, Vadeby, Hjalmdahl, and Akerstedt (2008) tested the effects of milled rumble strips on sleepiness and driving performance. Results revealed an increase in EEG indicators of sleepiness, eye closure duration, lane position variability, and subjective sleepiness immediately before drivers hit the rumble strips. The rumble strips improved performance and objective sleepiness, but this effect was short-lasting, with decrements recurring approximately 5 min after the drivers hit the rumble strips. This stresses an important point that rumble strips do not counter fatigue or sleepiness but, rather, warn drivers of their level of sleepiness and performance decrement so they can take a break and rest.

5.3. Fatigue Detection and Warning Systems Companies have developed detection and warning systems based on the driving variables sensitive to driver fatigue and sleepiness. Vehicles are being designed with lane departure warning systems, crash avoidance systems, and steering correction monitors to analyze driving patterns and warn drivers of their degrading performance. In the commercial trucking industry, technology has also focused on driver characteristics such as eye closures. Lane position variability and eye closure variables demonstrate sensitivity to subjective sleepiness, time of day, and time-on-task effects (Akerstedt et al., 2010). Although EEG algorithms demonstrate a reliable measure of driver fatigue (Lal & Craig, 2001b; Mallis, 1999), it is not a practical, real world option for monitoring driver fatigue.

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Computerized monitoring and analysis of eye closures has proven to be an effective tool in identifying driver fatigue. The PERCLOS (percent eye closure) system calculates the amount of eyelid closure over the pupil based on video monitoring of the eyes (Dinges & Grace, 1998; Dinges, Mallis, Maislin, & Powell, 1998). This system has been validated in on-road driving studies (Dinges & Grace, 1998) and with the psychomotor vigilance task with up to 42 hours of sleep deprivation Mallis, 1999). AVECLOS is a simple binary measure of eye closures; it is based on PERCLOS but does not use as much computer power (Barr, Popkin, & Howard, 2009). This technology is currently used in the Driver Fatigue Monitor DD850 (Attention Technologies) and the Delphi Driver State Monitor (Barr et al., 2009). Both are designed for use in the trucking industry. The Driver Fatigue Monitor provides audible alerts and visual displays of PERCLOS results and distance (May & Baldwin, 2009). Other eye-closure technologies are discussed by Barr et al. (2009). Lane-drifting or line-crossing technology analyzes visual information from the roadway received via video camera, determines lane boundaries, and alerts drivers when they unintentionally exit their lane (May & Baldwin, 2009). SafeTRAC (AssistWare Technology, 2005) and AutoVue (Iteris) are two examples of devices that use this technology. The AutoVue system alerts the driver by emitting a virtual rumble strip sound and is installed in several manufactured passenger vehicles. SafeTRAC emits an audible alert when the driver departs the lane and also calculates a score of performance. Collision avoidance warning systems alert drivers of potential rear-end or from-the-side crashes and could potentially be effective in monotonous longer drives with regard to driver fatigue (May & Baldwin, 2009). Collision avoidance warning systems can measure time to collision, temporal headway, or the distance between the side of the car and another object (Ben-Yaacov, Maltz, & Shinar, 2002). May, Baldwin, and Parasuraman (2006) demonstrated how this type of warning could be effective with task-related fatigue. Their study showed that crashes were reduced in drivers exhibiting taskrelated fatigue when presented with an auditory warning prior to a potential head-on crash in a driving simulator task. Corrective steering movements can also be monitored and analyzed using technology to detect performance decrements related to driver fatigue. Steering monitors analyze microcorrections or small, corrective steering movements and sound an alert when normal steering movements stop occurring (Hartley, Horberry, Mabbott, & Krueger, 2000; May & Baldwin, 2009). Steering monitors that have been developed include the S.A.M. G3, the ZzzzAlert Driver Fatigue Warning System, and the TravAlert early warning system (Hartley et al., 2000).

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6. CONCLUSIONS AND FUTURE RESEARCH There is a need for further research on driver fatigue, particularly with regard to individual differences and validating simulated research in a real-world environment. Individual differences influence tolerance to sleepiness and driver fatigue and subsequent risks to driving performance. Milia, Smolensky, Costa, and Howard (2011) reviewed how potential demographic factors could impact fatigue and driving accidents. Their review of the literature concluded that with the exception of age and gender, few other demographic variables have been investigated. Some potential variables they suggested include meal times, dependent care, physical/mental health, medications, circadian chronotypes (evening/morning personalities), and number of jobs. One recommendation they make for future research is to consider these demographics as independent variables instead of confounding variables. Ingre et al. (2006) also acknowledged the presence of individual differences in subjective sleepiness and accident risk. They suggest that these individual differences or susceptibility to sleepiness and accident risk could be considered a possible trait. Akerstedt et al. (2010) also stress the need for future research identifying and predicting individual differences with regard to sleepiness. These individual differences that seem to confound the literature make it difficult to develop strong models of how constructs of fatigue impact driving performance. Despite the widespread use of driving simulators to assess sleepiness and infer fitness to drive, research examining their ability to predict real driving performance is lacking. Many articles have discussed results of driving simulator tests in terms of how this performance is indicative of sleepiness, but how simulator performance translates to on-road driving performance has not fully been investigated beyond crash rates and observational reports. For instance, Findley, Guchu, Fabrizio, Buckner, and Suratt (1995) demonstrated that poorer drivers on the Steer Clear simulator (as defined by number of obstacles hit) had a higher documented accident rate. Two studies have investigated predictive validity of driving simulator performance but not in relation to sleepiness (Freund, Gravenstein, Ferris, & Shaheen, 2002; Tornros, 1998). Both of these validation studies show that driving simulator performance can be strongly related to on-road performance. Tornros validated two measures used in sleep and driving researchdlane position variability and speed variability. However, these measures were only validated for a tunnel scenario. Freund et al. were able to validate their urban driving scenario, which was designed using the same simulator used in several sleep and driving studies (May, Ware, & Vorona, 2005; Pizza et al., 2004); again, this was only validated for measures of error and rule

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violation and only validates their scenario. Validity can really only be applied to the context in which the test was intended. There are two possible explanations for this void in the literature. First, direct translation between driving simulation and on-road performance is situation and scenario specific. The validity of a driving simulator is going to vary depending on the characteristics of the scenario of the drive. Second, until recently, the technology to record and measure on-road performance was not advanced enough to be reliable for this purpose. The need to evaluate driving performance in a sleep disordered population (especially as required by, for example, the professional trucking industry) lends itself to be situation specific for this type of validation. Verifying that performance during simulator driving can predict on-road performance in the context of sleepiness would be especially important in sleep disorder centers in which driving simulation tests can be used as a supplemental clinical tool to determine fitness to drive in sleep disordered patients. In summary, driver fatigue is a significant factor in driving performance decrements and crashes. There are numerous task-related and sleep-related causes of driver fatigue, and these can interact to further degrade performance or make drivers more susceptible to driver fatigue. Specifically at risk are persons with untreated sleep disorders, shift workers, commercial drivers, and teenagers. Technology exists that can help identify and warn drivers of their performance deterioration or level of fatigue, but the only way to reduce driver fatigue or sleepiness is to take a break, rest, and intake caffeine. Future research should focus on variables that influence individual differences in susceptibility to driver fatigue and strengthen the case of predictive validity between simulator research and on-road performance.

ACKNOWLEDGMENTS I thank my colleagues and mentors for feedback on various parts of this chapter during its development. They include Drs. J. Catesby Ware, Bryan Porter, Elaine Justice, James Bliss, Carryl Baldwin, and Frederick Freeman.

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Key Problem Behaviors

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Driver Fatigue

Reyner, L. A., & Horne, J. A. (1998b). Evaluation of “in-car” countermeasures to sleepiness: Cold air and radio. Sleep, 21(1), 46e50. Risser, M. R., Ware, J. C., & Freeman, F. G. (2000). Driving simulation with EEG monitoring in normal and obstructive sleep apnea patients. Sleep, 23(3), 393e398. Schier, M. A. (2000). Changes in EEG alpha power during simulation driving: A simulation. International Journal of Psychophysiology, 37, 155e162. Shen, J., Barbera, J., & Shapiro, C. M. (2006). Distinguishing sleepiness and fatigue: Focus on definition and measurement. Sleep Medicine Reviews, 10, 63e76. Smith, R., Cook, L. J., Olson, L. M., Reading, J. C., & Dean, J. M. (2004). Trends of behavioral risk factors in motor vehicle crashes in Utah, 1992e1997. Accident Analysis and Prevention, 36(2), 249e255. Soames-Job, R. F., & Dalziel, J. R. (2001). Defining fatigue as a condition of the organism and distinguishing it from habituation, adaptation and boredom. In P. A. Hancock, & P. A. Desmond (Eds.), Stress, workload and fatigue (pp. 466e475). Mahwah, NJ: Erlbaum. Thiffault, P., & Bergeron, J. (2003). Monotony of road environment and driver fatigue: A simulator study. Accident Analysis and Prevention, 35(3), 381e391. Tornros, J. (1998). Driving behaviour in a real and a simulated road tunnel: A validation study. Accident Analysis and Prevention, 30(4), 497e503. Turkington, P. M., Sircar, M., Saralaya, D., & Elliot, M. W. (2004). Time course of changes in driving simulator performance with and without

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Vulnerable and Problem Road Users 22. 23. 24. 25.

Young Children and “Tweens” Young Drivers Older Drivers Pedestrians

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26. Bicyclists 27. Motorcyclists 28. Professional Drivers

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Chapter 22

Young Children and “Tweens” Kelli England Will Eastern Virginia Medical School, Norfolk, VA, USA

If a disease were killing our children in the proportions that injuries are, people would be outraged and demand that this killer be stopped. dC. Everett Koop, U.S. Surgeon General, 1982e1989

1. INTRODUCTION Road traffic injury is the leading cause of global injury death and the ninth leading cause of disease burden (World Health Organization, 2009). There are more than 1.2 million deaths and as many as 50 million traffic-related injuries in the world per year (World Health Organization, 2009). The global cost of traffic injuries is estimated to be $518 billion, costing governments between 1 and 3% of their gross national product (World Health Organization, 2009). If the current trends continue, road traffic injuries are predicted to be the fifth leading cause of disease burden for all ages by the year 2030 (World Health Organization, 2009). Children are especially susceptible to road traffic injury, and morbidity and mortality rates throughout the world reflect this vulnerability. Children interact with roadways in a number of ways. In most low- and middle-income countries, the majority of road users are not vehicle occupants but, rather, vulnerable road users without a protective shell around them. These include pedestrians, cyclists, motorcycle passengers, and passengers loading and unloading from public transport (Peden et al., 2008; World Health Organization, 2009). Often, transport projects are not designed with the unique safety needs of children and other vulnerable road users in mind (Toroyan & Peden, 2007; World Health Organization, 2009). Children may be required to walk alongside or weave in and out of traffic during their daily routines as they walk to school, complete their chores, and seek open areas to play. This, coupled with children’s limitations in development, is a recipe for disaster. In addition to the risk stemming from the manner in which children interact with the roadway environment, children possess a number of unique developmental characteristics that play into their

susceptibility to roadway injuries (Toroyan & Peden, 2007). Children’s bodies are small and still developing and thus are less able to withstand crash forces. Small body mass also means they are not easily seen by motorists, nor can they view surrounding traffic. Furthermore, children’s disproportionally large head size leads to a greater number of head injuries given their higher center of gravity. Judgments of speed and distance are difficult for children due to underdeveloped visual depth perception, hearing, and kinesthetic senses. Children are also prone to limitations in attention, engage in more risk taking, are more susceptible to social influence, and are generally inexperienced in accurately perceiving hazards in their environment (Toroyan & Peden, 2007). The unfortunate result of the interplay between the roadway environment and these developmental limitations is the global pandemic of road traffic injuries the world’s children are currently experiencing. One-fifth of all road traffic deaths are among children (Peden et al., 2008). Every year, approximately 262,000 of the world’s children are killed and an estimated 10 million are injured in roadway crashes (Peden et al., 2008). Globally, traffic injuries are among the top three causes of death for children older than age 5 years. For children aged 1e4 years, traffic injuries are among the top 10 causes of death (Toroyan & Peden, 2007; World Health Organization, 2009). By 2015, roadway injuries are predicted to be the number one cause of death and disability for all children older than age 4 years (Mathers & Loncar, 2005). Traffic-related injuries to children are most commonly head and limb injuries, often resulting in death or long-term disability (Peden et al., 2004; Toroyan & Peden, 2007). Traumatic brain injuries are the most frequent cause of trafficrelated deaths and injuries in countries of all income levels (Peden et al., 2004, 2008; Toroyan & Peden, 2007). Chest and abdominal injuries are less common by comparison but still frequent and very serious given the complexity of managing damage to internal organs (Peden et al., 2008). Following crashes, children and their families experience a myriad of issues, including coping with loss of a loved one; disabilities

301 Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10022-0 Copyright Ó Elsevier Inc. 2011 Portions of text have originally appeared in Advances in Psychology Research, Volume 40, ‘Large-Scale Prevention of Alcohol-Impaired Driving’, 2006, Kelli England, with permission from Nova Science Publishers, Inc.

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and impairments; financial hardships; costly and often substandard medical care; and high levels of psychosocial distress, including anxiety and post-traumatic stress disorder (Peden et al., 2008).

2. GLOBAL ECONOMIC DISPARITIES IN ROAD TRAFFIC INJURIES Unfortunately, the road traffic injury burden falls disproportionately on children from poorer economic settings. Despite the fact that the majority of the world’s vehicles are in high-income countries, more than 90% of motor vehiclerelated deaths occur in low- and middle-income countries (Peden et al., 2004, 2008; World Health Organization, 2009). For instance, two-thirds of the world’s annual crash deaths occur in the low- and middle-income countries of the Southeast Asia, African, and Western Pacific regions (Peden et al., 2008). Furthermore, the worst road death rate is in Africa, which has very few vehicles relative to the rest of the world. Consider that Africa has a road traffic injury mortality rate of 19.9 per 100,000 children, whereas the corresponding rate in the high-income countries of the European region is 5.2 per 100,000 children (Peden et al., 2008). Road traffic injuries remain a leading cause of death and disproportionately affect the poor even in wealthy countries. However, in wealthy countries, fatality rates are decreasing despite increases in motorization (Safe Kids Worldwide, 2004). In most high-income countries, slow increases in motorization have allowed safety practices to develop in concert with increased traffic volume. In Finland, for instance, 30 years of road safety campaigns have led to a 50% decrease in fatalities, despite tripling of the country’s traffic volume (United Nations, 2003). Conversely, in low- and middle-income countries, fatality rates are increasing. For instance, child traffic deaths increased by one-third in China and sub-Saharan Africa during the 1990s (Safe Kids Worldwide, 2004). Unlike the development of traffic safety practices in highincome countries, developing nations are becoming motorized at such a rapid pace that appropriate safety measures and regulations are lagging (World Health Organization, 2009). Many low- and middle-income countries contend with poor roads, lax enforcement of driving rules, multi-use roadways, and too few controlled intersections and safe pedestrian crossings (World Health Organization, 2009). Economic disparities in road traffic injuries are expected to worsen. By 2020, road traffic injuries are predicted to increase by 83% in low- and middle-income countries and decrease by 27% in high-income countries. The resulting global increase in traffic-related injuries is expected to be 67% (Peden et al., 2004). Road traffic deaths in India and China alone are expected to increase approximately 147

PART | IV

Vulnerable and Problem Road Users

and 97%, respectively, by 2020 (Kopits & Cropper, 2005). Despite the growing magnitude of the problem in developing countries, road traffic injuries are often neglected in these countries’ research and policy initiatives (World Health Organization, 2009).

3. REGIONAL DIFFERENCES IN PRIORITY ISSUES AND STANDARD PRACTICES FOR PROTECTING CHILDREN The purpose of this discussion is to underscore the variability and dissention throughout the world in road safety practices and regulations regarding children. Developing countries struggle with a number of unique road traffic risks. For instance, a primary problem in many countries is that pedestrians, cyclists, and motor vehicles share the road. Like pedestrians, small vehicles such as motorbikes are also considered vulnerable road users because many developing nations have multi-use roadways and lack loadlimit or oversized vehicle standards. Comparatively speaking, tiny two-wheeled motorbikes ride alongside towering trucks overloaded with cargo. It is common for children to ride as passengers on motorbikes, and often helmets are not required or are not the norm. Roadway crowding can be a problem in some areas, and the density is further complicated by unclear or absent lane markings. Roadways may be unpaved or in otherwise poor condition in many areas. Other localities struggle with vehicle scarcity. Vehicle scarcity presents a problem when getting to a destination overrides the need for safe travel. It is not uncommon in Mali, Africa, for instance, to witness vehicles overloaded with occupants to the point that passengers catch rides by hanging onto the back of vehicles at speeds as high as 45 mph. Crucial risk factors for children in developing countries include poor implementation of road safety measures and underutilization of safety devices such as seat belts, child restraints, and helmets. In low-income countries, most fatalities are among vulnerable road users (e.g., pedestrians and cyclists), whereas in high-income countries most are among vehicle occupants (World Health Organization, 2009). Consider that 70% of traffic deaths in the low- and middle-income countries of the Western Pacific are among vulnerable road users, whereas almost the opposite is true in the high-income countries of the Americas, with 65% of traffic deaths occurring among vehicle occupants (World Health Organization, 2009). Despite decreases in road traffic fatality rates in high-income countries, road traffic injury remains a leading cause of death for children in developed nations. Thus, a primary concern in the United States and Sweden is better protection of children riding in vehicles. By contrast, priorities in localities such as Bangladesh and China include creation of safe pedestrian facilities and

Chapter | 22

Young Children and “Tweens”

improving helmet use rates given the high death rates among vulnerable road users (World Health Organization, 2009). Clearly, road injury risks need to be viewed in their local context because each nation has its own unique needs for intervention. Also, traffic-related risks in developing countries may be occurring alongside epidemics of potentially deadly diseases such as tuberculosis, malaria, and HIV/AIDS. Residents may be struggling with the uncertainty that comes with war, poverty, or food and shelter insecurity. Because of unique needs, risks are likely prioritized differently from nation to nation. Familiarity with a hazard also reduces perception of risk (Sandman, 1991; Slovic, 1991); therefore, what seems highly risky to a high-income country may be viewed with relative complacency in a developing country (and vice versa). In addition to having varying risk priorities, countries are at differing levels of need regarding child safety interventions. In many high-income countries, restraint usage is more than 90% (Peden et al., 2008). Therefore, these countries’ main concern regarding restraints is misuse because research indicates that at least three out of four safety restraints are unintentionally misused, potentially reducing their effectiveness by half (Carlsson, Norin, & Ysander, 1991; Decina & Lococo, 2005; Dukehart, Walker, Lococo, Decina, & Staplin, 2007; Durbin, Chen, Smith, Elliott, & Winston, 2005). In contrast, the more immediate goals of intervention in many low-income countries would be increased use of age-appropriate child restraints, regardless of proper installation. Once use is increased, intervention agents can begin to focus on installation issues. Although related, the methods for increasing use versus correct use are quite different. Countries also differ regarding their most needed modes of intervention. Educational efforts may be warranted in some localities, whereas others may focus on engineering improvements. Some countries need to pass legislation, whereas others are only lacking in enforcement of existing legislation.

4. KEY STRATEGIES FOR PREVENTING ROAD TRAFFIC INJURIES AMONG CHILDREN Keeping the differing priorities and needs throughout the world in mind, global leaders have collectively proposed a number of recommended measures that countries can work toward for improved roadway safety. The reader is referred to the comprehensive review of recommendations and evidence for their effectiveness contained in the World Report on Road Traffic Injury Prevention (Peden et al., 2004). Most road traffic injuries can be prevented with proper education, protective safety equipment, and safe systems approaches to roadway development (Peden et al., 2008; World Health Organization, 2009). Unlike other public health problems

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(e.g., obesity and mental illness), many proven methods for reducing road traffic injury to children are well understood and agreed upon in the field (Peden et al., 2004). For instance, substantial proportions of injured children could be protected with safe pedestrian passageways, protective helmets, and optimal restraints and seating during travel. However, as any safety advocate knows, injury prevention mechanisms are only useful when they are prioritized by governments and utilized by the public. Although all improvements in road safety measures will undoubtedly benefit children, interventions included in the World Report on Road Traffic Injury Prevention (Peden et al., 2004) that are specifically targeted to children and young road users include implementing engineering measures, improving vehicle design standards, increased use of safety equipment, enacting legislation and standards, developing education and skills, and improving emergency and trauma care (Peden et al., 2008). Such measures are detailed with regard to their impact on youth safety in the World Report on Child Injury Prevention (Peden et al., 2008). Thus, Peden and colleagues’ (2008) conclusions are only summarized here. The first recommended strategy, engineering measures, entails modifying the built environment to enhance child safety. Recommended engineering measures include techniques to reduce speed (e.g., speed humps and roundabouts), create safe play areas, provide safe routes to school, and physically separate two-wheelers from other traffic. Recommendations for improving vehicle design standards include requirements for energy-absorbing crumple zones and side-impact bars, redesign of car fronts for improved pedestrian-collision outcomes, improved fittings for child restraints, rear-visibility aids, and alcohol-interlock systems. The use of safety equipment should be enhanced with mandatory use laws, heightened enforcement, public awareness campaigns, and improved accessibility and affordability. Key safety equipment needs for children include developmentally appropriate child restraint systems; seat belts; bicycle helmets; motorcycle helmets; and improved conspicuity with retroreflective strips, bright colors, and daytime running lights. Stricter standards and legislation should be enacted and enforced regarding minimum age for motorized vehicle licensing, drink-driving prohibitions, mandatory seat belt and child restraint use, and mandatory helmet use. Efforts to further develop effective educational approaches are recommended, especially those that incorporate modern ecological models and are consistent with the state of the science in educational and behavior change theories. Finally, improving emergency, trauma, and rehabilitative care is important because maximum recovery from road traffic injuries is dependent on the availability, accessibility, and quality of such services (Peden et al., 2008).

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5. RECOMMENDATIONS FOR PROTECTING CHILD OCCUPANTS IN MOTOR VEHICLES Given the vast scope of issues relevant to protecting children on the world’s roads, and considering other chapters in this book discuss policy, enforcement, alcohol, speed, young driver, safety belt, motorcycle, bicycle, and pedestrian initiatives, the remainder of this chapter focuses on best practice recommendations for protecting children while traveling in motor vehicles. Such recommendations are complex and vary by developmental stage; thus, the detail is warranted. Note that given the great variability in standards and recommendations for child passenger safety throughout the world, this section is written from the perspective of the author’s home country, the United States. Nevertheless, the developmental needs of children in all areas of the world are the focus of this discussion, not the current recommendations of any one country. Although the needs and vulnerabilities of children traveling in vehicles is fairly universal, best practice recommendations will undoubtedly differ slightly by region of the world. Regional differences are noted when possible.

5.1. Occupant Crash Dynamics and Safety Restraints Sir Isaac Newton’s first law of motion states that an object in motion keeps moving in the direction and speed it was traveling in unless it is stopped by something. In the case of a vehicle, the stopping mechanism could be the brakes, another vehicle, or a tree, pole, or other stationary object. In the case of the occupants in that vehicle, the stopping mechanism could be the windshield, seat belt, or anything else inside or outside the vehicle in the path of motion (National Highway Traffic Safety Administration (NHTSA), 2007). If the vehicle is traveling at 35 mph, occupants will continue traveling at 35 mph once the vehicle crashes, unless they are restrained and stop with the vehicle. There are three collisions during a vehicle crash: (1) the vehicle collision, as the vehicle begins to stop when it collides with another vehicle or object; (2) the human collision, as the occupants in the vehicle continue to move forward at the same speed until they collide with something inside or outside the vehicle; and (3) the internal collision, as the occupant’s internal organs continue to move forward at the same speed until they collide with other organs and bones (NHTSA, 2007). Crash forces are quite powerful given the abrupt changes in momentum and velocity that occur in mere fractions of a second. To illustrate, one can consider the pounds of force needed to keep an occupant in position during a collision. This can be estimated fairly simply (albeit roughly) by multiplying a person’s weight times the speed of the vehicle (NHTSA, 2007). For

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example, if a 90-pound child is properly restrained with a seat belt in a vehicle that crashes when going 30 mph, the restraining force provided by the seat belt in the crash is 2700 pounds. This roughly equals the weight of a small car. Thus, without a seat belt, the child will propel forward with the force of the weight of a small car. Using a safety restraint dramatically reduces injury in a collision. Failing to wear a safety restraint increases one’s odds of injury or death in a crash by 45e74%, depending on the type of restraint and seating position (Arbogast, Jermakian, Kallan, & Durbin, 2009; Durbin et al., 2005; NHTSA, 2010; Rice & Anderson, 2009). Properly fitted safety restraints prevent injury by (1) keeping occupants in the vehicle, (2) contacting the strongest parts of the body, (3) spreading forces over a wide area of the body, (4) helping the body slow down, and (5) protecting the brain and spinal cord (NHTSA, 2007). Chief among these is the prevention of ejection from the vehicle because an occupant is four times more likely to be killed if thrown from the vehicle during a crash (NHTSA, 2007). Proper fit of a safety restraint is essential for maximum protection. Because safety belts are designed to fit the bodies of adults, safety belts alone are not sufficient for preventing injuries to small children (Arbogast et al., 2009; Durbin et al., 2005; NHTSA, 2007). Indeed, the primary reasons for injuries to children who are restrained at the time of motor vehicle crashes relate to premature graduation from child safety seats to booster seats, premature graduation from booster seats to adult safety belts, prematurely turning a child forward, misuse of safety restraints, and children seated in the front seat of the vehicle (Arbogast et al., 2009; Berg, Cook, Corneli, Vernon, & Dean, 2000; Braver, Whitfield, & Ferguson, 1998; Durbin et al., 2005; Henary et al., 2007; Lennon, Siskind, & Haworth, 2008; Rice & Anderson, 2009; Winston & Durbin, 1999). Young vehicle passengers are the most vulnerable in collisions because their bodies have not fully developed. In addition to their short stature and small body mass, children have pelvises that have not developed the iliac crest, a part of the hip bone that aids in keeping an adult safety belt correctly positioned low on the hips (NHTSA, 2007). The shoulder strap often falls across a child’s face or neck, and the lap belt is incorrectly positioned over soft parts of the abdomen. To improve both comfort and safety, infants or toddlers should always ride in a child safety seat and small children in a belt-positioning booster (NHTSA, 2007). (Note that safety seats and booster seats are referred to in this chapter collectively as child restraints.) A child restraint is designed to either bear the majority of the crash forces (for a rear-facing child) or distribute the crash forces over the strongest parts of a child’s body (NHTSA, 2007). When correctly installed and used, child safety seats reduce the risk of fatal injury by as much as 74% for infants and 59% for toddlers (Rice & Anderson, 2009).

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Lap/shoulder safety belts reduce the risk of fatal injury to front seat occupants older than age 5 years by 45e60% and moderate-to-critical injury by 50e65% (NHTSA, 2010). However, children aged 4e8-years are 45% less likely to sustain injury when positioned in a belt-positioning booster seat versus a vehicle seat belt alone (Arbogast et al., 2009). Compared to appropriately restrained children (aged 0e15 years), unrestrained children are greater than three times more likely to sustain injury in a crash, and children traveling in inappropriate restraints for their size are at two times the risk of injury (Durbin et al., 2005). Furthermore, mistakes in restraint installation and use can hamper the restraint’s ability to maximally protect from injury in a crash; many studies have shown that at least three out of four safety restraints are unintentionally misused (Decina & Lococo, 2005, 2007; Dukehart et al., 2007; O’Neil, Daniels, Talty, & Bull, 2009).

does not require side-impact padding in child restraints as do many European nations. Although the United States is far advanced compared to many lower income countries, its crash-worthiness regulations and standard crash test procedures are lagging in comparison to some other high-income countries. Not surprisingly, regional variations in morbidity and mortality are inversely related to advances in safety practices and recommendations. In the United States (and some other countries), parent education follows the “4 Steps for Kids” concept, emphasizing that there are four stages for proper restraint of a child in a vehicle. Each stage dictates a recommended restraint configuration and guidelines for minimum/maximum ages, weights, and heights. Because safety restraint weight and height ranges vary greatly, parents must read labels and instructions to determine if the seat is correct for a child’s age, weight, and height.

5.2. Restraint Use Recommendations by Developmental Stage

5.2.1. Stage 1: Rear-Facing Seats

In many countries, best practice recommendations for occupant protection are organized by stages of development. Best practice recommendations represent the current knowledge of safety experts regarding the best methods for protecting children of various sizes in crashes. Because legislation usually lags behind knowledge given the slow process of policy change, best practice recommendations are used to guide advocates and caregivers in the interim. Specific recommendations and timing of transitions vary by different regions of the world. In fact, child occupant recommendations and standards can be described as existing on a continuum, where some nations’ recommendations maximally protect children, others do not protect children at all, and the majority of countries fall somewhere in between. For example, in Sweden, best practice recommendations place children in rear-facing seats until 4 years of age, after which they transfer directly to booster seats (Watson & Monteiro, 2009). Compare this with Afghanistan, where children are not required to be buckled at all (World Health Organization, 2009). Examples of countries that fall somewhere in between these two extremes include Argentina and the United States. Children in Argentina are not required to use child restraints but are required to use safety belts (World Health Organization, 2009). In the United States, all 50 states have a child restraint law, but requirements vary substantially by state and seldom reflect best practice. That is, many states’ regulations fail to adequately protect booster-age children (Partners for Child Passenger Safety, 2007; Ross et al., 2004). Child restraint crash performance standards also show great variation: Canada, Australia, and many European nations have stricter minimum performance standards for crash tests than does the United States (Llewellyn, 2000). Perhaps as a result, the United States

A baby’s head is relatively large and heavy (comprising 25% of total body weight), and the neck and back are weak (NHTSA, 2007; Watson & Monteiro, 2009). With poor head and neck control, a young infant’s head can fall forward and occlude the airway if seated upright. Furthermore, very young children’s immature skeletal and organ development make them especially susceptible to injury and death from crash forces (NHTSA, 2007; Watson & Monteiro, 2009). In particular, frontal crash forces on a forward-facing child can lead to excessive stretching and even transection of the spinal cord due to the underdeveloped anatomy of the cervical spine (Watson & Monteiro, 2009). The odds of severe injury for forward-facing infants (<1 year) are 1.79 times higher than for rear-facing infants. Moreover, children 12e23 months old are 5.32 times more likely to sustain severe injuries when traveling in forwardfacing versus rear-facing seats (Henary et al., 2007). For these reasons, safety experts advise that infants and very young children ride semi-reclined at a 45-degree angle in a rear-facing seat that will cocoon around a baby’s body and bear the majority of crash forces (Bull & Durbin, 2008; Watson & Monteiro, 2009). Because most crashes are frontal collisions, simply facing a seat rearward dramatically changes the dynamics of the child’s movement in the crash and places the majority of harmful forces on the seat versus the child (Henary et al., 2007; NHTSA, 2007). A child’s head, neck, and spine are kept aligned, and the crash forces that are not absorbed by the safety seat are distributed over all these body areas (Watson & Monteiro, 2009). Children should remain rear facing as long as possible to the maximum weight limit for the seat, provided the head is below the top of the seat; in the United States, it is currently recommended that children face rearward until 2 years of age and at least 20 pounds. However, safety advocates in

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many countries are increasingly advising rear facing until age 4 years due to the profound protective benefits for children (Henary et al., 2007; Watson & Monteiro, 2009). Infants and young toddlers should generally ride in an infant seat (often with a detachable base) or convertible seat that faces rearward at first, then converts to forward facing once the child reaches a certain weight (35 pounds in the United States). In Europe, seat choices include group 0þ (birth to 13 kg) and rear-facing group 1 (9e18 kg) seats (Watson & Monteiro, 2009). Infants and toddlers should always ride in the back seat. A rear-facing child should never be restrained in front of an active air bag because this is likely to be fatal in the event of a crash (Williams & Croce, 2009). In some localities, it is illegal to place a rearfacing seat in the front seat of a vehicle with an active air bag. The safety seat should be installed with the safety belt or child restraint anchors locked tight in position so that the seat will not move more than an inch when pulled from side to side or from the front (NHTSA, 2007). The harness straps must be routed according to instructions and positioned snugly (i.e., do not allow any slack but do not press too hard into the child’s body), with the retainer (chest) clip positioned at armpit level. Harness straps should be routed at or below shoulders when rear facing to keep the child down and in the protective shell of the seat in the event of a crash. In the United States, seats are not routinely tethered when in the rear-facing position (NHTSA, 2007). However, throughout Europe, Australia, and Canada, seats are tethered in the rear-facing position (NHTSA, 2007). Top tethers provide a third point of attachment to the vehicle and decrease forward head movement in a crash by 4e6 in. (NHTSA, 2007). Among countries that do tether rearwardfacing seats, there is some dissention regarding the best method of tethering.

5.2.2. Stage 2: Forward-Facing Seats with Harness Systems Toddlers and preschoolers should ride forward facing from approximately age 2 years and at least 20 pounds to approximately age 4 and 40 pounds in a seat equipped with a harness system (NHTSA, 2007). Again, children should remain rear facing as long as possible to the maximum rearfacing weight limit for the seat. Similarly, it is important that children remain in safety seats with harness straps until at least 40 pounds or to the maximum weight limit for harness straps. Parents are dissuaded from graduating children to a booster seat until at least 40 pounds (or later, if possible, given higher harness weight and height limits). Harnesses are important for keeping a child’s small body properly positioned in the seat and distributing crash forces over the strongest parts of the child’s body (NHTSA, 2007). Toddlers and preschoolers have a variety of seat options with a multitude of height/weight specifications, including

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convertible seats that face rearward or forward, standard forward-facing seats, and combination toddler/booster seats that convert from a harnessed seat to a booster seat. The rear-facing weight range is generally 5e35 pounds for convertible seats in the United States. Upper harness weight limits for forward-facing configurations vary greatly by type of seat and range from 40 to 80 pounds in the United States. The safety restraint should be installed with the safety belt or child restraint anchors locked tight in position so that the seat will not move more than an inch when pulled from side to side or from the front (NHTSA, 2007). The use of a top tether, positioned snugly, is highly recommended (and required in many countries) due to the resulting decrease in forward movement and head excursion during a crash (NHTSA, 2007). The harness straps must be routed according to instructions and positioned snugly (i.e., do not allow any slack but do not press too hard into the child’s body), with the retainer (chest) clip positioned at armpit level. To keep the child positioned snugly in the seat during a crash, harness straps should be routed at or below shoulders when rear facing and at or above shoulders when forward facing.

5.2.3. Stage 3: Booster Seats Once children outgrow traditional safety seats, beltpositioning booster seats are recommended prior to transitioning to safety belts alone. Booster seats are used in combination with the vehicle’s lap/shoulder safety belt system. The odds of motor vehicle crash injury to children aged 4e8 years are 45% lower when riding in beltpositioning booster seats than when riding in seat belts alone (Arbogast et al., 2009). The booster seat’s primary function is to raise the child higher so the vehicle belt system fits correctly over strong bony parts of the body, with the lap portion low on the hips and the shoulder belt snug across the chest and shoulder (NHTSA, 2007). Without a booster seat, a safety belt’s positioning over soft and vulnerable parts of the body increases the risk of abdominal, neck, and spinal cord injuries (collectively known as “seat belt syndrome”) in a crash (Arbogast et al., 2009; NHTSA, 2007). Furthermore, the risk for brain injury is increased when a child places a poorly fitting shoulder belt behind his or her back for comfort because the child’s head is likely to strike his or her knees or the vehicle interior during a crash (Arbogast et al., 2009; Winston & Durbin, 1999). Despite the risks to children prematurely graduated to safety belts, studies have found that many parents of booster-age children do not own a booster seat, and most are misinformed about recommendations for booster seat use (Eby, Bingham, Vivoda, & Ragunathan, 2005; Greenspan, Dellinger, & Chen, 2010; Lee, Shults, Greenspan, Haileyesus, & Dellinger, 2008; Simpson, Moll,

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Young Children and “Tweens”

Kassan-Adams, Miller, & Winston, 2002; Winston, Chen, Elliott, Arbogast, & Durbin, 2004). Lags in policy further complicate the confusion for parents (Partners for Child Passenger Safety, 2007; Ross et al., 2004). Booster seats are recommended for use until a child grows to approximately 4 feet 9 inches tall and 80 pounds, and graduation to a belt is best determined by proper fit of the safety belt on the seated child (NHTSA, 2007). Specifically, children are not ready for an adult safety belt until they can (1) sit all the way back in the vehicle seat; (2) with knees bent comfortably at the edge of the seat; (3) the shoulder belt crossing the middle of the chest and resting at the shoulder (not the neck); (4) the lap belt fitting low and snug on the hip bones, touching the upper thighs (not the stomach); and (5) stay seated like this for the entire trip. Most children require a booster seat until at least 8 years of age, and many small-frame youngsters will require a booster long past the age of 8 years. For this reason, some countries commonly recommend the use of booster seats up to age 10 or even 12 years, unless the child surpasses height recommendations. As with all children younger than age 13 years, children in this age group should always ride in the back seat. Both high-back and no-back boosters are available. Highback boosters are useful in vehicles that do not have headrests or have low seat backs. Backless boosters are usually less expensive and are easier to move from vehicle to vehicle. Backless boosters can be safely used in vehicles with headrests and high seat backs (Arbogast et al., 2009). Many high-back boosters are actually combination seats that come with harnesses that can be used for smaller children and can then be removed for older children. Lap and shoulder belts are required with booster seats because the shoulder belt is necessary to minimize forward movement of the torso and keep the child in position (NHTSA, 2007). There are some alternatives for vehicles with only lap belts, including having shoulder belts installed in the vehicle, using a safety seat with a harness system that goes up to high weights (e.g., 80 pounds), or using a travel vest.

5.2.4. Stage 4: Lap/Shoulder Safety Belts Older children should travel in a lap and shoulder safety belt system once they outgrow a booster seat, which is usually after they reach approximately 80 pounds or grow to 4 feet 9 inches tall. Again, proper fit of the belt system is the best determining factor for the timing of transition. The back seat is recommended for children younger than age 13 years. “Tweens” (8- to 12-year-olds) and young teens (13- to 15-year-olds) are at high risk for crash injury given a propensity toward inconsistent restraint use and increased front seat positioning compared to their younger peers.

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Studies of tween and young teen belt use have reported that the rate of belt use is frequently well below rates observed for adults and younger children (Agran, Anderson, & Winn, 1998; Greenspan et al., 2010; Will, Dunaway, Lorek, Kokorelis, & Sabo, 2010). Many tweens only sit in the back if told to do so by their parents, and most tweens in the United States (73%) sit in the front passenger seat when they are the sole passenger (Durbin, Chen, Elliott, & Winston, 2004). In the United States, twice as many 12- to 14-year-old children are unrestrained compared to 0- to 4-year-old children (Berg et al., 2000). Tweens and young teens are also at greater risk when traveling with teen drivers because they are less likely to wear their safety belt or sit in the back seat (Winston, Kallan, Senserrick, & Elliott, 2008). Tweens and young teens are at an ideal age for intervention because they are highly impressionable and very susceptible to both peer and parent influences (Jennings, Merzer, & Mitchell, 2006). Furthermore, they are in a time of transition and are just starting to make their own decisions and develop safety habits. Unfortunately, the traffic safety field has few programs specifically targeting this age group, and as such, a significant gap exists in traffic safety programming.

5.2.5. Children with Special Health Care Needs When considering developmentally appropriate restraints, it should be noted that some children may require special seats or positioning for a variety of medical conditions (O’Neil, Yonkman, Talty, & Bull, 2009). These sometimes include, but are not limited to, prematurity, low birth weight, orthopedic conditions, casts (including hip spica casts), cerebral palsy and other neuromuscular disorders, autism and related disorders, and Down’s syndrome. Whenever possible, a caregiver should use a standard child restraint system to transport children with special health care needs. Lateral support and positioning can be achieved with rolled towels or blankets positioned around the child (O’Neil, Yonkman, et al., 2009). In some cases, the use of specialized medical seats may be necessary. Medical equipment should be secured to the floor or under the seat in front of the child. Caregivers should seek recommendations for safe transportation from physicians, nurses, and physical, occupational, or rehabilitation therapists (O’Neil, Yonkman, et al., 2009).

5.3. Rear Seating Because frontal collisions are the most common type of crash, rear seating offers protective effects for children because they are farthest from the most common point of impact. Although age-appropriate restraints offer relatively more safety benefit than does rear seating, rear seating

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offers independent and additive safety protections in a crash (Berg et al., 2000; Braver et al., 1998; Durbin et al., 2005; Lennon et al., 2008). Children (age 0e12 years) in the front seat are at 40% greater risk of injury compared to those seated in the back (Durbin et al., 2005). For 0- to 4-year-olds, death risk is two times greater in the front seat versus the back, and risk for serious injury is 60% greater (Lennon et al., 2008). For children younger than 1 year of age, the death risk is four times greater in front versus back seats (Lennon et al., 2008). Both restrained and unrestrained children are at a lower risk of dying in rear seats (Braver et al., 1998; Durbin et al., 2005; Lennon et al., 2008). In the United States, one-third of children younger than age 13 years sit in the front seat (Durbin et al., 2004). Incidence of riding in the front seat increases with age, with most children older than age 8 years being seated in the front seat (Durbin et al., 2004; Greenspan et al., 2010).

5.3.1. Dangers of Supplemental Restraint Systems Supplemental restraint systems (air bags) have increasingly been incorporated in passenger vehicles in many areas of the world since the 1990s. Supplemental restraint systems operate by crash-detection sensors and inflate instantly to provide a barrier between the occupant and objects in the direction of the collision. Air bag systems are intended to supplement the use of safety belts and restraints, which are necessary to keep the occupants in position as the air bags inflate and provide protection. For adults, air bags are associated with a reduced risk of mortality and decrease in injury severity (Williams & Croce, 2009). Despite their protective benefits for adults, frontal air bags pose great risk to children seated directly in front of them due to the speed and force with which they deploy (Williams & Croce, 2009). Rear-facing children, especially, can endure fatal head, neck, and spinal cord injuries from frontal air bags (NHTSA, 2007; Williams & Croce, 2009). In response to the dangers to children, small adults, and unrestrained occupants, frontal air bag designs were depowered in 1998 to deploy at slower speeds. Although the redesign successfully reduced air bag-related mortality risk to children, frontal air bags remain unsafe for children (Arbogast, Durbin, Kallan, Elliott, & Winston, 2005; Williams & Croce, 2009). Thus, children younger than age 13 years should never be seated in front of an active air bag. To further mitigate risk to children, advanced air bags have been introduced on some vehicle models that automatically turn off when a child or small adult is detected in the seat. Side-impact, curtain, and rollover air bags pose less risk than frontal air bags to children due to the manner in which they deploy. Additional research is needed to determine the relative risks and benefits of side-impact and overhead air

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bags to children. Tentatively, it appears they do not pose great risk and may provide extra protection, provided the child is not leaning against them or otherwise out of position when they inflate (NHTSA, 2007).

5.4. Additional Challenges and Barriers to Occupant Protection 5.4.1. Factors Associated with Children’s Restraint Use A number of factors have been found to correlate with nonuse of appropriate restraints for children. Appropriate restraint use for children is decreased (1) among older children; (2) with increasing number of occupants; (3) in older vehicles, pickup trucks, and large vans; (4) in rural areas; (5) with unbelted drivers; (6) with young drivers; (7) with drivers older than age 60 years; and (8) with alcohol use (Agran et al., 1998; Eby et al., 2005; Greenspan et al., 2010; Lee et al., 2008; Winston et al., 2008). U.S. researchers have also noted differential use by other demographic subgroupings, including race and ethnicity (Lee et al., 2008). Driver restraint use is the strongest predictor of children’s use of appropriate restraints (Agran et al., 1998; Eby et al., 2005).

5.4.2. Misuse Studies in the United States have shown that approximately three out of four safety restraints are unintentionally misused, and critical misuse of a restraint can reduce its effectiveness against severe injuries (Arbogast & Jermakian, 2007; Decina & Lococo, 2005, 2007; Dukehart et al., 2007; O’Neil, Daniels, et al., 2009). Misuse is caused by a number of factors. As evidenced previously, the age and weight stipulations, variety of seat options, and vehicle placement stipulations vary widely. Also, at any given time, there are more than 100 different models of child safety seats, approximately 300 models of passenger vehicles, and at least 27 different seat belt systems. Therefore, it is not surprising that fitting a safety seat in a vehicle is often confusing to parents. Certain installation problems may seem like minor adjustments to parents, but they are vital for preventing ejection and for distributing crash forces across the strongest points of children’s bodies. The three most common errors parents make when installing their children’s seats are failure to (1) attach the seat tightly to the vehicle, (2) fasten the harness tightly, and (3) position the harnesses correctly (Decina & Lococo, 2005; Dukehart et al., 2007). Failure to secure a child seat tightly and properly to the vehicle permits excessive movement of the restraint in a crash and often results in head injuries as the child contacts parts of the vehicle’s interior (NHTSA, 2007).

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To combat misuse, three-point universal safety seat attachment mechanisms (e.g., Lower Anchors and Tethers for Children or universal anchorage systems) are increasingly incorporated on new vehicles and safety seats manufactured in many areas of the world (NHTSA, 2007). Allowing parents to simply “click” the safety seat in place, the safety seat anchor systems are meant to combat one of the most common and serious installation errorsdfailing to attach the safety restraint tightly to the vehicle. However, only those caregivers with both a new vehicle and a new safety seat benefit from the mechanism, and the system does not address the many other errors that can occur when positioning a child in a safety seat. Furthermore, evidence is mounting that universal attachment systems are still prone to misuse (Arbogast & Jermakian, 2007; Decina & Lococo, 2007). Misuse of restraints is not limited to traditional safety seats. The misuse rate for belt-positioning booster seats is between 40 and 65%, and is related primarily to misrouting of the safety belt (Decina & Lococo, 2005; O’Neil, Daniels, et al., 2009). Furthermore, a study in the United States found that one-fifth of 8- to 12-year-old children did not use shoulder belts, placing them at a 1.8% higher risk of injury compared to those using both lap and shoulder belts (Garcia-Espana & Durbin, 2008). Common misuses of booster seats and safety belts alike include the shoulder belts being positioned behind the child’s back, under the child’s arm, over the booster seat armrest, not at midshoulder position, and too loosely (Decina & Lococo, 2005; O’Neil, Daniels, et al., 2009).

5.4.3. Caregiver Na€ıvvete´ and Complacency Misuse and non-use of safety restraints involves a complex interplay of determinants, including, but not limited to, perception of risk, parenting style, and personal beliefs (Bingham, Eby, Hockanson, & Greenspan, 2006; Simpson et al., 2002; Will, 2005; Will & Geller, 2004; Winston, Erkoboni, & Xie, 2007). Safety advocates are presented with both challenges and opportunities for promoting proper restraint use. On the one hand, recommending the use of a safety restraint is a relatively straightforward recommendation (compared to, for example, losing weight or quitting smoking), which results in dramatic improvements in safety. On the other hand, caregivers often struggle with low perceptions of risk, poor recognition of restraint system effectiveness, flawed understanding of crash forces, and a number of other competing factors such as child protest and legal loopholes. Motor vehicle travel is familiar, occurs in a wellunderstood system, permits one to feel in control while behind the wheel, has the added perk of convenience, and disperses injuries and deaths over time and space. Research has shown that all of these characteristics of motor vehicle travel lead to reduced perception of risk (Sandman, 1991;

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Slovic, 1991; Will & Geller, 2004). Consequently, caregivers struggle with an immunity fallacy, or a reduced perception of personal or familial risk for injury in a crash (Will, 2005). This is unfortunate because risk communication research and other health behavior theories demonstrate that recognition of personal vulnerability to a hazard is a necessary prerequisite to behavior change (Bandura, 1986; Sandman, 1991; Slovic, 1991; Weinstein, 1988). Stage models such as the precaution adoption process model (PAPM; Weinstein, 1988) and transtheoretical model (Prochaska, 1979; Prochaska, Johnson, & Lee, 1998) remind us that it is important to examine whether or not a health message is appropriate for the audience. Stage models provide a framework for understanding how individuals progress toward, adopt, and maintain behavior change. For instance, the PAPM includes both an “unaware” stage, in which individuals are not informed of the problem, and an “unengaged” stage, in which individuals can be fully informed of the problem but not motivated to do anything about it (Weinstein, 1988). This is often due to low recognition of personal vulnerability. The combination of many factors leaves many caregivers in the unengaged stage of the PAPM. A key assumption of stage models is that interventions should be matched to the audience’s stage (or readiness for change) to achieve maximum results (Prochaska, 1979; Prochaska et al., 1998; Weinstein, 1988). Education-only messages common in child passenger safety are only appropriate for caregivers in the unaware stage of the PAPM. Unengaged caregivers are unlikely to read a brochure (even one handed to them), watch a video on their own, or attend a safety event for the issue. Information and education is absolutely necessary and beneficial, especially considering so many caregivers are uninformed regarding safety recommendationsdbut information is only effective if parents attend to it and find it personally applicable. If much of the audience is in the unengaged stage, then our task as public health professionals is to design better interventions to stir attention and motivate action (not just educate). This will not only mean enhancing the message in accordance with theories of behavior change and risk communication but also changing the intervention approach from passive (where caregivers must seek out the intervention) to more progressive (where caregivers are intervened upon) (Trifiletti, Gielen, Sleet, & Hopkins, 2005; Will, Dunaway, Kokorelis, Sabo, & Lorek, in press).

5.5. Recommended and Empirically Supported Interventions for Child Passenger Safety Recommended and empirically supported interventions for child passenger safety include laws, enforcement, and

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educational campaigns that are enhanced with an intervention modality to improve motivation (Dellinger, Sleet, Shults, & Rinehart, 2007; Dinh-Zarr et al., 2001; Turner, McClure, Nixon, & Spinks, 2005; Zaza, Sleet, Thompson, Sosin, & Bolen, 2001). A fairly comprehensive review of interventions for occupant protection by level of evidence was completed for the Handbook of Injury and Violence Prevention (Dellinger et al., 2007). Although Dellinger and colleagues’ main conclusions are summarized here, the reader is referred to their chapter for additional details on specific intervention strategies and studies included in their review. Laws regulating child passenger safety are among the most effective mechanisms for decreasing childhood crash injuries among the masses (Bingham et al., 2006; Dellinger et al., 2007; Dinh-Zarr et al., 2001; Partners for Child Passenger Safety, 2007; Winston, Kallan, Elliott, Xie, & Durbin, 2007; Zaza et al., 2001). However, laws must be well publicized, comprehensive, and understood (Sleet, Schieber, & Gilchrist, 2003). Combining results from a variety of intervention studies, child safety seat laws are associated with a median 13% increase in child safety seat use and a median 17% decrease in fatal and nonfatal injuries (Dellinger et al., 2007). Safety belt laws are also associated with increased use (median of 33%) and decreased fatalities and injuries (median decreases of 9 and 2%, respectively) (Dellinger et al., 2007). Primary enforcement laws (allowing an officer to stop a vehicle solely for a restraint use violation) have stronger evidence of support than do secondary enforcement laws (Dellinger et al., 2007; Dinh-Zarr et al., 2001). Enhanced enforcement programs combine enforcement of restraint use violations at specific locations and times with pre-publicity about the enforcement effort (Dellinger et al., 2007). Such programs, including the U.S.-based Click It or Ticket, are associated with reductions in injury rates and a median 16% increase in observed safety belt use (Dellinger et al., 2007). Enhanced education campaigns combine education with an intervention modality to improve motivation. Recommendations with strong or sufficient evidence for effectiveness include education plus enforcement, education plus incentives, and education plus distribution programs (Dellinger et al., 2007). Communitywide information plus enforcement campaigns use mass media mailings and public information displays to promote use, combined with special enforcement strategies such as checkpoints to enforce child seat use laws (Dellinger et al., 2007). Education plus enforcement strategies are associated with a median increase in child safety seat use of 12% (Dellinger et al., 2007). Education plus incentive programs provide rewards to children and parents for purchasing and using child safety seats, coupled with education regarding child passenger safety (Dellinger et al., 2007; Ehiri et al., 2006). Education plus incentive programs are frequently implemented in day care centers and communitywide, and they are associated

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with a median 10% increase in child safety seat use (Dellinger et al., 2007). Education plus distribution programs provide education and safety seats to parents through loans, low-cost rentals, or giveaways. Education and distribution programs are associated with a median 23% increase in possession of and proper use of safety seats (Dellinger et al., 2007). Successful education and distribution programs have been implemented by a variety of agencies, including hospitals and clinics, as part of postnatal home visits, and by auto insurance companies (Dellinger et al., 2007; Ehiri et al., 2006; Johnston, Britt, D’Ambrosio, Mueller, & Rivara, 2000; Kedikoglou et al., 2005; King et al., 2005). Although there is currently not a sufficient body of evidence to draw definitive conclusions, empirically supported education plus risk communication programs are also emerging as a fourth type of enhanced education campaign. The emerging education plus risk communication programs are solidly grounded in theory and incorporate targeted components for key health behavior concepts such as risk perception, vulnerabilities, fear, self-efficacy, and readiness for change (Erkoboni, Ozanne-Smith, Rouxiang, & Winston, 2010; Will, Sabo, & Porter, 2009; Winston, Erkoboni, et al., 2007). There is insufficient evidence concerning the effectiveness of education-only programs, but these programs may increase knowledge and thus be a predisposing factor for other interventions (Dellinger et al., 2007; Zaza et al., 2001). Hands-on education at child passenger safety checkup events has been shown to increase correct use of safety restraints (Dukehart et al., 2007). Finnegan and Viswanath (1997) recommend using multiple strategies to aim for synergy among interventions because each strategy has weaknesses. For instance, groupintensive interventions plus mass media can help to overcome knowledge gaps, particularly when media coverage is designed to increase perception of risk. A number of multicomponent community-based interventions have been employed, and these often combine multiple empirically supported strategies (e.g., legislation with targeted or mass media education) (Ekman, Welander, Svanstrom, & Schelp, 2001; Turner et al., 2005). Other interventions have found success with school-based multicomponent programming (Floerchinger-Franks, Machala, Goodale, & Gerberding, 2000; Will et al., 2010; Williams, Wells, & Ferguson, 1997). However, few comprehensive community-based studies have employed sound designs and rigorous evaluation methods (Klassen, MacKay, Moher, Walker, & Jones, 2000; Turner et al., 2005).

6. CONCLUSIONS Children are especially susceptible to road traffic injury, and morbidity and mortality rates throughout the world

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reflect this vulnerability. The interplay of a dangerous roadway environment and children’s developmental limitations has led to road traffic injury being one of children’s greatest threats to health and well-being. In the push toward global intervention on behalf of road traffic injury among children, ample logistical questions are yet to be answered. The United Nations and World Health Organization note a number of pertinent research and policy needs that are important for the advancement of global efforts (Peden et al., 2008; Peden et al., 2004; World Health Organization, 2009, 2010). Chief among these is a prioritization of children’s needs when planning transport projects. Also important are implementing engineering measures, improving vehicle design standards, increased use of safety equipment, enacting legislation and standards, developing education and skills, and improving emergency and trauma care (Peden et al., 2008). More frequent and sophisticated applications of behavioral and social science theory, more rigorous evaluation methodologies, and increased funding for these scientific investigations are needed in the field of injury control. Focused global coordination and intervention is urgent and paramount to reverse the trend toward road traffic injury becoming the number one cause of death and disability for children.

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Johnston, B. D., Britt, J., D’Ambrosio, L., Mueller, B. A., & Rivara, F. P. (2000). A preschool program for safety and injury prevention delivered by home visitors. Injury Prevention, 6(4), 305e309. Kedikoglou, S., Belechri, M., Dedoukou, X., Spyridopoulos, T., Alexe, D. M., Pappa, E., et al. (2005). A maternity hospital-based infant car-restraint loan scheme: Public health and economic evaluation of an intervention for the reduction of road traffic injuries. Scandinavian Journal of Public Health, 33, 42e49. King, W. J., Leblanc, J. C., Barrowman, N. J., Klassen, T. P., BernardBonnin, A. C., Robitaille, Y., et al. (2005). Long term effects of a home visit to prevent childhood injury: Three year follow up of a randomized trial. Injury Prevention, 11, 106e109. Klassen, T. P., MacKay, J. M., Moher, D., Walker, A., & Jones, A. L. (2000). Community-based injury prevention interventions. Future of Children, 10(1), 83e110. Kopits, E., & Cropper, M. (2005). Traffic fatalities and economic growth. Accident Analysis and Prevention, 37(1), 169e178. Lee, K. C., Shults, R. A., Greenspan, A. I., Haileyesus, T., & Dellinger, A. M. (2008). Child passenger restraint use and emergency departmentdReported injuries: A special study using the National Electronic Injury Surveillance System-All Injury Program, 2004. Journal of Safety Research, 39(1), 25e31. Lennon, A., Siskind, V., & Haworth, N. (2008). Rear seat safer: Seating position, restraint use and injuries in children in traffic crashes in Victoria, Australia. Accident Analysis and Prevention, 40, 829e834. Llewellyn, S. T. (2000). Senators propose better testing for child safety seats. AAP News, 16(5), 4ea. Mathers, C., & Loncar, D. (2005). Updated projections of global mortality and burden of disease, 2002e2030: Data sources, methods, and results. Geneva: World Health Organization. National Highway Traffic Safety Administration. (2007). National child passenger safety certification training program student manual. (No. DOT HS 810 731). Washington, DC. National Highway Traffic Safety Administration. (2010). Traffic safety facts: Children. (No. DOT HS 811 387). Washington, DC: Author. O’Neil, J., Daniels, D. M., Talty, J. L., & Bull, M. J. (2009). Seat belt misuse among children transported in belt-positioning booster seats. Accident Analysis and Prevention, 41(3), 425e429. O’Neil, J., Yonkman, J., Talty, J., & Bull, M. J. (2009). Transporting children with special health care needs: Comparing recommendations and practice. Pediatrics, 124, 596e603. Partners for Child Passenger Safety. (2007). Do laws make a difference. Philadelphia: Children’s Hospital of Philadelphia. Peden, M., Oyegbite, K., Ozanne-Smith, J., Hyder, A., Branche, C., & Rahman, A. et al. (Eds.). (2008). World report on child injury prevention. Geneva: World Health Organization. Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A., & Jarawan, E. et al. (Eds.). (2004). World report on road traffic injury prevention. Geneva: World Health Organization. Prochaska, J. O. (1979). Systems of psychotherapy: A transtheoretical analysis. Homewood, IL: Dorsey. Prochaska, J. O., Johnson, S., & Lee, P. (1998). The transtheoretical model of behavior change. In S. A. Shumaker, E. B. Schron, J. K. Ockene, W. L. McBee, & 2nd ed. (Eds.), The handbook of health behavior change (pp. 59e84). New York: Springer. Rice, T. M., & Anderson, C. L. (2009). The effectiveness of child restraint systems for children aged 3 years or younger during motor vehicle

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collisions: 1996 to 2005. American Journal of Public Health, 99(2), 252e257. Ross, T. C., Colella, J. M., Korn, A. R., Mickalide, A. D., Paul, H. A., & Eichelberger, M. R. (2004). Closing the gap across the map: A progress report on Safe Kids’ efforts to improve child occupant protection laws. Washington, DC: Safe Kids Worldwide. Safe Kids Worldwide. (2004). Safe Kids Worldwide. http://www. safekidsworldwide.org. Accessed August 18, 2004. Sandman, P. M. (1991). Risk ¼ hazard þ outrage: A formula for effective risk communication. (Videotaped presentation for the American Industrial Hygiene Association). New Brunswick, NJ: Environmental Communication Research Program, Cook College, Rutgers University. Simpson, E. M., Moll, E. K., Kassan-Adams, N., Miller, G. J., & Winston, F. K. (2002). Barriers to booster seat use and strategies to increase their use. Pediatrics, 110, 729e736. Sleet, D. A., Schieber, R. A., & Gilchrist, J. (2003). Health promotion policy and politics: Lessons from childhood injury prevention. Health Promotion Practice, 4(2), 103e108. Slovic, P. (1991). Beyond numbers: A broader perspective on risk perception and risk communication. In D. G. Mayo, & R. D. Hollander (Eds.), Acceptable evidence: Science and values in risk management (pp. 48e65). New York: Oxford University Press. Toroyan, T., & Peden, M. (Eds.). (2007). Youth and road safety. Geneva: World Health Organization. Trifiletti, L. B., Gielen, A. C., Sleet, D. A., & Hopkins, K. (2005). Behavioral and social sciences theories and models: Are they used in unintentional injury prevention research? Health Education Research, 20(3), 298e307. Turner, C., McClure, R., Nixon, J., & Spinks, A. (2005). Communitybased programs to promote car seat restraints in children 0e16 years: A systematic review. Accident Analysis and Prevention, 37, 77e83. United Nations. (2003). Global road safety crisis: Report of the secretary general. (General Assembly Resolution 57/309). New York. Watson, E. A., & Monteiro, M. J. (2009). Advise use of rear facing child car seats for children under 4 years old. British Medical Journal, 338, 1496e1497. Weinstein, N. D. (1988). The precaution adoption process. Health Psychology, 7(4), 355e386. Will, K. E. (2005). Child passenger safety and the immunity fallacy: Why what we are doing is not working. Accident Analysis and Prevention, 37, 947e955. Will, K. E., Dunaway, K. E., Kokorelis, D. A., Sabo, C. S., & Lorek, E. J. (in press). Challenges and opportunities for promoting booster seat use: Progressive dissemination of a high-threat message. Health Promotion Practice Will, K.E., Dunaway, K.E., Lorek, E.J., Kokorelis, D.A., & Sabo, C. S. (2010, February). Development and evaluation of a school-based transportation safety program for "tweens." Paper presented at the 10th annual Scientific Meeting of the American Academy of Health Behavior, Clearwater Beach, FL. Will, K. E., & Geller, E. S. (2004). Increasing the safety of children’s vehicle travel: From effective risk communication to behavior change. Journal of Safety Research, 35, 263e274. Will, K. E., Sabo, C. S., & Porter, B. E. (2009). Evaluation of the Boost ‘em in the Back Seat program: Using fear and efficacy to

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Winston, F. K., Kallan, M. J., Elliott, M. R., Xie, D., & Durbin, D. R. (2007). Effect of booster seat laws on appropriate restraint use by children 4 to 7 years old involved in crashes. Archives of Pediatric and Adolescent Medicine, 161, 270e275. Winston, F. K., Kallan, M. J., Senserrick, T. M., & Elliott, M. R. (2008). Risk factors for death among older child and teenaged motor vehicle passengers. Archives of Pediatric & Adolescent Medicine, 162(3), 253e260. World Health Organization. (2009). Global status report on road safety: Time for action. www.who.int/violence_injury_prevention/road_ safety_status/2009. World Health Organization. (2010). Global plan for the Decade of Action for Road Safety 2011e2020. Geneva. Zaza, S., Sleet, D. A., Thompson, R. S., Sosin, D. M., & Bolen, J. C. (2001). Reviews of evidence regarding interventions to increase use of child safety seats. American Journal of Preventive Medicine, 21 (4 Suppl.), 31e47.

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Chapter 23

Young Drivers Patty Huang* and Flaura Koplin Winston*, y *

Center for Injury Research and Prevention and Division of Child Development and Rehabilitation Medicine at The Children’s Hospital of Philadelphia, Philadelphia, PA, USA, y Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA

1. INTRODUCTION In the United States and other developed countries, motor vehicle crashes are the leading cause of death and acquired disability for adolescents. According to the Organisation for Economic Co-operation and Development (OECD) and European Conference of Ministers of Transport, although drivers younger than age 25 years only comprise one-tenth of the population of OECD countries, they account for more than a quarter of fatally injured drivers, and for every 10 young driver fatalities, an additional 13 passenger or other road users die in the same crashes (OECD, 2006). The role of behavior change interventions in traffic injury prevention has waxed and waned during the past decades. Early efforts in traffic injury prevention focused on the role of behavior principally via driver education (Bonnie, Fulco, & Liverman, 1999; Mayhew, 2007). Much of traffic safety and driver education content was based more on educated intuition and subject matter expertise rather than on scientific evidence, and these efforts underwent little evaluation. When these interventions were evaluated, they were mostly found to be ineffective, and multiple initiatives in driver education were all but abandoned (Mayhew & Simpson, 1996; National Highway Traffic Safety Administration (NHTSA), 1994). These early challenges in the promotion of safe driving behaviors resulted in reduced reliance on individual behavior change and education/training strategies and more emphasis on regulation, laws, and enforcement (Bonnie et al., 1999). One of the key challenges in ensuring road traffic safety among the young, from infancy to young adulthood, is that we must anticipate and design according to the range of safety behaviors, which for children rapidly evolve according to physical, social, emotional, and cognitive developmental stages. Therefore, interventions to reduce road traffic injuries among the young must take into account the evolving complexity of brainebehavioresocial context interactions from birth to young adulthood. The substantial increase in motor vehicle crash injury risk emerges as teens begin to drive and take rides with peers (Winston, Kallan, Senserrick, & Elliot, 2008). In adolescence, Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10023-2 Copyright Ó 2011 Elsevier Inc. All rights reserved.

teen drivers start as learners who are highly dependent on adult supervisors who teach, serve as role models, help to manage the behavior of any passengers within the car, and provide a second “set of eyes” looking for hazards on the road. Within just a year of their first driving experience, most teens are licensed as independent drivers with the primary responsibility for the driving task as well as managing passenger behavior and interacting with other road users (Winston & Senserrick, 2006a). Successful development of driving skills, expertise, and competencies, including psychomotor, cognitive, and perceptual proficiencies, requires a balance between safety limits and the freedom to explore and test the teen’s ability. Although novice teen drivers can acquire basic operational driving skills, a watershed of new research reveals that biological and cognitive factors inherent to the adolescent developmental period may affect the capacity of young persons to perform safe driving (and occupant protection) behaviors effectively. This new science provides not only a challenge but also cautious optimism, revealing that novice teen drivers are not “defective” or “deficient” adult drivers. Rather, these young people are undergoing an explosive period of physical, social, emotional, and behavioral development within an expanding environmental context. During adolescence, habits and patterns of driving are being sculpted and embedded, and potential exists for promoting adoption of safe rather than unsafe patterns. Although we recognize the global importance of young driver crashes, we limit this chapter to the U.S. context and focus on novice teenage drivers rather than all young adults. The chapter is organized into four sections: the epidemiology of teen driving in the United States, including risk and protective factors; developmental and psychosocial considerations for teen drivers; the impact of developmental disabilities on teen driving; and recommendations for developing evidence-based interventions linked to these teen-specific behavioral characteristics. Although important initiatives emerge from a range of disciplines, including medicine, engineering, and law, the focus of this chapter is on the behavioral contributions to 315

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safety that can be made by the teen drivers, their peer passengers, their parents, and society. It is important to remember that individual behavior change is only one piece of a comprehensive strategy. Adoption of safe behaviors in conjunction with improvements in vehicle safety, roads, and enforcement of laws will produce the largest degree of success.

2. EPIDEMIOLOGY OF TEEN DRIVING IN THE UNITED STATES: RISK AND PROTECTIVE FACTORS FOR CRASHES In 2007, there were 13.2 million licensed young drivers between the ages of 15 and 20 years in the United States, which is an increase of 4.8% since 1997 (NHTSA, 2008a). Although young drivers represented 6.4% of the total of 205.7 million licensed drivers, 13% (6982) of all drivers involved in fatal crashes (55,681) were young drivers ages 15e20 years, and 15% (1,631,000) of all drivers involved in police-reported crashes (10,524,000) were young drivers (NHTSA, 2007). Motor vehicle crashes resulted in 3174 fatalities and more than 250,000 injuries among 15- to 20-year-old drivers. An additional 4476 people died in these crashes. Although young people from ages 15 to 24 years represent only 14% of the U.S. population, they account for 30% ($19 billion) of the total costs of motor vehicle injuries among males and 28% ($7 billion) of the total costs of motor vehicles among females (Centers for Disease Control and Prevention, 2009). Fortunately, the majority of teen drivers do not crash. According to the National Young Driver Survey, a nationally representative survey of teens in public high schools in the United States, 12% of teens reported any crash as a driver in the past 12 months and 6% reported a severe crash as a driver

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in their lifetime (Ginsburg, Durbin, Garcia-Espana, Kalicka, & Winston, 2009). In addition, current population-based novice teen driver crash rates are lower than they were in the 1970s, with the greatest reductions experienced by male teens (1978 peak: 9.1 fatal crashes per 100,000 population; 1983, declined to 6.0 fatal crashes per 100,000 population) (Shope & Bingham, 2008). However, further analysis reveals that there has been no reduction in fatal crash rates among teens since 1983 and that the rates since 1992 have increased. When driving exposure is taken into account (crash rates by vehicle miles traveled), a similar trend emerges in which most of the reductions in crash rates occurred before 1992, with males experiencing an overall decreasing trend and females experiencing little overall change since 1989.

2.1. Age Compared to other age groups, young drivers have the highest rates of fatal crashes per vehicle miles traveled, with 1.83 times the average rate for all ages (Figure 23.1). However, the risk is age dependent: Among young drivers, with each increase in year of age, crash rates decline. By examining month-to-month changes in collisions among new drivers, Mayhew, Simpson, and Pak (2003) demonstrated that crash rates decline most dramatically during the first 6 months of nonsupervised driving, with certain crash types (run-off-the-road, singlevehicle, and night and weekend) demonstrating even more pronounced declines. It is important to note that independent, unsupervised teen drivers are 10 times as likely to be involved in an accident compared to teen drivers who are supervised by an adult driver (Gregersen, Nyberg, & Berg, 2003; Mayhew et al., 2003). In fact, teens are at their lowest lifetime risk of crashing while supervised by an adult.

FIGURE 23.1 Fatal crash involvement by age (number of fatal crashes per 100,00 licensed drivers), United States, 2008. Source: Reprinted from NHTSA (2008b).

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Teens are at risk not only as drivers but also as passengers. A study of U.S. crash data from 2000e2005 (Winston et al., 2008) found that, on average, 424,000 passengers ages 8e17 years were in tow-away crashes each year and experienced a fatality rate of 4 per 1000 crashes (Figure 23.2). Nearly three-fourths as many passengers (ages 8e17 years) were in crashes with 16- to 18-year-old drivers as with all adult drivers older than 24 years, with double the passenger fatality rate.

2.2. Gender Across all ages, males have higher fatal crash rates on a population basis than do females, but this difference is more pronounced among young drivers. However, new methods by Shope take into account the lower exposure to driving of females versus males in terms of person-miles driven (PMD) and demonstrate that female teens are at more risk than male teens. In Michigan from 1990 to 2001, male teens aged 16e19 years had 14.9 crashes per 100,000 PMD, whereas female teens had 22.5 crashes per 100,000 PMD. Similarly, casualty (fatal and nonfatal injury) crash rates per PMD were 4.1 and 7.0, respectively, for male and female drivers aged 16e19 years (Shope & Bingham, 2008). 2500 Drivers aged ≥ 25y Drivers aged 20-24 y Drivers aged 18-19 y Drivers aged 16-17 y Drivers aged <16y

Unweighted No. of deaths

2000

1500

1000

2.3. Other Demographic Factors Differential crash experience based on age and gender demonstrates the importance of tailoring intervention goals and content to the target audience rather than relying on generalizations about young drivers. Further refinement and focus can come from investigation of other demographic factors. Academic performance, race/ethnicity, and rural residency can influence perception of safety factors, exposure to driving hazards, and driving behaviors among adolescents. For example, teens with a C or D grade point average have been shown to have a higher risk of citation and crash risk than teens with an A or B grade point average (McCartt, Shabanova, & Leaf, 2003). African American students and Hispanic students were more likely than white students to either rarely or never wear safety belts (CDC, 2009). Black and Hispanic youth also reported different perceptions of certain driving behaviors on safety (alcohol use and speeding), and they reported exposure to driving under the influence of alcohol more frequently than did white youth. Unlicensed drivers more frequently reported being of black race or Hispanic ethnicity; unlicensed status was also associated with hazardous driving behaviors such as drinking under the influence of alcohol and not using a safety belt (Elliott, Ginsburg, & Winston, 2008). Also, nonrural teens had lower exposure to intoxicated drivers than did rural teens (Ginsburg et al., 2008).

2.4. Modifiable Factors That Affect Risk of Crash, Injury, and Death

500

0

Involvement in more risky behaviors by male drivers may account for why males are more frequently involved in fatal crashes despite being involved in fewer crashes per PMD. In the National Young Driver Study, which examined exposure to and perception of risky driving behaviors, girls generally viewed certain driving behaviors as having a greater effect on safety than did boys, and boys were more likely to report witnessing drivers smoking marijuana, passengers instigating speeding, and inexperienced drivers (Ginsburg et al., 2008). In other sources, male high school students were less likely to wear safety belts than were female high school students (CDC, 2009) and more likely to violate driving rules (Maycock, 2002).

8

9

10

11 12 13 14 15 Age of passenger fatality.y

16

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FIGURE 23.2 Distribution of child passenger (aged 8e17 years) fatalities in tow-away crashed by passenger age and driver age group, 2000e2005. Source: Adapted from Archives of Pediatrics & Adolescent Medicine, March 2008, volume 162, page 256. Copyright Ó 2008 American Medical Association. All rights reserved.

A number of factors have been identified that alone or in combination increase a teen driver’s risk of crashing and can become targets for intervention. It is beyond the scope of this chapter to review all of these factors or the methods behind the underlying studies. For more detail, readers are referred to several excellent references for this information (Shults & Compton, 2007; Williams, Catalano, Mayhew, Millstein, & Shults, 2008; Winston & Senserrick, 2006b). The focus of this section is modifiable risk factorsdthose

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that can serve as behavioral objectives for targeted interventions. In the early 1980s, crash data revealed blood alcohol concentrations of 0.08% or greater in more than half of fatally injured drivers younger than the age of 21 years at the time of the crash (Williams & Mayhew, 2008). As a result, the young driver problem was attributed largely to alcohol. Careful analysis revealed a more complex story in which the risk factors for crashes changed with year of age: Alcohol became more of an issue for older teen and young adult drivers. For 16- and 17-year-old drivers, alcohol, when present, posed a substantial risk for crashing, but the more prevalent scenario was no alcohol involvement. Instead, inexperience and risk taking exacerbated by distractions were primary hazards. The primary reason why teens died in crashes was and continues to be lack of safety belt use.

2.4.1. Safety Belt Use Safety belts have consistently been shown to reduce injuries in the event of a crash, and safety belt use remains an important protective behavior for teen drivers and their passengers. Non-use of safety belts is a significant reason why teens are more likely to die or be seriously injured in crashes. Observed safety belt use is lowest for teens and young adults (ages 16e24 years) compared with other age groups (Ye & Pickrell, 2008). In 2007, safety belt use nationwide was 82%, whereas for those ages 16e24 years, the usage rate was 77%. Approximately two-thirds of teens who died in crashes were not wearing a safety belt at the time of the crash (NHTSA, 2006).

2.4.2. Driving Experience Although young age alone poses developmentally based challenges for young drivers, it is age in combination with inexperience that puts young drivers at substantial risk for crashing. According to the National Young Driver Survey, most teen drivers do not consider themselves inexperienced, perhaps because they associate having a license with experience (Ginsburg et al., 2008). Although 60% of teens believe that inexperience heavily influences driving safety, only 15% reported observing inexperienced driving among their peers. Regardless of age, the first months of independent driving pose the greatest risk for crashes, with a two-thirds reduction in crash risk after the first 500 miles (McCartt et al., 2003), but the magnitude of this risk is greater for teens than for older novice drivers. The 6-month period during which crash reductions are seen is too short to be explained by developmental maturity and suggests instead the impact of the initial learning curve. Even in countries that delay licensure to age 18 years, the first 2 years of

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driving are associated with a decline in crashes (Twisk, 1996). In England, crash risk declined by 30% in the first year of driving, regardless of age of licensure (Maycock, Lockwood, & Lester, 1991). Although it is not known how many hours of supervised practice are needed to gain experience, a study in Sweden with 18-year-old novice drivers found that an average of 120 h of supervised driving practice with an adult reduced post-licensure crashes, and a study in Australia with drivers ages 17 e24 years found that more than 42 h of supervised practice was associated with fewer crashes post-licensure (Gregersen et al., 2000; Ivers, Stevenson, Norton, & Woodward, 2006; Sagberg & Gregersen, 2005). McKnight and McKnight (2003) examined 2000 accident reports for teen drivers (ages 16e19 years) to determine behavioral contributors. The majority of the nonfatal crashes were attributed to driver errors that could be associated with inexperience, including inattention, inadequate visual search and hazard recognition, too high speed relative to conditions, and errors in emergency maneuvers. Contrary to prevailing beliefs, risky behaviors such as driving at high speeds were less common causes of crashes. Ulmer, Williams, and Preusser (1997) similarly reported driver error as the most prevalent cause of 16-year-old fatal crashes.

2.4.3. Driver Risk Taking Excess speed as a cause of fatal crashes is more common among teens than adult drivers (Figure 23.3), especially among young male drivers, and speeding-related crashes decrease with increasing driver age. More than one-third of fatal crashes involving male drivers ages 15e20 and 21e24 years involved speeding (NHTSA, 2008c). Speeding is a particular risk for inexperienced teen drivers in that it reduces the driver’s ability to steer safely around curves or hazards and extends the distance needed for a vehicle to come to a stop. Further exacerbating the risks of speeding, teens tend to follow vehicles too closely, reducing their available stopping distances. Speeding and following closely are more common in the presence of a male teen passenger (Simons-Morton, Lerner, & Singer, 2005).

2.4.4. Driver Distraction Because teen drivers are inexperienced and more prone to inattention than older drivers, distractions can be more problematic. Teens recognize the risks of distraction, although they may fail to take action to reduce the risk. Of the top 25 factors teens believe affect safety, 17 cause the driver to become distracted (Ginsburg et al., 2008). Eleven take the driver’s eyes and focus off the road (e.g., text messaging, talking on a cell phone, and teen passengers). Six other factors reduce the driver’s ability to concentrate

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FIGURE 23.3 Percentage of fatal crashes by contributing factor and driver age, United States, 2006. Source: Reprinted from NHTSA (2006).

on the road (e.g., driving while intoxicated, tired, or highly emotional). When teenage passengers present a distraction or negatively influence behaviors of teenage drivers, the potential crash affects not only the safety of the driver but also that of the passenger. Data from national highway safety studies suggest that the presence of passengers increases the likelihood of fatal injury among young drivers, and the relative risk of death among 16- and 17-year-old drivers with at least one passenger in the car is significantly greater than that for teenagers who drive alone (Chen, Baker, Braver, & Li, 2000). As the number of passengers in the car increases, the risk of death increases: One teen passenger doubles the risk of fatal crashes among 16- and 17-year-old drivers, whereas three or more passengers quadruple the risk. Passenger age is also an important factor: Drivers 16e20 years of age are more likely to cause a crash when driving with young passengers aged 12e24 years but less likely to cause a crash when driving with an adult and/or children (Aldridge, Himmler, Aultman-Hall, & Stamatiadis, 1999). Distractions from cell phone use and texting are a growing concern for young drivers. Drivers younger than 20 years of age had the highest proportion (16%) of distracted drivers involved in fatal motor vehicle crashes (Department of Transportation, n.d.). In 2007, 1.7% of drivers ages 16e24 years were observed visibly manipulating handheld electronic devices while driving. Distracted driving is discussed more in-depth later.

2.4.5. Driving while Intoxicated Drinking while driving is a less common cause of fatal crashes among teens compared to young adults, but when teens drink, they have a much higher risk of crashes and

fatalities than do other age groups. In 2007, 31% of young drivers ages 15e20 years who were fatally injured in crashes had a blood alcohol concentration of 0.01 g/dl or higher, indicating that they had been drinking (Figure 23.4). This represents a 5% reduction in alcohol-involved fatal young driver crashes between 1997 and 2007. Of note, alcohol involvement is more prevalent among young male driver fatalities than young female driver fatalities (26 vs. 14%, respectively). Although alcohol may be less prevalent in teen crashes, crash severity is higher when alcohol is present. In 2007, 3% of the 15- to 20-year-old drivers involved in propertydamage-only crashes had been drinking, whereas 23% of those involved in fatal crashes had been drinking. At the same blood alcohol concentration level, teen drivers are much more likely than older drivers to be involved in a fatal crash (Prato, Toledo, Lotan, & Taubman Ben-Ari, 2010). Higher drinking age and zero tolerance laws have been important factors in reducing teen and young adult crashes (Hingson, 2009). Research suggests that crash risk is even greater among teens who use illicit substances than among those who use cigarettes, alcohol, or marijuana (Dunlop & Romer, 2010).

2.4.6. Nighttime and Drowsy Driving Teens and young adults are at higher risk for drowsy driving and sleep crashes than are older adults. Young males also appear to be at higher risk than females (Giedd et al., 1999; NHTSA, n.d.; Prato et al., 2010). Although only approximately 15% of the total miles driven by 16- and 17-year-old drivers occur between 9 p.m. and 6 a.m., 40% of their fatal crashes occur during that time period (Williams & Preusser, 1997). When corrected for road traffic flow, young drivers (ages 18e24 years) were 5e10 times more likely to be involved in motor vehicle crashes when driving late at night

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FIGURE 23.4 Alcohol involvement in young driver crash fatalities.

100 Percentage of drivers with BAC > 0.01 g/dL

Vulnerable and Problem Road Users

90 80 70 60 50 40 30 20 10 0 15

16

17

18

19

20

Age in years

˚ kerstedt & Kecklund, 2001). A simulator-based study (A demonstrated that compared to older drivers, young drivers were more sleepy while driving at night and were less able to resist sleepiness (Lowdena, Anund, Kecklund, Peters, & ˚ kerstedt, 2009). Studies evaluating the effectiveness of A nighttime driving restrictions have shown the restrictions to successfully reduce the number of crashes among teenage drivers (Lin & Fearn, 2003). It is important to note that nighttime crashes are high risk but low exposure, with peaks in crashes occurring before and after school (Williams, 2006), other times of day when teens tend to be tired, and on weekends. Potential reasons for these findings are discussed later.

2.4.7. Parents, Parenting, and Restricting Access to the Vehicle Research suggests that parenting style is important with regard to teen driving behaviors. Parents who are described as authoritative (high support and high control) are associated with half the crash risk among their teenage children and are also associated with fewer risk-taking behaviors compared to parents who described as uninvolved (low support and low control) (Ginsburg et al., 2009). One specific action parents can take is to limit primary access to vehicles. Compared with teens who share access to vehicles, primary access is associated with significantly increased crash risk and prevalence of unsafe driving behaviors, such as speeding and cell phone use while driving (Garcia-Espana, Ginsburg, Durbin, Elliott, & Winston, 2009). Parenteteen agreements can serve as an effective structure for managing these risks (Simons-Morton, Hartos, & Beck, 2004; Simons-Morton, Hartos, Leaf, & Preusser, 2005). A body of work by Simons-Morton and others demonstrates promising results when parents limit

exposure to challenging driving situations (e.g., driving with passengers, at night, or on high-speed roads) for new drivers and gradually increase privileges over time. These agreements serve to reinforce and enhance graduated driver licensing laws.

2.5. Other Considerations 2.5.1. Teen Driving in Other Countries The problem of teen driving is generally greater in the United States than in most other developed countries for several reasons. Access to cars and driving differs among countries as well as within countries. For instance, European countries grant driver’s licenses at later ages than does the United States. Driving is generally less common in developing countries, and young drivers have less access to vehicles. Finally, the extent to which an area is ruralized or urbanized may also affect access and exposure to driving.

2.5.2. Teen Drivers Versus Young Adult Drivers Although this chapter does not address young adult drivers, we recognize that motor vehicle crashes remain a significant health issue for both teen and young adult drivers. Crash risk persists at a high level through the mid-20s, which is generally consistent with the finding that brain development continues until age 25 years. However, certain differences in driving epidemiology and risk factors among these two groups exist that support the notion that teenagers constitute a unique set of drivers. As discussed in greater detail later, research suggests that inexperience plays a larger role in the increased risk of crashes among teenagers. Teen drivers are also more likely to be influenced by peer pressure than are young adults (Steinberg, 2005). Finally, although teen drivers are more

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susceptible to the effects of alcohol than are any other age group of drivers, alcohol is less likely to be a contributing factor in crashes among 16-year-old drivers than among young adult drivers. Driving is a complex motor and cognitive skill, integrating multiple levels of cognitive processing and executive functioning. Although teens may exhibit the ability to execute the basic operational skills of driving, they need experience, practice, and time to attain mastery of the higher order cognitive tasks and decision-making processes involved in driving. The following section addresses why teenagers represent a distinct type of driver and discusses the biologicalebehavioralepsychosocial considerations concerning crashes involving teen drivers.

3. DEVELOPMENTAL AND PSYCHOSOCIAL CONSIDERATIONS FOR TEEN DRIVERS There is dramatic physical growth and change from birth through adolescence, in addition to changes in social context for children and teens, when a young person’s social world generally shifts from a relatively constricted and protected family-centric environment to a more expansive environment in which peers and nonfamilial adults play increasingly important roles (Steinberg, 2003). This section examines more closely the multitude of influences affecting teen driving behavior, including biological, behavioral, and psychosocial factors.

3.1. Driving as a Hierarchical Model of Competencies To understand how and why certain teen-specific factors affect driving, it is helpful to break down the complex task of driving into multiple levels of discrete but integrated skills. Russell Barkley suggests a model that conceptualizes driving as multidimensional, comprising three levels of competency (Barkley & Cox, 2007). These discrete and distinct levels must be coordinated and integrated for successful driving. Operational competency refers to the most basic skills of driving. These skills include paying attention to the road, reaction time, visual scanning of the environment, spatial perception, cognitive processing of various stimuli, and overall motor coordination. Tactical competency refers to the behaviors and decision-making skills used to drive in traffic. Examples of tactical skills include making decisions about driving speed, passing other vehicles, and when to yield. Finally, strategic competency refers to the decision and planning skills related to using the vehicle at any particular time, such as deciding the best time of day for a trip, assessing weather, and assessing the driver’s own condition (e.g., tired or intoxicated). Individuals with difficulty focusing, significant impulsivity, atypical

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observational skills, weaknesses in visual motor skill coordination and integration, emotional regulation difficulties, decreased self-awareness, or decreased mental flexibility may be at risk for unsafe driving behaviors based on poor operational, tactical, and strategic decision making.

3.2. The Model of a New Driver Along with considering the types of skills involved in the task of driving, we must also consider how the process of decision making occurs. The following model illustrates why a new, inexperienced driver may become involved in a more serious crash due to small but significant differences in reaction time. Figure 23.5 illustrates a schematic to conceptualize a “crash sequence model” that occurs between hazard detection and crash avoidance (Senserrick, 2006). Two scenarios are presented, each representing a different driver type while assuming similar traffic conditions. The first diagram portrays the scenario of an unimpaired experienced driver. Due to vigilant and attentive scanning of the traffic environment, the driver detects a potential hazard, recognizes the potential hazard as a true hazard, and then chooses and implements a response to avoid or minimize the severity of the crash. Based on previous research, this chain of decision-making events typically takes approximately 2 s (Olson & Sivak, 1986). The second diagram portrays the scenario of a new, inexperienced driver who is unimpaired and who is driving in the same traffic circumstances as in the first diagram (Senserrick, 2006). Inexperience renders the driver’s visual scanning to be less effective, and a potential hazard is detected fractions of a second later than detected by the driver in the first diagram. Inexperience also delays the driver’s decisionmaking process and execution of response by a fraction of a second longer than performed by the driver in the first diagram. These delays result in a reduction from the 2 s available for reaction and action. The inexperienced driver may not recognize the hazard and not execute a response, resulting in a crash. Alternatively, the driver may recognize the hazard but may have to rely on trial and error (as opposed to experience) to respond, and a crash can become more likely. Impaired hazard detection as has been noted in novice drivers can lead to crashes that could have otherwise been avoidable or made less severe among more experienced, unimpaired, and undistracted drivers.

3.3. BraineBehavioreSocial Context Influences on Teen Behavior: Debunking Myths With the publication of Hall’s Adolescence in 1904, scholarly work on the transition from childhood to adulthood

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Hazard Hazard Decision to detected recognized respond

Driving context

Scanning

Vulnerable and Problem Road Users

Response Last moment Chosen to avoid crash

Processing Processing Decision making Action

FIGURE 23.5 Schematic crash sequences model. Source: Adapted from Inquiry Prevention, Senserrick TM, volume 12, pages 56e60. Copyright Ó 2006, with permission from BMJ Publishing Group LTD.

2 seconds Experienced driver - unimpaired

Hazard Hazard detected recognized

Scanning

Decision to respond

Processing Processing

Last moment to avoid crash

Decision making

1.75 seconds Inexperienced driver - unimpaired

began. He labeled adolescence as a period of inevitable “storm and stress” that was universally experienced. For the first half of the twentieth century, observational studies prevailed that described teens and young adults from this deficit framework (Steinberg & Lerner, 2004). The prevailing theories of that time were largely untested. During the second half of the twentieth century, scientists demonstrated developmental plasticity and diversity in the adolescent experience, refuting the pessimistic view of the universality of an inevitable troubled adolescence (Lerner, 2005). Their studies demonstrated that all young people possess strengths and potential that can be nurtured within the contexts in which they live and develop. The adolescent’s growing context (from parents to peers, community, and society) can develop and support strengths and limit exposure to harmful risks. These new discoveries led to theoretically based programs to promote “positive youth development” (Lerner, 2005). Recent studies in brain development have further validated the new approach. Unfortunately, little of this science was applied to interventions to improve safe driving among adolescents. Many interventions continue to alienate adolescents, inaccurately portraying them as irrational and defective beings who need to be “scared straight.” Debunking these myths about teenagers directly not only impacts our understanding of the driving experience of adolescents but also affects approaches to developing driving safety interventions. The following sections address the neurobiological, cognitive, and psychosocial factors that influence teen driving. Within each section, commonly held myths are described and clarified.

3.4. The Teenage Brain and Driving Chapter 9 in this handbook, “Neuroscience and Young Drivers,” provides a detailed discussion about the brain as it applies to road traffic safety. In this section, salient points are reinforced as they apply to driving among teens. Of note, most associations between brain development among healthy adolescents and observed driving behaviors remain theoretical; however, understanding brain development during adolescence provides a context in which one can view the developing driving skills among teens. One myth about adolescence incorrectly asserts that brains are fully formed early in adolescence. Two decades ago, Giedd at the U.S. National Institute of Mental Health launched a longitudinal effort to map the brain’s developmental trajectory via serial magnetic resonance imaging in children and adolescents; the work of Giedd and other brain scientists has revolutionized our understanding (Giedd et al., 1999; Lenroot & Giedd, 2006). They discovered that in addition to the known dramatic brain growth that occurs during early childhood, a second wave of gray matter “overproduction” occurs during adolescence. Another myth inaccurately attributes mature cognitive capacities to teens based on physical appearance. Pubertal development is associated with dramatic changes, including rapid physical growth, sexual maturation, and activation of new drives, motivations, and emotions. However, key aspects of brain development important to driving occur later along the timetable than the neuroendocrinological changes of puberty; thus, a teenager’s physically mature appearance may belie the continuing changes in the development and

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Early adolescence

Middle adolescence

Late adolescence

Puberty heightens emotional arousability. sensation-seeking. reward orientation

Period of heightened vulnerability to risk-taking and problems in regulation of affect and behavior

Maturation of frontal lobes facilitates regulatory competence

maturation of his or her brain. Not only does this concordance create a mismatch between expectations of adult behavior with the capacity of a teen to perform adult-like tasks but also, according to Steinberg, it creates a potentially dangerous challenge for the developing adolescent in that “changes in arousal and motivation brought on by pubertal maturation precede the development of regulatory competence” (Steinberg, 2005, p. 69) (Figure 23.6). A third myth incorrectly describes teen brains as “defective” or “deficient” and incapable of learning. It is a fact that driving is a complex cognitive task that utilizes multiple areas of the brain; however, the teen brain is primed for learning. The overproduction and subsequent pruning of nerve fibers during adolescent brain development prepares a teen to explore and manage his or her expanding world, and the additional subsequent pruning and myelination increases the efficiency of brain processing. The final adult brain is “sculpted” to match genetic potential and environmental exposures during adolescence and may provide the opportunity for embedding safe driving behaviors as well as the potential for establishing unsafe attitudes and behaviors (Steinberg, 2005). Although direct associations between differences in brain development among healthy teenagers and specific driving behaviors remain theoretical, understanding brain development can be helpful to set accurate expectations and assessments of teen driving. For example, cerebellar functions associated with muscle coordination and balance develop early and, as a result, within a short period of approximately 10e15 h, a novice teen driver can gain sufficient proficiency at the operational leveldstarting and stopping, taking right and left turns, driving in a straight line, and backing up. Adults practicing driving with teens may develop a false sense of confidence and comfort in their teen’s driving ability because the teen’s coordination allows him or her to drive more smoothly. However, the cerebellum continues to develop into the mid-20s for higher order coordinating functions that might be associated with more highly proficient or safe driving.

FIGURE 23.6 Cognitive changes during adolescence. Source: Adapted from Trends in Cognitive Science, volume 92, edition 2, Steinberg L., “Cognitive and affective development in adolescence,” pages 69e74. Copyright Ó 2005, with permission from Elsevier.

Furthermore, safe driving requires developmental maturity associated with executive functioning, which may in part depend on brain myelination. The last area of the brain to become fully myelinated is the prefrontal cortex, which mediates planning, impulse control, multitasking ability, perception of risk, and decision making (the abilities required for the tactical and strategic skills of driving). This maturation continues until a teen reaches his or her mid-20s. As the frontal lobe undergoes maturation, effects from activity in other parts of the brain that typically rely on the frontal lobe to regulate decision making may become prominent. As a function of the nucleus accumbens and the ventral tegmental areas of the brain, adolescents may be wired to highly value reward from risk taking and pleasure seeking during decision making. The amygdala, part of the limbic system, is very sensitive and reactive to stressful stimuli during adolescence, and connectivity among the limbic system of the brain and the frontal lobe increases during adolescence. Without a mature frontal lobe, decisions during adolescence may be more highly affected by reward feedback and reaction to emotion (Giedd, 2009; White, 2009). These changes in brain biology also address why teenagers are more susceptible to the effects of alcohol than are other drivers. For adolescents, alcohol may affect other driving-related mechanisms in the brain more powerfully than it affects other drivers: Alcohol tends to inhibit the regulatory mechanisms of the frontal lobe, leading to attentional and impulse control difficulties; the activity in the amygdala is calmed by alcohol, leading to a false sense of comfort when one may be in danger; the feedback system of the nucleus accumbens and ventral tegmental areas is “tricked” into being rewarded by alcohol; and alcohol use may lead to blackouts in the hippocampal areas, which is typically essential for recording memories (White, 2009). Other changes in brain physiology may also explain why adolescents account for a disproportionate number of

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drowsy-related crashes. The conflict between biorhythmic changes, which often result in later bedtimes, and the demands of early starts to high school days often results in decreased and disrupted sleep. Meanwhile, maturational changes in adolescence increase the need for sleep (Horne & Reyner, 1995). The combination of inexperience with increased fatigue leads to inattentive driving behaviors, impaired judgment and decision-making skills, exacerbation of the effects of alcohol, and increased aggression among young drivers, impacting the operational, tactical, and strategic skills of driving (Dahl, 2008; Groeger, 2006).

correction of errors are integral to this training, but driving cannot accommodate errors without putting teens at risk. Evidence suggests that specialized off-road, computerized training can impart driving skills, such as scanning for hazards (Fisher, Pollatsek, & Pradhan, 2006). Finally, expertise requires acquisition of automaticity as a result of embedding of automatic routines into the brain. This points to the critical importance of high-quality instruction and practice to ensure that these embedded routines produce safe and competent driving rather than automated risky driving responses.

3.5. Higher Order Cognitive Development and Driving

3.5.2. Perceptions and Decision Making with Regard to Risk

In a review of developmental and adolescent drivers, Keating and Halpern-Felsher describe driving as a “set of complex, interrelated, and simultaneous competencies, including psychomotor, cognitive and perceptual proficiencies” that must be translated into safe driving through “complex strategies, expertise and concentration” in the context of potential challenging “social influences” (Keating & Halpern-Felsher, 2008, p. 272). The higher order skills associated with safe, competent driving include planning and strategy development. Emergence of these skills depends not only on experience behind the wheel but also on cognitive development. In addition to competence needed to regulate impulses and emotions, teens must gain expertise and an accurate appraisal of risk.

Another myth about adolescents inaccurately portrays teens as irrational; they often engage in risky behaviors because they either do not understand the consequences of their actions or perceive themselves to be immune to harm (the myth of “teen invulnerability”). In reality, teens recognize driving risks but may perceive low personal risks (“I’m a good driver”) or rationally accept the risks for benefit (e.g., sensation seekers) (Ginsburg et al., 2008). Furthermore, the contextual setting may influence the relative weight of risks versus benefits (e.g., accepting risks that they would not otherwise accept when in the presence of peers in order to “look cool”). Consistent findings from multiple studies reveal that compared to young adults, adolescents perceive themselves as just as vulnerable to risks (Quadrel, Fischhoff, & Davis, 1993), and that risk behaviors among teens may be associated with a teen’s belief in his or her premature mortality rather than immunity to risk (Borowsky, Ireland, & Resnick, 2009). Studies exploring the perception of driving safety among adolescents also suggest that teenagers generally understand the impact of deliberate risk-taking behaviors such as substance use, text messaging while driving, speeding, and non-use of safety belts (Ginsburg et al., 2008). Harre (2000) created a typology to summarize risk taking among teen drivers by categorizing them on two dimensions: objective crash risk and perceived crash risk. For example, thrill seekers would be high on both dimensions. Decision making among teens occurs differently from that of adults. Teens are more likely to weigh the costs and benefits of decisions rather than use “gist”-based decision making (Ben-Zur & Reshef-Kfir, 2006; Reyna & Farley, 2006). Gist-based decision making, which involves nondeliberative reaction to a mental representation of a general meaning of an experience, typically increases with age combined with experience. In addition, instead of simply weighing the risks and benefits of any particular action without taking affective processes into account (“cold cognition”), a teen’s decision is often influenced by emotions and a high level of arousal (“hot cognition”). As

3.5.1. Expertise Driving expertise involves gaining specific knowledge and skills and being able to apply them effectively and efficiently. As described previously, driving expertise comes with experience rather than with age. However, the goals and, therefore, nature of this driving experience might be different from the perspective of a teen (who may focus on what is needed to pass the licensure exam) than from the perspective of a parent or others who aim to ensure that teens learn to drive proficiently and safely. Current stateof-the-art approaches for training to achieve expertise focus on deliberate, guided practice with feedback (Ericsson, 2005). In addition, driver training requires not only a sufficient quantity of instructional and practice hours but also carrying out this practice under a wide range of driving conditions (Groeger, 2006). Because the novice teen driver has few relevant driving memories, he or she needs to rely on effortful, error-prone decision making. The more experienced driver makes fewer errors in that his or her behavior is less effortful and less prone to distraction, particularly under unpredictable and highly variable conditions (e.g., anticipating another driver’s behavior). The challenge to deliberate practice is that feedback and

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an illustration, in a study to explore the age differences in risk-taking behaviors, subjects were asked to play a computerized game in which they had opportunities to take driving risks (e.g., driving through a yellow light) in order to earn more points (Steinberg, 2005). When individuals played alone, adolescents and young adults showed no significant differences in risk-taking behaviors. In the presence of peers, adolescents took more risks, whereas the risk-taking behaviors of adults did not change. A teen’s desire for peer acceptance and approval may put him or her at risk for making decisions based on shortterm gains (Reyna & Farley, 2006; Rivers, Reyna, & Mills, 2008; Steinberg & Monahan, 2007) and affects his or her ability to make safe tactical and strategic decisions about driving. In addition, in situations in which learned skill and quick and accurate decision making are required to attend to, identify, and avoid hazards, this type of deliberative approach puts a teen at risk for unsafe driving behaviors (Tricky, Enns, Mills, & Vavrik, 2004).

3.6. Psychosocial Factors 3.6.1. Personality Certain personality factors may also be related to risky driving behaviors. Teens with a sensation-seeking personality as well as difficulties with emotional regulation are more likely to be involved in crashes (Patil, Shope, Raghunathan, & Bingham, 2006). Another personality trait associated with driving behaviors is that of tolerance of deviance; those with a higher tolerance of deviance are involved in more motor vehicle crashes (Bingham & Shope, 2004). For some adolescents, such as teenagers with attention deficit/hyperactivity disorder (ADHD), impulsive and risk-taking behaviors may stem from other neurobiological forces. The following section presents what is currently known about the driving experience of teenagers with certain developmental and behavioral disorders.

3.6.2. Growing Social Context and Influences The teen’s biological and cognitive development occurs within an ever-enlarging environmental context that shapes not only the teen’s social development but also his or her brain and cognitive development. It is beyond the scope of this chapter to provide a thorough review of social development, but key aspects of parents, peers, and society are reviewed. Despite beliefs to the contrary, parents remain an important influence in adolescent development with regard to driving. As described previously, authoritative parenting style, particularly in combination with parenteteen agreements with regard to driving, can be protective for teens. In addition, parents can serve as role models, teachers, guides,

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rule makers, monitors, and supports through the learning to drive and early driving processes. Through modeling, it can be said that driver education begins when the child first sits in a forward-facing child restraint and witnesses the parent’s driving style. In fact, Prato et al. (2010) identified an association between parents and teens regarding driving risk taking. Although parents are a dominant influence in teen driver safety behaviors, research demonstrates that peer approval is a tremendous influence in shaping other teen health behaviors (Brown, 2004; Gardner & Steinberg, 2005; Jacobsohn, 2007; Perkins, 2003; Steinberg & Monahan, 2007). As described previously, peer passengers greatly influence novice teen driver crash risk, and teens recognize the potential dangers posed by peer passengers. Other studies further confirmed that peer approval was important in the adoption of safe behaviors involving teen driving with peer passengers (Winston & Jacobsohn, 2010). Parents’ influences over their children’s health and wellbeing changes in form and function as teens continue to develop. Indeed, problematically, the period in which peers’ influence becomes increasingly more important as parental influence is often resented or rebuffed overlaps with the very period in which teens begin to learn to drive and are often licensed. Finding an optimal balance between parental control and monitoring with adolescent independence is an ongoing process that can be difficult for many families and presents yet another challenge in promoting safe driving behaviors for teens (Darling, Cumsille, & Loreto Martı´nez, 2007; Darling, Cumsille, & Martı´nez, 2008; Smetana & Asquith, 1994; Smetana, Metzger, Gettman, & Campione-Barr, 2006). The driving behavior of peers and of the broader community can also affect a teen’s perception of what is expected. Just as teen drivers may model the driving behaviors of their parents, they may also pick up and model the driving behaviors of their peers (Shope, 2006). The speed and aggression of video games and popular race car drivers, and the pressure of a teenager to own a car (that is not necessarily the safest car for a teen), may influence the tactical and strategic driving skills of an adolescent (Kellerman & Martinez, 2006). On a more expansive level, neighborhood community characteristics, such as poverty level and exposure to violence, may impact a teen’s overall attitude toward safety. In turn, these characteristics are influenced by the political will and funding for local programs aimed at improving teen safety (Johnson & Jones, 2011). Clearly, teenagers represent a unique set of drivers who are influenced by a multitude of physical, social, behavioral, and environmental factors. Interventions most likely to succeed will be multipronged and involve the teen and his or her family, peers, and society, applying the science of adolescent development and driving to capitalize on the

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assets of teens (e.g., ability to learn) while limiting their exposures to hazards that they cannot handle.

4. IMPACT OF DEVELOPMENTAL DISABILITIES ON TEEN DRIVING The preceding section described the developmental and behavioral factors that may influence the general teenage population, but there are certain subpopulations of teens with developmental disabilities, such as ADHD, autism spectrum disorders, and intellectual disabilities, who, as a result of their disabilities, may be at higher risk for unsafe driving behaviors. This section addresses these subpopulations of teenagers. Although we acknowledge that individuals with other types of disabilities, such as physical disabilities or medical conditions, may also encounter risks with regard to driving, this chapter focuses on the unique intersection of the behavioral and cognitive changes during adolescence and certain neurocognitive disabilities affecting teens.

4.1. The Impact of Attention Deficit/ Hyperactivity Disorder on Driving Relative to other developmental disabilities, the impact of ADHD on motor vehicle driving in teenagers is the best studied in the scientific literature. ADHD is a developmental disability characterized by difficulties with sustained attention, easy distractibility, impulsivity, and hyperactivity. ADHD affects approximately 4e10% of the pediatric population and commonly persists into adulthood (American Psychiatric Association, 2000). Thorough reviews of the literature exist elsewhere (Barkley & Cox, 2007), and so this section highlights major findings. Results from observational studies generally indicate that ADHD is associated with increased unsafe driving behaviors, including receiving citations, reckless driving, and motor vehicle crashes. ADHD is also associated with other conditions that contribute to unsafe driving behaviors, such as use of alcohol and drugs and emotional regulation difficulties. Using Barkley’s model of driving as a multidimensional activity, teens with ADHD are at risk for impaired operational, strategic, and tactical skills due to their high level of impulsivity, impaired reaction time, difficulties with emotional regulation, and executive functioning differences.

4.1.1. ADHD and Licensure Status Although large epidemiologic studies of licensure status among teens with ADHD have not been published, followup and observational studies of the driving outcomes of teens with ADHD do not suggest any difference in licensure frequency between ADHD and community control

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groups or delay in obtaining a driver’s license among those with ADHD (Barkley, Guevremont, Anastopoulos, DuPaul, & Shelton, 1993; Fischer, Barkley, Smallish, & Fletcher, 2007). Furthermore, there have not been any studies on the decision-making process of families and teens with ADHD regarding driving and whether and how the knowledge of the increased risk of unsafe driving behaviors among teens with ADHD impacts the decision to learn to drive.

4.1.2. ADHD and Driving Outcomes Compared to teens without ADHD, young drivers with ADHD are two to four times more likely to be in motor vehicle crashes, three times more likely to sustain injuries in crashes, four times more likely to be at fault, and six to eight times more likely to have their license suspended (Barkley et al., 1993; Barkley, Murphy, & Kwasnik, 1996). In addition, the diagnosis of attention deficit in early adolescence is suggested to be a risk factor for traffic violations, increased number of tickets and crashes, and unsafe driving behaviors (reckless driving and speeding) in later adolescence and young adulthood (Barkley et al., 1993, 1996; Barkley, Murphy, Dupaul, & Bush, 2002; Fischer et al., 2007; Nada-Raja et al., 1997; Thompson, Molina, Pelham, & Gnagy, 2007; Woodward, Fergusson, & Horwood, 2000). Many of these studies utilized self-report, and because teens and young adults with ADHD are more likely to underreport adverse driving outcomes, these findings may be an underestimate of the true difference in risk. Studies also suggest that others perceive impaired driving ability among teens with ADHD. Both parents and driver’s education instructors (who were blinded to ADHD status) rated adolescents with ADHD as less likely to drive with safe habits and to demonstrate more driving errors (Barkley et al., 1993; Fischer et al., 2007). Finally, reports using official records such as those from the department of motor vehicles revealed a greater (but not statistically significant) number of motor vehicle crashes among ADHD groups (Barkley et al., 1993, 1996, 2002), whereas studies using simulators to assess driving performance showed that individuals with ADHD experienced more risky driving behaviors (scrapes, collisions, and poorer steering control) compared to their non-ADHD peers (Barkley et al., 1996; Fischer et al., 2007). Individuals with ADHD may also be more susceptible to other behaviors or characteristics that may contribute to unsafe driving behaviors. For instance, college-aged students with higher levels of ADHD symptoms were reported to experience more anger while driving, display anger in more aggressive ways, and had more crash-related outcomes (Deffenbacher, Richards, Filetti, & Lynch, 2005; Richards, Deffenbacher, & Rosen, 2002). Indeed, drivers with higher aggression levels in general may have a higher

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Young Drivers

prevalence of behavioral and psychiatric disorders, including ADHD (Jonah, Thiessen, & Au-Yeung, 2001; Malta, Blanchard, & Freidenberg, 2005). Aggressive driving, speeding, and increased alcohol use while driving may also be associated with a greater degree of sensationseeking behaviors (Jonah et al., 2001). Differences in cognitive processing may also impact the way a teen understands driving rules and regulations. Teens with ADHD may respond differently to changing road conditions and make more errors in response tasks in a driving simulator and also score lower on tests of knowledge of driving rules (Barkley et al., 1996, 2002; Fischer et al., 2007). That these differences are likely due to abnormal cognitive processing is supported by other study findings suggesting that certain ADHD groups demonstrate a trend for greater deficits in processing speed on psychological testing (Fried et al., 2006).

4.2. The Impact of Autism on Driving Within the past 20 years, a growing segment of the autism population has been increasingly recognizeddthose with at least average overall intelligence who share characteristics with those more seriously affected by autism. Broadly termed for the purposes of this chapter as higher functioning autism spectrum disorders (HFASD), and affecting as many as 2/1000 individuals, many in this population are integrated into typical life (both in school and at work) but experience deficits in social interaction, communication, and motor/ coordination skills that, if untreated, can impair aspects of daily living (Fombonne & Tidmarsh, 2003; Ghaziuddin, 2008; Gillberg, 1998; Paul, Orlovski, Marcinko, & Volkmar, 2009; Tantam, 2003). HFASD represents a high-functioning disability associated with more subtle cognitive deficits that may directly impact automobile driving. The following discussion presents potential challenges faced by an adolescent driver with HFASD and early results from unpublished research.

4.2.1. Motor and Cognitive Differences Many individuals with HFASD are described as “clumsy,” which is often listed as a characteristic feature of the disorder (Bonnet & Gao, 1996; Weimer, Schatz, Lincoln, Ballantyne, & Trauner, 2001). Studies of adolescents with HFASD revealed specific impairments in movement preparation, planning, inhibition, and execution compared to the skills of IQ-matched, typically developing controls (Christ, Holt, White, & Green, 2007; Freitag, Kleser, Schneider, & von Gontard, 2007; Hughes, 1996; Rinehart, Bradshaw, Brereton, & Tonge, 2001). In addition, up to half of children and adolescents with autism spectrum disorders have attention difficulties that meet criteria for ADHD (Sinzig, Bruning, Morsch, & Lehmkuhl, 2008).

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Differences in higher order cognitive processes among individuals with HFASD may impact the tactical and strategic competencies in driving as well. Studies of individuals with autism spectrum disorders suggest differences in mental flexibility (the ability to shift to a different thought or action based on the environment), self-monitoring, and self-correction (Hill, 2004; Pijnacker et al., 2009). These differences may impact an individual’s ability to adapt his speed to driving conditions, his decision making (e.g., whether to pass another vehicle), and his planning abilities (e.g., evaluating weather for a drive or trip).

4.2.2. Visual Perceptual Differences Individuals with HFASD tend to engage in local processing (focusing on details) rather than focusing on the global or larger meaning of the presented visual stimuli (Iarocci, Burack, Shore, Mottron, & Enns, 2006). This is the basis of the weak coherence account theory, and it is thought to be due to underlying dysfunction in the amygdala in the brain (Shalom, 2005). As a result, an individual may become “lost in the details” of what he or she sees in the road, affecting both operational and strategic competencies of driving. Although studies exploring the experience of driving individuals with autism have yet to be done, one study assessing the hazard perception skills of nondriving young males found that compared to individuals without autism, individuals with autism spectrum disorders identified fewer hazards where the source involved people (as opposed to objects such as another car), and that they also were slower to respond to hazards (Sheppard, Ropar, Underwood, & van Loon, 2010).

4.2.3. Emotional Regulation Difficulties A greater proportion of individuals with autism spectrum disorders are at risk for a co-occurring mood disorder, including anxiety, which may impact the driving experience (Hofvander et al., 2009). In addition, difficulties with emotional regulation (self-calming) are common. The inability to stay calm and focused while driving may result in unsafe driving behaviors (Patil et al., 2006).

4.2.4. Strengths among Individuals with HFASD Although several characteristics may place individuals with HFASD at risk for unsafe driving behaviors, there are also strengths that may help to protect individuals while driving. Attention to detail is commonly noted among individuals with HFASD and may serve to result in more careful driving. In addition, many individuals with HFASD are described as “rule bound” and may be more likely to follow the law carefully (e.g., follow street signs, wear their safety belt, and not talk on cell phones) than to act recklessly.

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4.2.5. Current Research on Driving in Teens with Autism Although the deficits associated with HFASD are relevant to the task of driving, to date there are only a handful of ongoing studies of the driving experience of teens with HFASD. One study suggests that driving is a significant issue, and learning to drive is frequently considered among teens with HFASD (Huang & Durbin, 2010). Independent predictors of learning to drive included measures of functioning at school and outside of the home, support from the school, and parent experience with teaching other teens to drive. A previous study of children with special health care needs in motor vehicle crashes suggested that parents of children with special needs are generally more vigilant with regard to their child’s safety (Huang et al., 2009). Teens with HFASD and their parents may have increased concern regarding driving practices and may self-restrict driving. For individuals with HFASD, for whom social interaction is a particular deficit, a narrowed ability to access a social network secondary to limited independence may serve to further exacerbate their disability.

4.3. The Impact of Intellectual Disability on Driving Intellectual disability (previously known as mental retardation) is typically defined by an IQ <70e75 and significant deficits in adaptive behavior, and it affects approximately 0.6e1% of young adults and adolescents. Although the impact of intellectual disabilities on multiple aspects of daily life has remained prominent in disability literature, the majority of the research on the impact of intellectual disabilities on driving performance took place in the 1960s and 1970s. Most studies found that individuals with intellectual disabilities were more likely to experience traffic violations compared to controls (Boyce & Dax, 1974; Gutshall, Harper, & Burke, 1968). More recent studies have focused on finding and developing tools to predict and differentiate between individuals who are likely to successfully obtain a license and those who are not. For instance, a test of information processing, the Perceptual Memory Task, may be a predictor of driver’s license status among individuals with intellectual disabilities (Geiger, Musgrave, Welshimer, & Janikowski, 1995). Given the advances in vehicle monitoring and driving assessment technology (e.g., driving simulators and eye tracking technology), current research exploring the driving behaviors of individuals with intellectual disabilities would better help to guide advances in driving education and the evaluation of fitness to drive among individuals with these disabilities.

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4.4. Summary of Driving in Teens with ADHD, Autism, and Intellectual Disabilities ADHD, autism, and intellectual disabilities all represent common neurocognitive disabilities associated with deficits that may impact the operational, tactical, and strategic competencies of driving. Because individuals with autism spectrum disorders and intellectual disabilities are at higher risk for ADHD-like behaviors, it would not be surprising if they had similar risky driving patterns. In theory, teens with autism may have more difficulty with regulating their emotions and correctly interpreting the behaviors of other drivers. Teens with intellectual disabilities may have more difficulty learning and applying the “rules of the road.” Slower processing speeds might impact reaction time in both groups. More research is needed to accurately describe the driving performance and experience of individuals with autism and intellectual disabilities. However, many differences also exist that might serve to protect individuals with autism and intellectual disabilities from unsafe driving behaviors. Families of teens with autism and intellectual disabilities may be more cautious about their teen learning to drive. Teens with autism may be more rule-bound and may be less apt to engage in risky behaviors, unlike teens with ADHD. More research is needed to describe a common driving profile for teens with autism and intellectual disabilities. In addition, physicians, whom parents may consult regarding driving decisions, do not have any evidence-based guidelines to offer families regarding whether it is safe for their teens with developmental disabilities to learn to drive. Understanding how individuals with these diagnoses might differ is important when considering how general driver’s education should account for the potential needs of teens with developmental disabilities and how educational interventions should be tailored.

5. RECOMMENDATIONS FOR DEVELOPING EVIDENCE-BASED INTERVENTIONS TO PROMOTE SAFE DRIVING AMONG TEENS If we conceptualize safe driving as a health behavior that takes into account the changes in developmental and social contexts, then interventions to reduce teen driver crashes can be informed by recent use of theory-driven and research-based design of interventions to promote healthy and safe living, much of which draws from the recent dramatic growth in knowledge about human development and behavior. Central to recent successful health-promotion efforts and public health interventions is a systematic application of strong theory and empirical evidence.

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Young Drivers

The preceding section revealed how teenage drivers possess both challenges and potential compared to drivers from older age groups. Their developmental trajectory can be a risk if teens are permitted to drive in situations for which they are ill-prepared due to either lack of experience or expertise (e.g., driving on high-speed roads) or lack of sufficient maturity (e.g., regulatory control, particularly in the presence of peers). However, because teens are mostly new to the driving task, their developing brain can be an asset. If teens are provided with effective training and gradual privileges in a supportive context of pro-safety societal norms, they have tremendous potential to develop safe driving behaviors. The challenge of teen driving is to support the transition from nondriving youth who are dependent on others for mobility to independent individuals who can make safe decisions, manage their peers, recognize and avoid hazards on the roads, and continue to gain new driving abilities. Interventions aimed at improving the safety of teen drivers must consider this particular driving experience of adolescents. The goal of improving driving safety among teens and their peers involves many stakeholders in addition to teens and their parents, such as automobile manufacturers, insurance companies, educators, and policy makers. Despite this common goal, many programs are presented to the public without concrete plans for evaluation, whereas others promote unsafe behaviors (e.g., advertisements that glorify speeding or movies that portray drinking and driving without negative consequences). Intervention programs need to systematically apply evidence and theory to ensure the highest likelihood for success and then test and refine, particularly before large-scale dissemination. Then, the programs will have the greatest chance to effect positive change when implemented.

5.1. A Practical Approach to Intervention Development Youthful driving can be conceptualized as a behavioral framework of various influences and factors: Factors that are not likely to be fluid (e.g., demographics and personality features) can be used to help identify target populations for interventional efforts, whereas factors that may be more amenable to change (e.g., driving ability and driving environment) provide the focus for the activities. Part of the challenge of addressing these various factors includes understanding the purpose of certain behaviors, clarifying the intent of any specific intervention, monitoring for unintended consequences, and ongoing reevaluation (Shope, 2006; Winston & Jacobsohn, 2010). Best practices in intervention development dictate using a program theory as a model for developing and evaluating intervention content that links together intervention components with the intended outcomes (Rossi, Lipsey, &

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Freeman, 2004). The key approach involves re-orienting to work from right to left: Intervention)Target constructs )Behavioral objectives)Key outcome Rather than starting with a preconceived idea for an intervention, researchers should start by clearly defining a key outcomeda broad measurable vision such as reducing teen driver crashes and associated injuries. Next, those developing interventions should define the key behaviors that when adopted will increase the likelihood that the vision will be achieved. Then, the behavioral objectives should be narrowed to focus on smaller actionable goals or target constructs (e.g., changes in attitudes, skills, behaviors, knowledge, or perceived norms) that increase the likelihood of the adoption of the chosen target safe behavior. Finally, researchers should develop a prevention strategy and its intervention components to address these target constructs. Acting as a compass, the program theory guides not only an intervention’s development but also its assessment. The issue of teen driving safety is complex, and thus multicomponent interventions have the highest likelihood for success. An example of a program theory as applied to teen driving specifically has been developed (Winston & Jacobsohn, 2010). These interventions might target individuals (teens or parents), populations (peer teens, schools, or communities) or society as a whole. For each, a different set of priority behavioral objectives can be identified as both important and modifiable (Senserrick, 2006) (Table 23.1). It is important to choose behavioral objectives with proven effectiveness in reducing the incidence of crashes and fatalities. For teens, behavioral objectives might include wearing safety belts on every trip, refraining from electronic device use while driving, and maintaining a safe following distance, whereas for parents, behavioral objectives might include facilitating quality, quantity, and

TABLE 23.1 Examples of Behavioral Objectives for Teen Intervention Teens Use of restraints Refrain from impaired driving (alcohol or drowsy driving) Abstain from cell phone use and other distractions while driving Avoid risky or reckless driving (speeding) Parents and teens Engage in sufficient practice in novice period Follow graduated driver licensing laws Require shared access to vehicles, rather than primary access, for new teen drivers

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diversity of practice driving and setting and enforcing restrictions for new teen drivers. Behavioral objectives for community or government leaders or the media might include enacting laws and displaying safe messaging, respectively, to support parents and teens in the adoption and practice of safe driving. As a next step, target constructs should be articulated that promote or impede adoption of the behavioral objective. These target constructs can be discovered through review of the literature and formative research with members of the population that should adopt the behavior. It is important to choose target constructs that are related to the behaviors, have room to change, and are feasible to change (Fishbein, 2000; Fishbein & Ajzen, 1975; Fishbein & Yzer, 2003). For example, if teens are aware that non-use of safety belts increases the risk of injury, then knowledge about this risk would not be a target construct because there is little room to change. The target constructs then become the measurable goals of the interventions. For interventions directed at teens, teens should not only be subjects of the research but also partners in intervention development (Brown, 2004; Gardner & Steinberg, 2005; Jacobsohn, 2007; Steinberg & Monahan, 2007). Content and messaging as well as implementation strategies must be salient to the teens. Knowledge and experience gained from campaigns to promote healthy behaviors and safety among teens can be applied to promoting safe driving: Teens change rapidly, teens prefer to be listened to rather than talked at, teens are strongly motivated by independence from authority, content with embedded messages is more effective than “hard sells” or fear tactics, and social networks and brands are important and trustworthy (Smith, 2006). The most widely recognized and successful social marketing campaign for safe driving was the Designated Driver Campaign. The famous slogan, “Friends Don’t Let Friends Drive Drunk,” reflected the campaign’s well-accepted and effective use of positive peer pressure and socialization (Smith, 2006). This marketing campaign resulted in more than 80% of Americans recalling having heard or seen the public service announcement and nearly 80% of Americans reporting that they took action to prevent a friend from driving drunk.

5.2. Examples of Evidence-Based, Theoretically Grounded Intervention Programs to Reduce Teen Driver Crashes The following discussion provides examples of proven or promising theoretically grounded interventions to promote safe teen driving. A combination of graduated driving licensing (GDL) laws, school-based programs such as Ride Like a Friend, and training programs such as the hazard

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perception program from the University of Massachusetts with specific roles for parents (e.g., parenteteen agreements) are likely the most effective way to address the multifactorial issue of teen driving. Table 23.2 illustrates how these programs, in concert, address the multiple biological, psychosocial, and cognitive factors of driving and uses multiple aspects of effective program development, including partnering with legislation and advocacy groups, finding salient messages for teens and their parents, and using peer-to-peer delivery of messages. This chapter focuses primarily on interventions for U.S. teen drivers, but it should be noted that interventions are delivered to individuals or communities and should be adapted or developed for the specific audience. International driving interventions need to be developed to address specific needs of youth in specific countries. Research would be required to determine whether U.S. strategies could be adapted or used; if not, new strategies may need to be developed.

5.2.1. Intervention at a Societal Level: Graduated Driving Licensing and Other Laws An example of a successful intersection of science and public policy is the GDL program. Multiple studies have found that the highest risk of crashes occurs among the youngest drivers, particularly in the first 6 months and 500 miles for a new driver. Also, as previously discussed, driving with other teen passengers as well as driving during the nighttime on the weekends has consistently been associated with unsafe driving outcomes (Lin & Fearn, 2003). GDL laws push licensure to older ages and phase in driving privileges, and they issue driving restrictions in order to limit the beginning driver’s experience to lower risk situations. Although programs and thus the resulting reduction in crashes may vary by state, GDL laws have generally reduced the risk of crashes in the youngest drivers by 20e40% (Shope, 2007). Other examples of public policies that work in concert with GDL laws to reduce the risk of crashes in teen drivers are the national minimum drinking age and zero tolerance blood alcohol concentration laws. Despite reducing crashes among teens by delaying licensure, GDL laws have not been shown to be effective in changing actual teen driving behavior. Probable reasons for not changing behavior include not addressing other biological, psychosocial, and environmental factors that influence teen driving. For instance, GDL laws do not effectively teach teens about the risks of driving with peer passengers and do not try to build on their belief systems by creating salient messages for parents and teens, informed by input from parents and teens themselves. In addition, hazard perception is not addressed by GDL laws. Other

Chapter | 23 Young Drivers

TABLE 23.2 Components of Various Teen Driving Interventions Driving intervention

GDL laws

Ride Like a Friend

Computer training programs (e.g., hazard perception) (Fisher et al., 2006)

Parenteteen agreements (SimonsMorton et al., 2008)

In-vehicle monitoring (McGehee et al., 2007)

Factors that influence teen driving experience Biological

Nighttime driving; drowsy driving

Cognitive

Environmental

Reducing in-car distractions; feedback on performance Perceived control over behaviors; responsibility of role in car

Parent training; peer passengers

Inexperienced driving hazard perception

Peer passengers

Limits on early independent driving

Limit exposure to hazardous situations

Limits set by parents

Proxy for an adult passenger

Partnering with parents

Vehicle build-ins

Components of developing effective intervention Use of program theory

Yes

Creating an effective message

Informed by peers

Implementation

Partnering with policy changes and enforcement

School-based, peer-to-peer delivery

Addresses salient issue of inexperience

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interventions have been developed to fill these gaps, and these are discussed separately. Policies aimed at the driving experience of teens with developmental disabilities are not as well developed, however. In the United States, there is no standard from state to state for assessing cognitive or neuropsychiatric fitness to drive, and states do not systematically take into account the different developmental disabilities that may affect driving. Physicians are not routinely given guidelines on how to assess overall fitness to drive from the licensing agency. In Pennsylvania, where applicants for a learner’s permit must undergo a physical exam by a medical professional, physicians or another medical practitioner must certify that the individual’s conditions is not “likely to impair the ability to control and safely operate a motor vehicle” (“Vehicle Code: Physical and Mental Criteria, including Vision Standards Relating to the Licensing of Drivers,” 1991) (Figure 23.7). However, guidelines regarding fitnessto-drive standards are not routinely provided for many neuropsychiatric and developmental conditions. The impact and efficacy of screening tools for first-time drivers such as the one used in Pennsylvania should be evaluated.

Vulnerable and Problem Road Users

1-day workshop focusing on driver risk behavior and a broader community program that included a 1-day workshop with follow-up activities that focused on reducing risk taking and building resilience. The broader community program aimed to build resilience by empowering youths with not only the skills, attitudes, and knowledge to make informed driving-related decisions but also general decisions concerning safety (e.g., safe celebrating). Subsequent data from police records found that the resilience-focused program was associated with a 44% reduced relative risk for crashes, whereas the driver-focused program was not associated with a reduction in crash risk (Senserrick et al., 2009). A school-based social marketing program in the United States, Ride Like a Friend (RLAF), was developed using the principles of program theory to reduce driver distraction from passengers (The Children’s Hospital of Philadelphia, n.d.). The aims of the program are to build awareness about teen drivers’ motor vehicle crash risks involving passengers in their cars and to establish beliefs and behaviors among teen drivers and passengers that promote safe driving. RLAF was designed as a peer-to-peer in-school campaign, and its goal is to provide a sense of empowerment to both a teen driver and his or her passenger. Figure 23.8 shows the key outcomes, behavioral objectives, target constructs, and intervention content of RLAF. In 2008, participation in RLAF was associated with increased likelihood of holding safety-positive beliefs and demonstrating safety-positive behaviors related to being a passenger. For example, posttest data showed that compared to teens with no RLAF

5.2.2. Intervention at the School Level: Schoolwide Educational and Social Marketing Programs A schoolwide educational program in Australia was developed to compare the impact of two educational programsda Dl- 180 (7-10) ALL INFORMATION IN THIS SECTION

MUST BE COMPLETED IN FULL BY A HEALTH CARE PROVIDER

Please check any of the following that would prevent control of a motor vehicle. Circulatory disorder Neuropsychiatric disorders Neurological disorders Uncontrolled Epilepsy Cognitive lmpairment Uncontrolled Diabetes Conditions causing repeated lapses of consciousness (e.g. epilepsy, narcolepsy, hysteria, etc.) Specify : Impairment or Amputation of an appendage. If so, list: Other:

Cardiac disorder Alcohol abuse

Hypertension Drug abuse

If seizure disordr, date of last seizure:

NOTE: Any recommendations/additional comments must accompany this certificate on a health care provider’s letterhead.

PROVIDER INFORMATION (Please print or type) PROVIDER’S NAME

SPECIALTY

STATE LICENSE #

STREET ADDRESS

CITY

STATE

TELEPHONE

ZIP CODE

FAX

I hereby state that the facts above set forth are true and correct to the best of my knowledge, information and belief. I understand that the statement made herein are made subject to the penalties of 18 Pa.C.S s s 4904 (relating to unsworn falsification to authorities) punishable by a fine up to $2.500 and/or imprisonment up to 1 year. Examinee’s Signature (SIGN ONLY IN PRESENCE OF PROVIDER)

Provider’s Signature

Physical Date

FIGURE 23.7 Physical exam form for first-time license applicants in Pennsylvania. Source: Available at http://www.dmv.state.pa.us/pdotforms/dl_ forms/dl-180.pdf.

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Young Drivers

Ride Like a Friend/ Drive Like You Care Initiative For delivery in schools:

1. Activites & materials; 2. Websites; and 3. Support for organizers

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For teens, address beliefs pertaining to behavioral objectives 1. Subjective norms, 2. Descriptive norms, 3. Self-efficacy about performing behaviors, and 4. Percived control over behaviors.

Teen passenger 1. Always wear belt 2. Show respect 3. Help driver Teen driver 1. Set rules in car 2. Ask for help 3. Expect respect

Reduce crashes Reduce injuries For teens and their passengers

FIGURE 23.8 Highlights of program evaluation to reduce driver risk. Source: Reproduced from Injury Prevention, Winston FK and Jacobsohn L, volume 16, pages 107e112. Copyright Ó 2010 with permission from BMJ Publishing Group Ltd.

participation, teens who had at least some participation were significantly more likely to report more favorable attitudes and self-efficacy toward wearing a seat belt. Teens with at least some RLAF participation were also significantly more likely to wear a seat belt after completion of the initiative (Jacobsohn & Winston, n.d.). Several driver education programs aimed at individuals with cognitive disabilities have resulted in successful licensing. Project Drive is a current effort spearheaded by researchers at the University of Alabama, in concert with the Department of Education and Department of Special Education (Lanzi, 2005). Their aim is to help adolescents with mild intellectual disability obtain their learner’s permit by training driving educators and creating modifications to their existing driver’s education program (e.g., enlarging font in the manual, changing two-sided pages to single-sided pages, modifying text to a second-grade level, and adding teacher video supplements). Approximately three-fourths of a pilot group (mean IQ, 71; SD, 10.77) passed the written exam on the first attempt, including individuals with IQs in the 40s. However, more research is needed to explore the effects of such modified education programs on actual driving performance.

5.2.3. Interventions at the Family Level: Checkpoints The Checkpoints program was developed by SimonsMorton and represents an intervention aimed at increasing parental limits on early teenage independent driving by utilizing newsletters, videos, and parenteteen driving agreements during the post-permit phase. Results from randomized controlled studies revealed that the treatment group was more likely to report adopting and maintaining the parenteteen agreement, engaged in fewer risky driving behaviors, received fewer traffic violations, and had stricter

limits on driving privileges (Simons-Morton, Ouimet, & Catalano, 2008). Parents of teens with developmental disabilities may seek information from relevant local and national parent advocacy groups/websites, which could be an effective host for delivering interventional messages. More research is needed to explore how families make decisions with regard to the driving process, which could help to inform the development of family-based interventions centered around driving.

5.2.4. Interventions at the Individual Level: Driver Training and In-Vehicle Monitoring Recognizing the need to improve perceptual skills for driving among teens in the general population, a computerbased skill-building intervention was developed at the University of MassachusettseAmherst. The aim was to train teens on where to scan for hazards when driving in various driving environments (Fisher et al., 2006). On-road results demonstrated that trained drivers, compared to untrained drivers, were significantly more likely to scan areas of the road that contained information relevant to risk reduction (64.4 vs. 37.4%, respectively), even when the onroad situations differed from the training road environments (Pradhan, Pollatsek, Knodler, & Fisher, 2009). Another example of technology improving driver safety is the use of advanced driver assistance systems (ADAS), which include cameras, GPS and navigation systems, sensors, and control systems. Because the literature has demonstrated that teens engage in safer driving behaviors in the presence of an adult passenger, one teen-specific use of ADAS is to enhance GDL by acting as a proxy for an adult passenger. ADAS can also limit exposure to risky driving situations by providing less hazardous routes, restricting driver speed, warning drivers before potential

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collisions, locking out potentially distracting vehicle functions such as the radio, and providing feedback regarding driving performance (Lee, 2007). In-vehicle data recording both with and without cameras has been shown to reduce risky driving behavior (McGehee, Carney, Raby, Reyes, & Lee, 2007; Toledo & Lotan, 2006). For teens with ADHD, treatment with stimulant medications generally results in improved driving performance, although results depend on the specific type of stimulant medication. Of the three most commonly used stimulant medications in ADHD treatment, longer acting methylphenidate appears to be the most effective in improving driving performance, as measured by both driving simulator and self-report (Cox, Humphrey, Merkel, Penberthy, & Kovatchev, 2004; Cox, Merkel, Kovatchev, & Seward, 2000; Cox et al., 2006; Cox, Merkel, Penberthy, Kovatchev, & Hankin, 2004). Although these experimental studies were generally limited by small sample sizes that were predominantly male, the improved driving behaviors found among these study teens with ADHD strongly suggests that medication treatment to control symptoms of inattention, decreased focus, and increased impulsivity may be one useful strategy to help improve the road traffic safety of teens with ADHD.

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be considered an essential partner and collaborator; feedback from the community can help to refine program designs and delivery. Thoughtful and iterative evaluation of programs aimed at improving teen driver safety is essential to effective intervention.

ACKNOWLEDGMENTS This chapter was written as part of the Young Driver Research Initiative, a collaborative research program between the Center for Injury Research and Prevention at The Children’s Hospital of Philadelphia (CHOP) and State Farm Insurance Companies (State Farm). The views presented are those of the authors and not necessarily the views of CHOP or State Farm. We acknowledge the commitment and financial support of the National Science Foundation Center for Child Injury Prevention Studies (CChIPS) at CHOP and its industrial advisory board (IAB) for support of the original study on which some of the included analyses were based and for technical guidance. This chapter represents the interpretation solely of the authors and not necessarily the views of CChIPS or its IAB. This project was funded, in part, under a grant with the Pennsylvania Department of Health. The department specifically disclaims responsibility for any analyses, interpretations, or conclusions.

REFERENCES 6. CONCLUSION Although driving is a challenge facing both teens and young adults globally, this chapter focused on the unique issues facing U.S. teen drivers. Traffic crashes are the leading cause of injury among teenagers in the United States and are a greater problem in the United States than in most other developed countries. The causative agents of this crisis are multifold, as research clearly suggests that teenagers represent a unique population of drivers who are influenced by a myriad of physical, social, developmental, behavioral, and environmental factors. Conceptualizing driving as a hierarchy of competencies can help to promote the understanding of the impact of common developmental disabilities, such as ADHD, on driving. Due to the spectrum of differences among adolescents, studying interventions in a controlled environment does not address the actual relationships between adolescents and their own contexts (Lerner & Castellino, 2002). Hence, research for program design and delivery needs to be conducted and evaluated in real-world settings. Only then can the feedback about the efficacy and acceptance of any intervention be considered valid. Programs for teen drivers also require multiple disciplines, and their efforts should be conducted in a synthesized and concerted manner; our current era of advances in safety and monitoring technology as well as policy and legislative action has guided the way to programs such as GDL laws. Moreover, communities in which these interventions take place must

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Chapter 24

Older Drivers Barbara Freund and Paula Smith Pasadena City College, Pasadena, CA, USA

1. INTRODUCTION It is estimated that by 2020, there will be 38 million drivers older than age 70 years on roads in the United States compared to 13 million today. Older driver involvement in fatal crashes is projected to increase 155% by 2030, accounting for 54% of the total projected increase in fatal crashes among all drivers (Lyman, Ferguson, Braver, & Williams, 2002). Driver characteristics and environmental factors have been associated with increased crash risk in older people (Owsley et al., 1998; Preusser, Ferguson, Ulmer, & Weinstein, 1998; Sims, Owsley, Allman, Ball, & Smoot, 1998; Wallace, 1997). As the population ages and the number of older drivers increases, declining driver competence becomes an urgent public health problem and a challenge for health professionals to recognize impaired driving ability in the elderly. Declining driving ability is a cause for concern because these drivers may encounter driving situations known to be difficult for the older driver and in which they are less capable of responding to safely. This chapter presents an overview of the cognitive, physical, and social challenges faced by aging drivers.

2. CHALLENGES FACED BY OLDER DRIVERS 2.1. Cognitive Demands of the Driving Task One of the most widely acknowledged changes that occurs with age is the decline in cognitive processes. Driving is a cognitively demanding task requiring attention, memory, problem solving, and information processingdskills that often decline with aging. Cognitive impairment and dementia are increasingly prevalent among older apparently healthy individuals, affecting up to one-third of people older than age 65 years. More alarming estimates suggest dementia is overlooked in 25e90% of older adults (Alzheimer’s disease and the dementias are reviewed later). Although many older drivers restrict or stop driving voluntarily, a large number continue to drive. This is Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10024-4 Copyright Ó 2011 Elsevier Inc. All rights reserved.

especially true for persons with cognitive impairment who may not possess the requisite insight to acknowledge their limitations and adjust driving activities. Indeed, cognitively impaired individuals rate their driving as good or better than that of drivers their own age, and these ratings are incongruent with actual driving behavior in individuals with cognitive impairment (Freund, Colgrove, Burke, & McLeod, 2005; Goszcynska & Roslan, 1989). It has been well documented that one particular aspect of cognitive processingd“executive function”ddeclines precipitously with age. Connections between executive dysfunction and particular types of driving errors have been identified, as executive function is a critical component of safe driving. Executive dysfunction can lead to a variety of hazardous driving outcomes. In this section, a brief overview is presented of the extensive literature on executive function and on the role of executive function in the context of the driving task. This is followed by comment on the economic and health impacts of motor vehicle injury on elderly drivers. Finally, the types of errors that elderly drivers make, including pedal errors, lane position errors, crashes, red light running, and speeding, are considered. Although these errors are well recognized (National Highway Traffic Safety Administration (NHTSA), 2003, 2004), the underlying cognitive errors are not fully understood. Some work has been reported in the psychological and human factors literature, but much remains unexplained.

2.2. Overview of Executive Function Executive function (EF) is an elusive construct both in measurement and in precise definition. It is responsible for organizing actions in goal-oriented behavior, specifically coordinating different centers of the brain to respond to environmental cues and demands through recognizing the need for a response, planning the response, and subsequently carrying it out. EF is only engaged in such activities to the degree that the required course of action is novel or mentally challenging. EF is involved to the greatest degree in the following types of tasks and situations: planning and decision making, error correction and trouble shooting, 339

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situations requiring novel responses and sequences of action, situations that are hazardous or technically challenging, and situations necessitating the resistance to temptation or requiring a course of action that goes against strong habitual response (Norman & Shallice, 1980). These describe tasks involved in everyday driving and are discussed in more detail later. Executive function has been a difficult construct to study in part due to the inability of tests of EF to capture its pure essence. Because EF orchestrates the actions and responses of other parts of the brain, any test by definition will also capture the functioning of other constructs involved. Compounding the difficulty in studying EF is the trouble defining testeretest reliability because EF tends to be more associated with the learning of novel tasks. Such tasks decrease in novelty with repetition. This phenomenon is true of many driving maneuvers, much of which rely on procedural memory. Reliance on procedural memory may explain why some cognitively impaired drivers are able to drive without negative outcomes provided they are not presented with situations requiring quick judgments and response decisions to novel situations. The anterior portion of the frontal cortex, known as the prefrontal cortex (PFC), is the seat of executive functioning, although EF involves interconnections and relationships with other parts of the brain as well. The PFC is the focal component of a vast network of neuronal connections that link this area to the rest of the brain. The PFC is subdivided into three histologically, and probably functionally, distinct regions: dorsolateral, mesial, and orbital (Bechara, Damasio, Tranel, & Damasio, 1997). The dorsolateral region appears to be involved with “cool” executive functions involving abstract thinking and problem solving, whereas the orbitofrontal region is associated with “hot” executive functions involving the mediation of emotional triggers and cues (Rosenzweig, Leiman, & Breedlove, 1999). Although both cool and hot EF undoubtedly play a role in the performance of the driving task, it is highly likely that hot EF is associated with the more hazardous driving tasks, where increased stress or panic related to driving events may serve as the emotional trigger. The development of EF occurs over the course of early childhood and adolescence, reaching a peak in early adulthood. Although development is continuous, there are three specific stages in which growth occurs rapidly. The first stage occurs at the end of the first year of life when infants demonstrate less stimulus-bound and impulsive behavior and begin to interact meaningfully with the environment. The second stage occurs in preschool years as children begin to be able to modulate their own behavior based on complex learned rules, begin to contemplate the past and future, and begin to see situations from another person’s vantage point. The third drastic stage of change occurs at approximately the age of puberty when motivations such as romantic passions

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and desires compete with complex learned rules. EF begins to decline gradually with age. Elderly adults exhibit poorer performance on certain tests of EF, such as the Stroop task, compared to young adults (Zelazo, 2006). Overall, the development of EF can be represented by an inverted Ushaped curve, with functions learned in the late stages of childhood and adolescence being the first functions to be lost with aging (Hongwanishkul, Happaney, Lee, & Zelazo, 2005; Zelazo, 2006). Executive function is composed of various subfunctions, but there is no consensus regarding the exact identity of these subfunctions. The three most widely reported subfunctions are inhibition of competing responses, working memory or planning, and attentional flexibility or the ability to shift between tasks. Other postulated subfunctions (which overlap to varying degrees) include decision making, initiation, inhibition, self-monitoring, and task persistence and maintenance of set, all of which would be required in the driving task. In addition to identifying EF through the demonstration of increased self-control, abstract thinking ability, and problem-solving skills over the course of human maturation, EF has been studied and characterized historically by its effects on behavior in patients who experienced damage to EF through traumatic brain injuries. People with damage to the PFC display a range of executive dysfunctions, often depending on the area of the PFC that has been damaged. Patients with damage to the dorsolateral PFC may experience dysexecutive-type syndromes involving reduced judgment, planning, insight, temporal organization, and self-care. Patients with damage to the orbitofrontal PFC may exhibit disinhibited-type syndromes such as distractibility, emotional impairment, and increased stimulus-driven behavior. Patients with damage to the mediofrontal PFC demonstrate apathetic-type syndromes, including decreased spontaneity, verbal output, and motor behavior and increased response latency (Rosenzweig et al., 1999). A growing number of clinical and developmental studies are contributing to our understanding of EF and how it modulates human behavior and action. Focusing on specific subfunctions has been one approach to increase our understanding. A number of studies have attempted to determine which subfunctions work alone and which are interconnected through “loading” these subfunctions in certain tasks of EF. For example, Bechara and colleagues (1997) studied EF in patients with either ventromedial or dorsolateral/high mesial PFC lesions to clarify the distinctions between working memory and decision making. Patients were able to complete EF tasks to varying degrees of success depending on the location of the lesion. Results indicated that ventromedial PFC structures were involved in decision making, whereas some portions were also involved in working memory. Dorsolateral/high mesial PFC regions, however, appeared to only be involved with working memory.

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Older Drivers

Miyake, Friedman, Emerson, Witzki, and Howerter (2000) demonstrated the complex nature of EF by exploring the independence of three subfunctions (shifting, updating, and inhibition). They compared the performance of healthy college students on tasks that loaded these three subfunctions to performance on more traditional tasks of EF measuring multiple subfunctions. Factor analysis confirmed that shifting, updating, and inhibition were significantly independent constructs, although they were moderately correlated. Interpretation of the more complex, traditional measures of EF showed that each of these tasks emphasized one or more of the three subfunctions. One of the tasks, however, a dual-function task, did not correlate with any of the three specified subfunctions, suggesting the presence of a fourth subfunction, yet to be determined.

2.2.1. Hot and Cool Executive Function To date, most studies have focused on the cool aspects of EF, such as planning and abstract thinking. There is a growing body of research examining the role of emotion in decision making. Kerr and Zelazo (2004) studied the development of emotional decision making in children ages 3 and 4 years through their performance on gambling tasks. The younger cohort of children had trouble with these tasks, consistently choosing from more disadvantageous decks. By contrast, the 4-year-olds were more likely to choose from advantageous decks. Hongwanishkul and colleagues (2005) also focused on hot and cool EF development in young children. Their findings corroborated evidence that hot and cool EF develop rapidly in preschool-age children. In addition, the authors found that the two measures of cool EF included in the study were positively correlated with each other. Scores on tasks involving hot EF, however, were found to be negatively correlated, indicating the need for reinvestigation of this construct. Analysis of the relationships between hot and cool EF and other measures of general intellectual function demonstrated that scores on tasks involving cool EF were correlated with measures of general intelligence and temperament, whereas scores on tasks measuring hot EF were not correlated with scores for either of these. The authors surmised that hot EF may be more correlated with measures of emotional intelligence, which were not included in the study. Overall, these findings indicate that cool EF, which has been the subject of a relatively large number of studies, is better understood and characterized than hot EF. The focus in all these studies has been the development of hot and cool EF in children and on the role of emotion in decision making. In studies of adults, a more clinical approach has been taken to examine the role of emotion in EF in subjects with significant frontal lobe impairments. Dolcos and McCarthy (2006) studied the role of emotional distractions on EF, specifically working memory. Through the use of functional

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magnetic resonance imaging, they demonstrated the interruption of activity in the dorsolateral PFC and the lateral parietal cortex by emotional distractions during tasks of working memory. When subjects were shown images of high emotional content, such as images of violence, activity in the dorsolateral prefrontal cortex and the lateral parietal cortex was temporarily decreased, whereas activity in the ventrolateral PFC and the amygdala (areas associated with emotional processing) increased. Subjects temporarily lost focus on the task at hand while they processed the emotional images that were presented to them. Interestingly, the patients who displayed the most activity in the ventrolateral PFC as a result of emotionally distracting images were the same patients who reported the least amount of distractibility and also displayed the least detrimental effects on their task scores. Although the study was carried out in a small population of healthy young individuals, it offers direct evidence of an interaction between the cool aspects of EF in the dorsal regions of the PFC and the hot aspects of EF in the ventral regions of the PFC. The results of studies conducted by Watts, Macleod, and Morris (1988) and Mayberg (1997) confirm interactions between these two regions in depressed patients. These patients had difficulty concentrating on the performance of tasks due to increased emotional distractibility. The separate conclusions reached by many studies confirm that EF is a complex entity involving the coordination of different subfunctions and different parts of the brain to complete a task successfully. The impairment of EF in patients with brain lesions associated with dementia and Alzheimer’s disease in later years of life is another vast realm of study. Many members of society suffer from these conditions, affecting their day-to-day lives and contributing to functional impairment, including impairments of EF. Although Alzheimer’s disease can impair the ability to carry out routine tasks, results have conflicted with regard to the role of EF in these impairments. Giovannetti, Libon, Buxbaum, and Schwartz (2002) found that successful completion of everyday activities in patients with early stages of Alzheimer’s disease was more correlated with scores on global measures of cognitive ability than with specific measures of EF. Other studies have found a negative correlation between successful completion of performance tasks and dementia severity (Feyereisen, Gendron, & Seron, 1999; Skurla, Rogers, & Sunderland 1988). Nadler, Richardson, Malloy, Marran, and Brinson (1993) demonstrated a correlation between dementia severity and scores on tests of EF. Based on these findings, it appears that global EF impairment is not a strong predictor of ability to complete day-to-day activities in patients with early stages of Alzheimer’s disease given the learned and practiced nature of these activities, although progression of the disease will lead to increased impairments in EF. However, with the exception of driving, overlearned daily tasks

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generally do not require constant monitoring, attentional shift, updating, and rapid decision making. This monitoring, surveillance and driving task performance may be greatly affected by emotional distractions. Hot EF presents an intriguing area for the study of driving skill decline in the elderly.

2.2.2. Executive Function and the Driving Task Driving presents an interesting set of conditions because the driving task relies on learned and practiced or routine skills while also requiring the driver to have intact abilities to make novel and sometimes split-second judgments and decisions. Several studies have confirmed a significant relationship between global measures of EF and the driving task. Whelihan, DiCarlo, and Paul (2005) demonstrated a positive correlation between the results of on-road driving test scores of drivers with mild dementia and scores on EF and visual attention tests. Scores on other neuropsychological tests did not demonstrate a significant correlation, suggesting that the skill decline was not just a measure of general cognitive decline, and it may help explain why some individuals with dementia must stop driving, whereas others continue to drive safely for some time. Other studies have also shown that drivers with Alzheimer’s disease (having a Clinical Dementia Rating of 0.5e1.0) demonstrate impaired driving (Rizzo, McGehee, Dawson, & Anderson, 2001). In their driving simulator study, Rizzo, Reinach, McGehee, and Dawson (1997) compared driving performance of impaired drivers to performance of healthy controls, finding that the drivers with dementia experienced significantly more close calls and actual crashes than did the control group. Anderson, Rizzo, Shi, Uc, and Dawson (2005) also compared simulated driving performance and neuropsychological test scores in elderly drivers with impaired cognitive abilities. A composite score was derived from results of all neuropsychological test scores, including one for EF. This composite score was significantly correlated with a composite score based on the driver’s driving performance. The driving score was also significantly correlated with individual neuropsychological test scores, particularly EF. Drivers who collided with an intruding vehicle during an intersection sequence of the scenario were especially likely to perform poorly on the tests of cognitive ability. These crashers demonstrated specific problems with EF tests (in addition to problems with visuomotor abilities). A meta-analysis of 27 primary studies examining the relationships between neuropsychological test scores and driving performed by Reger and colleagues (2004) found that tests of visuospatial skills and EF (especially attention and concentration) demonstrated a significant relationship with driving performance on non-road tests (e.g., self-reports, driving records, and proxy ratings). Other tests of global

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cognitive ability, such as the Mini Mental State Exam, only demonstrated correlations with drivers in populations of elderly drivers with mid- to late-stage Alzheimer’s disease. In some studies, a substantial minority of cognitively impaired individuals demonstrated driving competence that was judged to be normal (Carr, Duchek, & Morris, 2000; Duchek et al., 2003; Friedland et al., 1988; Lucas-Blaustein, Filipp, Dungan, & Tune, 1988). Such competence was maintained over a 2-year period (Duchek et al., 2003), supporting high individual variability in the rate of decline in driving skills among individuals with cognitive impairment. Although the overall effect of global executive dysfunction on driving ability in the elderly population has been studied, relatively few studies have focused on the detrimental effects of specific impaired subfunctions on driving in this population. Uc, Rizzo, Anderson, Shi, and Dawson (2005) focused on a specific aspect of EF, attentional flexibility, in their investigation of the ability of drivers with early Alzheimer’s disease to identify landmarks and traffic signs during an on-road drive. Compared to healthy elderly controls, drivers with cognitive impairments due to Alzheimer’s disease were much more likely to overlook targets and to commit driving errors while searching for the targets. The two tests of EF included in the test battery were significantly correlated with driving performance, demonstrating impairment in ability to switch between two concurrent tasks. Examining the role of specific subfunctions in driving may help to elucidate the nature of EF as well as the effect of specific clinical impairments, such as Alzheimer’s disease, on driving.

2.3. Costs Associated with Motor Vehicle Injuries Each year, as many as 140,000 older adults are injured in motor vehicle crashes (NHTSA, 2004). In 2003, persons 70 years old or older accounted for 9% of the U.S. population and 10% of licensed drivers but 5% of driving-related injuries and 12% of both all-traffic and occupant fatalities. This sector of the population is growing at a rate faster than the total population. The risk of being involved in a crash increases significantly at age 70 years (Li, Braver, & Chen, 2003). The oldest drivers, those 75 years old or older, crash at a rate second only to the youngest drivers, those up to 24 years old. Older drivers also experience a higher rate of vehicular fatalities than any other adult age group (Wang & Carr, 2004). Unlike other age groups, older adult drivers involved in fatal crashes had the lowest blood alcohol levels, suggesting alcohol is not a major cause of vehicular fatalities among older drivers. The two major contributing factors to this high rate of vehicular fatalities are (1) an increased crash rate per mile driven, and (2) an increased risk of fatality as the result of a crash. Both driver characteristics

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Older Drivers

(e.g., use of prescription medications, impaired visual processing, reduced useful field of view, diabetes, coronary heart disease, or cognitive impairment) and environmental factors (e.g., intersections, left turns, and uncontrolled and stop sign-controlled locations) have been associated with increased crash risk in older people (Owsley et al., 1998; Preusser et al., 1998; Sims et al., 1998; Wallace, 1997). The NHTSA (2005) estimated that highway crashes cost U.S. society approximately $230.6 billion a year, with each fatality costing an average of $977,000, and each critically injurious crash estimated at an average of $1.1 million (NHTSA, 2003). These accidents also exact costs due to loss of work and productivity both for younger workers hurt by older driver crashes and for older workers who have remained in or re-entered the workforce. Based on 2001 data, an average of 28 days were lost from work per crash, or 60 million days lost during the year, yielding an annual $7.5 million loss in productivity (Ebel, Mack, Diehr, & Rivara, 2004). Personal and societal costs, although more difficult to quantify, are no less serious for crash victims and their families. The psychosocial costs associated with older driver safety should not be discounted. Driving is an important part of mobility and socialization for older adults. As Marottoli and colleagues (1997) have reported, driving cessation is a major decision because it is associated with an increase in depressive symptomology and potential social isolation. Access to friends, families, employment, shopping and commerce, personal care, social interaction, educational and cultural enrichment, and religious expressiondnearly all the benefits of modern societyddepends on our ability to transport ourselves from one location to another. Higher levels of mobility mean higher levels of access, choice, and opportunity, which can lead to self-fulfillment and enrichment. Lower levels of mobility can lead to isolation and cultural impoverishment. Many older drivers restrict or stop driving voluntarily, but a large number continue to drive. Clinical and policy-related discernment of driving fitness must therefore be carefully executed and guided by proper testing and screening methods. As the numbers of older drivers increase, it becomes essential to optimize driving for capable older adults and otherwise ensure that only those who pose no risk to themselves or others on the road continue to drive. Understanding risky patterns of behavior and the contribution of specific EF subfunctions to specific driving errors contributes to this goal.

2.4. Common Driving Errors Although we have information about certain types of driving errors committed by older drivers, the depth of information is highly variable. For many errors, we know only that they occur but have little or no information about the underlying cognitive issues at play. Here, we present an

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overview of what is known about the specific errors overrepresented in the older driver population and that are believed to be associated with EF.

2.4.1. Pedal Errors Crashes continuing to receive national attention involve older drivers who confuse the gas and brake pedals. The most widely publicized crash occurred on July 16, 2003, when an 86-year-old man drove his car into an outdoor market in Santa Monica, California, killing 10 people and injuring 45 others (NTSB, 2004). Numerous other examples have been reported, including an 81-year-old man who drove his car through a sandwich shop in Chicago, killing 1 and injuring 2 others (Thomas, 2004), and an 84-year-old Portsmouth, Virginia, woman who drove her car into a local fast-food restaurant. In all cases, drivers reported that they mistakenly pushed the accelerator instead of the brake. Termed unintended acceleration, drivers experience full, unexpected acceleration, often colliding with nearby objects and resulting in injuries or death. Examination of the vehicle immediately after the incident reveals normally functioning brake and fuel delivery systems (NHTSA, 2004; Schmidt, 1989). The cause is not well understood, although the contribution of impaired EF has been demonstrated (Freund, Colgrove, Petrakos, & McLeod, 2008). Unintended acceleration demands error correction in a situation that is potentially hazardous and necessitates a course of action that goes against strong habitual response (Freund et al., 2008).

2.4.2. Other Driving Errors Dobbs, Heller, and Schopflocher (1998) identified errors that distinguished cognitively impaired drivers from unimpaired older (mean age, 72 years) and young drivers by a driving test (N ¼ 100). A triad of error sets were made by those with cognitive impairment. The first was termed hazardous or potentially catastrophic, and it was made only by drivers with dementia. These included errors that could have resulted in a crash if the driving evaluator had not intervened or the traffic adjusted. The second driving error set was committed by all groups, but the frequency and severity discriminated among the three groups. Errors were most frequently made by the impaired groups, often by older control subjects and rarely by the younger control subjects. These errors included turning position errors and observational errors. Finally, a set of errors that would fail drivers on traditional licensing tests (e.g., rolling stops and speeding) did not differentiate drivers. In another study, it was found that hazardous errors were the single best indicator of assignment to the older

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impaired group (Dobbs et al., 1998). The older impaired group differed from the controls on turning position errors, minor positioning errors, and overcautiousness errors. The errors accounted for 57% of the variance associated with a global rating provided by expert driving evaluators. Group means for error categories and for hazardous error, minor positioning error, turning positioning errors, scanning errors, and overcautiousness were the best measures for discriminating drivers with dementia from nondemented older and younger drivers. In their study of driving errors and test outcomes for older drivers, Di Stefano and Macdonald (2003) reported findings similar to those of Dobbs et al. (1998): 56% of hazardous errors requiring evaluator intervention occurred during intersection negotiation, lane changes, and merging. The next most common errors involved position errors.

2.4.3. Lane Position Errors Lane position errors range from straddling dividing lines to driving in the lane of oncoming traffic or wrong-way driving. A study by the North Carolina Department of Transportation indicated that drunk drivers and older drivers are more likely to drive the wrong way than are other drivers (Braam, 2006). For example, an 80-year-old woman died in a crash after driving south in the northbound lane of Interstate 380 in Center Point, Iowa. Another 80-year-old driver was stopped by deputies near Raleigh, North Carolina, after driving south in the northbound lane of Interstate 40 for 14 miles. The driver reported that she did not realize she was in the wrong lane (Braam, 2006). NHTSA’s (1999) older driver error classification study employed videotaping of drivers completing two on-road testsda standard route and a home (familiar) route. The results showed that regarding lane position, some older drivers (in both test situations) executed turns from the wrong lane (4e13%), drove in the far right of lanes or in parking or bike lanes (3e10%), and drifted in and out of lanes (10e15%).

2.4.4. Crashes Preusser and colleagues (1998) compared crash risk of older drivers to that of middle-aged drivers. Older drivers were two times more at risk for multiple-vehicle crashes at intersections. For those 85 years old or older, the risk increased to 10 times for multiple-vehicle crashes at intersections. The crash risk was high at uncontrolled or stop sign-controlled intersections, when driving straight or when starting to enter the intersection. The main driver error in these events was failure to yield. For two-vehicle fatal crashes with an older driver or a younger driver, the older driver was twice as likely to be struck as the younger driver. In 27% of these (7 times more often than the younger driver), the older driver was turning left (NHTSA, 2004). Intersection crashes also occur when the driver is hit

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from behind. In the NHTSA (1999) older driver study, the drivers failed to observe traffic behind them before decelerating for an intersection 87e96% of the time.

2.4.5. Compliance with Traffic Control Lights We rely on the NHTSA (1999) older driver error classification study to obtain frequency data for these errors. Some older drivers ran red lights (5e7%) and stop signs (3e6%). Some sat at green lights waiting to make right-hand turns (2e8%). A larger percentage (26e39%) stopped for no reason (e.g., in the middle of an intersection, mid-lane on approach to turn, uncontrolled right turns, and before pulling over to park). No information was provided about health or cognitive status of these drivers. Perhaps the low percentage of drivers running red lights and stop signs is reflective of higher functioning, community-dwelling older drivers. The routine nature of the driving maneuver may also contribute to the low error rate. Interestingly, the maneuvers requiring judgment and planning (approaching turns and uncontrolled turns) proved difficult for a larger percentage of these older drivers and may be indicative of greater demands on EF.

2.4.6. Speed Control Again, we must rely on the NHTSA (1999) study of driver errors to describe errors related to speed and control of speed. Although up to 24% of the study drivers drove 10e25 mph below the speed limit, 4% exceeded the speed limit on the standard route and 15% exceeded the speed limit on the familiar route. In addition to measures of actual speed, the NHTSA study presents data on braking maneuvers for speed control. Drivers were almost twice as likely to execute a hard braking maneuver on the home or familiar route compared to the standard route. Both of these errors on the familiar route may be related to issues of attention; drivers may not be as vigilant monitoring their surroundings in familiar places. However, in the absence of additional data, this conclusion remains speculation. Much of what is reported in the driving literature, as presented, is mirrored in older driver evaluation clinics (Freund, Colgrove, et al., 2005). Research to date reveals agreement on the driving errors that distinguish the safe from the unsafe driver. Understanding the underlying cognitive contributions to those errors will aid the development of interventions to allow drivers to safely drive as late in life as possible and to assist those retiring from driving to transition prior to a negative event and outcome.

2.5. Physical Changes Associated with Aging and Their Impact on Safe Driving All machines wear out due to use and even after long periods of disuse. Electrical systems become less conductive, distribution lines clog with residue, and moving parts

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Older Drivers

lose lubricant and are deformed and stiffened by friction and resultant heat. Connective and exterior casings are subject to oxidation and corrosion from external sources. Human bodies are machinesdmarvelously intricate and enormously complex machines. Every part of the human body is affected by the normal process of aging, and in many instances, these normal changes affect, to some degree, the ability to drive safely. In addition to simple wear and tear, there is increased susceptibility to pathological disease. The greater the age, the greater the risk factors for various diseases. Deterioration due to aging occurs at the cellular level, and all cells are affected. Because tissues are composed of cells, and all organs are composed of one of the four basic types of tissue (connective, epithelial, muscle, and nerve), organs and body systems inevitably change with aging. “Aging organs gradually but progressively lose function, and there is a decrease in the maximum functioning capacity” (Martin, 2007, p. 143). Cellular waste products and fatty substances such as lipofuscin accumulate in many tissues. Cell membranes become less permeable, and this makes it more difficult to receive oxygen and nutrients and eliminate carbon dioxide and waste. Aging organs, unable to manage fuel/waste exchange properly, become less efficient. Most older adults have at least one chronic medical condition, and many have multiple conditions. Medical conditions that are more common in the elderly population include sleep apnea, dementia, Alzheimer’s disease, arthritis, Parkinson’s disease, diabetes, hypoglycemia, and the residual affects of strokes and physical limitations after myocardial infarctions and/or heart procedures/surgeries. In this section, we review the major age-associated changes and medical conditions that impact safe driving. These conditions are organized by system categories, and each provides examples of how driving may be affected. It is important to remember that although the prevalence of driving difficulties increases with age, “at-risk” and “older” are not necessarily synonymous. Visual, cognitive, and physical problems can occur at any age and complicate the driving task. Similarly, old age alone does not necessarily indicate the presence of any of the characteristic older driver problems.

2.5.1. Visual Impairment The most common sensory problem affecting older adults is visual impairment. This may range from mild to severe, and it may be sufficient to prevent driving (AARP, 2005). Vision, which tends to rapidly deteriorate in function among the elderly, is integrally involved in driving activities. The decrease of vision acuity may be due to any one or a combination of causes prevalent among the elderly. For example, thickening of the lens of the eye can interfere with close-up

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acuity and slow refocusing on distant targets. The lens also tends to yellow with age, which can reduce color perception. Opacity of the lens, known as cataracts, is the most common cause of vision impairment among adults older than age 65 years, with up to 90% experiencing some degree of impairment (Venes, Biderman, Adler, & Enright, 2001). With age, the lens becomes more opaque, leading to clouded vision, color distortion, and light sensitivity. Cataracts cause decline in acuity and greater vulnerability to glare. This is particularly significant when one considers that for older adults in general, there is more difficulty accommodating changing light levels (e.g., a 55-year-old takes eight times longer for glare recovery compared to a 16-year-old; NHTSA, 2003). The older adult with cataracts will experience greater difficulty with driving situations involving glare. Glaucoma is the third most prevalent cause of blindness in the United States. Onset can be signaled by mild pain, visual disturbances, poor night vision, a halo effect around lights, and impaired peripheral vision. Loss of peripheral vision may cause difficulty noticing signs or cars and pedestrians about to cross the driver’s path. Age-related macular degeneration (AMD), the leading cause of vision loss in people older than 65 years of age, is characterized by degeneration of the macula, the area of the retina responsible for central vision (Quillen, 1999). AMD has perhaps the most dramatic effect on driving safety, resulting in an inability to perform visual tasks necessary for safe driving. Corneal flattening occurs during the aging process, allowing less light to enter the eye. With less light entering the eye, driving at dawn, dusk, and night becomes more difficult. Also common among the elderly is a decline in retinal acuity related to decreased blood supply and the cumulative effect of radiation damage causing a decrease in depth perception and peripheral vision as well as an increase in sensitivity to bright sunlight and glare conditions. External to the eye, but of significance to the driving task, is the presence of ptosis: The upper eyelid droops down over the eye, causing external obstruction of view. The eyelid can significantly limit the visual axis of either or both eyes. This can be corrected surgically and visual field can be restored. A frequent nonpathological cause of impaired vision among the elderly is uncorrected refractive error. This may be due to the fact that elderly drivers may not notice a gradual decline in visual acuity, or because they do not have routinely scheduled vision examinations. For this reason, elderly people may often neglect to update eyeglass prescriptions. Many of the visual conditions described are overrepresented in people who have diabetes. Diabetics are 60% more likely to develop cataracts than are nondiabetics, and the onset is at an earlier age and the progression more rapid. When vision is greatly affected, treatment involves the removal of the lens and insertion of an intraocular lens.

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Unfortunately, among diabetics, retinopathy often worsens after removal of the lens, and glaucoma may ensue (American Diabetes Association, 2010). Patients with diabetes are at a greater risk for eye complications, including glaucoma, cataracts, and diabetic retinopathy. Diabetics are 40% more likely to suffer from glaucoma than are people without diabetes (American Diabetes Association, 2010). The incidence of glaucoma is higher for those with a long history of the disease. Advancing age is another risk factor. When the intraocular humor builds too rapidly in the anterior chamber of the eye or drainage slows, pressure builds in the eye. This increased pressure pinches the blood vessels that carry blood to the retina and optic nerve causing retinal and nerve damage, eventuating gradual loss of vision. Diabetic retinopathy is a general term for all disorders of the retina caused by diabetes. The retina is damaged by several types of vascular changes, which may ultimately result in blindness. Keeping blood pressure and blood sugar within normal limits can reduce the incidence and prevent vision changes that would prevent driving.

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Autonomic neuropathy affects the inner organs in their regulation and, more to the focus of this discussion, response to sudden changes in environment, such as imminent danger or signs of hypoglycemia. Focal neuropathy results in the sudden weakness in any nerve in the body, which can cause muscle weakness and pain. Proximal neuropathy affects the legs, causing pain and weakness in thighs and hips. Epilepsy is a neurological condition characterized by lapses of consciousness and is a condition required to be reported by drivers. Nearly 2.5 million people in the United States have epilepsy, with 150,000 developing the condition each year. New cases of epilepsy are most common among children and older adults (Centers for Disease Control and Prevention, 2010). It is generally agreed that anyone with acute motor, sensory, or cognitive deficits should not drive. Temporary driving cessation may be recommended until further neurological recovery has occurred. Once neurological symptoms have stabilized, drivers with residual sensory loss, cognitive impairment, visual field deficits, and/or motor deficits should be referred for formal driver evaluation.

2.5.2. Neurological Impairment In the older population, the prevalence of cognitive impairment and dementia is alarmingly increasing and, as discussed previously, significantly affects the ability to drive safely. Alzheimer’s disease is the most common type of dementia, afflicting an estimated 5.1 million Americans aged 65 years or older, or 1 in 8 people aged 65 years or older (Alzheimer’s Association, 2010). Women are more likely than men to have Alzheimer’s disease and other dementias, primarily because women generally live longer than men. As the brain and nervous system age, changes occur that affect reflexes, movement, coordination, and balance. These normal changes are separate from the degenerative brain disorders such as dementia, Alzheimer’s disease, or delirium. With decreased cerebral blood flow, the brain has reduced oxygenation, and waste products tend to accumulate. Lipofucin may also build up in nerve tissue. There is a decreased production of neurotransmitters leading to a corresponding reduction in synaptic transmission. Simply stated, the messages from the various senses to the brain, and thence from the brain to the various muscles directing appropriate response, are slower and sometimes do not get through at all. A decrease in the synaptic transmission of nerve tissue can also cause a reduction or loss of reflexes such as deep tendon reflexes. Throughout the body, an elderly driver may experience peripheral, autonomic, proximal, and focal neuropathy. The symptoms may include numbness, pain, and weakness. Peripheral neuropathy can impair a driver’s awareness of placement and pressure of his or her hands and feet. The driver has difficulty knowing not only which pedal he or she pushes but also how much pressure he or she is applying.

2.5.3. Cardiovascular System With aging, the cardiac output and stroke volume reduce, particularly during exertion. Shortness of breath during physical exertion and pooling of blood in the extremities are related to increased rigidity and thickness of heart valves. Vessels have reduced elasticity and increased peripheral resistance due to decreased contractile strength. Both systolic and diastolic blood pressure increase due to the inelasticity of systemic arteries and increased peripheral resistance. Hypertension is a known risk factor for Alzheimer’s disease and dementia. Untreated hypertension can cause stroke (cerebral vascular accident (CVA)). Causes for stroke include atherosclerosis, thrombosis, embolism, and cerebral hemorrhage. CVA occurring in the left hemisphere of the brain can cause right-sided weakness and aphasia. CVA occurring in the right hemisphere can cause left-sided weakness and perceptual deficits. Patients who have perceptual deficits are vulnerable to accidents, especially when driving. Pupillary anomalies and visual field defects make it unsafe to drive because drivers are unable to see and respond to certain events in the driving environment. Other deficits include loss of voluntary movement on one side of the body. Hypotonia (flaccidy) and hyptertonia (spasticity) may occur, which can cause the patient difficulty controlling muscle tension. In apraxia, they are unable to perform purposive movements. Emotional lability (excessive emotional reactivity associated with frequent changes or swings in emotions and mood) may affect a driver, especially if the driver is frustrated. Lastly, patients with cardiovascular disorders may have varying degrees of impaired judgment and memory.

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When a stroke occurs, driving safety may be compromised both acutely and chronically because the brain can suffer residual effects of stroke. Areas in the brain that are needed to ensure safe driving include motor control, reflexes, and perception. Neglect, a condition that may occur with a stroke, affects driving safety. The person is incapable of acknowledging (receiving and responding) stimuli on one side of his or her body. Neglect occurs most often in the right hemisphere of the brain and results in inattention to the left side. Patients who have a medical diagnosis of unstable coronary syndrome with angina pectoris (chest pain) or congestive heart failure with low output syndrome should not drive if they experience symptoms, such as severe dyspnea (difficulty breathing) at rest or at the wheel. Patients who experience cardiac conditions that may cause a sudden, unpredictable loss of consciousness with risk of presyncope or syncope related to bradyarrhythmia or tachyarrhythmia should not drive until the condition is treated and controlled. Coronary artery bypass graft (CABG) surgery may also have negative driving outcomes. An increasingly widely recognized complication of CABG surgery, particularly in the elderly population, is postoperative cognitive decline. Fifty to eighty percent of patients undergoing CABG surgery with cardiopulmonary bypass are discharged from the hospital with significant postoperative cognitive dysfunction (POCD) (Newman, Kirchner, et al., 2001). Importantly, cognitive impairment following CABG surgery has been shown to negatively affect driving performance in older adults at 4e6 weeks postsurgery (Ahlgren, Lundqvist, Nordlund, Aren, & Rutberg, 2003). No studies have examined driving performance past the 6-week postoperative time point, but POCD has been shown to persist for months and years. Furthermore, whereas on-pump (cardiopulmonary bypass pump) versus off-pump studies have had mixed results regarding cognitive outcomes, studies examining the effects of off-pump intervention on postoperative driving performance are lacking. Cognitive impairment and dementia are surprisingly prevalent among older apparently healthy individuals, affecting up to one-third of people older than age 65 years, but it remains undiagnosed in 25e90%dapproximately 5 million people (Callahan, Hendrie, & Tierney, 1995; Finkel, 2003; Ross et al., 1997; Valcour, Masaki, Curb, & Blanchette, 2000). This population may be at greater risk for developing persistent POCD. Indeed, among some individuals, POCD symptoms have been measured 5 years postsurgery, with some studies reporting that as many as 42% have it (Newman, Kirchner, et al., 2001). The evidence of cognitive decline persisting postoperatively for months and even years is at odds with common practice guidelines to resume driving at 6 weeks based on sternum healing (Society of Thoracic Surgeons,

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2008). It is imperative to consider cognitive consequences when making recommendations to resume driving, but there is a knowledge gap regarding when it is safe to do so.

2.5.4. Respiratory System Diseases such as emphysema, chronic bronchitis, and chronic obstructive pulmonary disease (COPD) are commonly found in the elderly population. Patients with this progressive respiratory disease need periodic re-evaluation for symptoms and oxygenation status by a pulmonologist. In the aging lungs, COPD, if exacerbated, can cause a patient to suffer shortness of breath at rest or at the wheel with minimal exertion (even with supplemental oxygen). Excessive fatigue or significant cognitive impairment can occur that would compromise the ability to drive safely. If the patient is unable to maintain a hemoglobin saturation of 90% (oxygen saturations of 95% or higher are normal), he or she should use oxygen at all times, especially when driving. In COPD, the respiratory muscles become more rigid, causing decreased vital capacity and increased residual capacity of lungs. Gas exchange is less effective, as is the cough mechanism, and shortness of breath may occur during times of physical stress. Patients with COPD should be counseled by their physician not to drive, especially if they have an illness or exacerbation of COPD, such as new cough, increased sputum production, change in sputum color, or fever. These are all signs or symptoms of infection. Early clinical manifestations include restlessness, anxiety, and altered level of consciousness.

2.5.5. Endocrine System The primary endocrine medical condition of concern that impacts driving is diabetes. Diabetes is more common among the elderly and has many associated effects, including impairment of vision, neuropathy, and unstable glucose levels. Untreated hypoglycemia may cause seizures and/or loss of consciousness, significantly impacting driving safety. Diabetes is a known risk factor for Alzheimer’s disease and dementia.

2.5.6. Musculoskeletal Impairment The normal physical changes that occur in the aging musculoskeletal system include decreased muscle mass (atrophy) and elasticity. Muscle strength is reduced, affecting the elderly person’s balance and range of motion and strength. Diminished proprioception (the ability to relate posture, proximity, and spatial reference to the body and environment) interferes with balance and coordination. Joints stiffen, making movement painful. Decreased physical activity affects muscle strength and tone. Impaired muscle strength can affect balance and decrease speed and

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power of skeletal muscle contraction. This causes slowed reaction time and additional loss of coordination, which is necessary for safe driving. More common in the older driver, osteoarthritis can cause pain and stiffness in weight-bearing joints such as the knees and hips. This condition may necessitate joint replacement and a very rigorous postoperative physical therapy regimen. Rheumatoid arthritis, an autoimmune joint disease, occurs in synovial joints causing inflammation, deformity, contractures, stiffness, and pain. These joint conditions affect mobility and range of motion, which can limit how far the driver can bend or move his or her shoulders, hands, head, and neck. This can make it more difficult to grasp or turn the steering wheel, apply the brake and gas pedals, put on the safety belt, and enter and exit the vehicle. The medications prescribed for these include analgesics and muscle relaxants, both of which can affect the older driver’s judgment and ability to focus on the environment. The neck and spine change with age, causing stiffness and, at times, curvature of the spine, negatively affecting the ability to look over the shoulder to check the blind spot. Coupled with loss of peripheral vision, it becomes more difficult to view traffic to the sides and rear of the vehicle.

2.5.7. Influence of Medication The liver and kidneys are the major organs responsible for drug clearance from the body, and the liver decreases in size after age 70 years. Liver dysfunction and subsequent decrease in enzyme function limit the ability to metabolize and detoxify drugs, thereby increasing the risk of drug toxicity (Kee, Hayes, & McCuistion, 2009). Drug accumulation can result in reduction in metabolic rate. Older adults also experience decreased renal function, decreasing drug excretion. Drug accumulation and drug toxicity can occur (Kee et al., 2009). Medications may have side effects that can affect driving performance. These include drowsiness, dizziness, blurred vision, unsteadiness, fainting, slowed reaction time, and extrapyramidal side effects (Carr, Schwartzberg, Manning, & Sempek, 2010). To assess a medication’s potential for impairing driving safety, the physician may order the patient to have formal psychomotor testing and/or driver evaluation by on-road or driving simulation assessment. Among the classes of medications that affect the central nervous system in a way that would impair driving safety are alcohol, anticholinergics, anticonvulsants, antidepressants, antiemetics, antihistamines, antihypertensives, antiparkinsonians, antipsychotics, benzodiazepenes, sedatives, anxiolytics, muscle relaxants, narcotic analgesics, nonsteroidal anti-inflammatory drugs, and stimulants. Many older adults take one or more medications daily.

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Vulnerable and Problem Road Users

In considering the effects of alcohol as a drug, lower blood alcohol content (BAC) levels for older drivers may be exacerbated by potential interactions with many agerelated conditions, including chronic illness, polypharmacy, or an underlying cognitive impairment. The effects of alcohol on the central nervous system have been demonstrated at levels as low as 9 mg/dl or 0.009% BAC, a level just over one-tenth of the legal definition of intoxication in most states. Both age and alcohol habits alter alcohol sensitivity in the central nervous system. Alcohol intoxication is associated with impairment in simulated driving for both young and older men. The subsequent results are contrary to most pharmacological data in that older men did not achieve higher BACs from equivalent doses of alcohol compared to middle-aged men (Quillian, Cox, Kovatchev, & Phillips, 1999). Although it may be true that aging alone does not alter the rate at which alcohol is absorbed or eliminated, the body systems upon which alcohol works often change with increasing age. As we age, lean muscle mass decreases, fat tissue increases, and the result is an overall reduction in the volume of water in the body. With less water volume in which the alcohol may be distributed, equivalent amounts of alcohol will yield a higher BAC in older persons compared to size- and gender-matched younger persons (Dufour, Archer, & Gordis, 1992). Aside from this basic pharmacology, differential effects of alcohol consumption for older adults show great variability, with some research suggesting heightened alcohol sensitivity in older adults even after controlling for body water volume (Tupler, Hege, & Ellinwood, 1995). Other mechanisms specifically related to aging, such as reduced first-pass metabolism in the liver, may also be at play. For polypharmacy, or the concurrent use of many medications, it is not just prescription drugs that are of concern. Over-the-counter (OTC) preparations can be equally dangerous in terms of potential for interaction with other medications and alcohol. It is estimated that more than 90% of adults age 65 years or older use at least one medication (Kaufman, Kelly, Rosenberg, Anderson, & Mitchell, 2002). Unfortunately, many of the classes of drugs commonly used by older adults are also ones that pose the greatest risk for interaction with alcohol. Drug classes such as antihistamines, sedatives, some antidepressants, and other psychotropic drugs pose considerable risks, both for interaction with alcohol and an increased central nervous system depressive effect (National Institute on Alcohol Abuse and Alcoholism, 1995). A population-based study found that 38% of the older adults surveyed reported using both alcohol and a medication with a high risk for interaction. Antihypertensives, sedatives, and narcotics were on the list of commonly used high-risk drugs (Adams, 1995), which is a cause for concern given that drug interactions can occur with even

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Older Drivers

low to moderate alcohol use, resulting in a synergy of impairing substances that may go unrecognized in the driving context.

2.6. Psychosocial Challenges Among the most devastating consequences of cognitive decline and physical impairments are loss of independence and the resulting inextricably related outcome of diminished quality of life. Mobility in the United States, a key to independent function, is often dependent on the ability to drive. Indeed, many are reluctant to discontinue driving because of the lack of alternate transportation and unwillingness to be a burden through reliance on others for transportation. This is particularly true for individuals living alone, although other factors have been shown to contribute to continued driving. For example, in a study of driving determinants (Freund & Szinovacz, 2002), gender was found to be significantly related to driving, with men being less likely to restrict or cease driving than women. Cognition had a more pronounced effect on driving cessation of women than that of men, as did physical impairment. Women with cognitive impairments were more likely than men to stop driving or drive only short distances. Men with severe or mild limitations in activities of daily living were much more prone to continue driving long distances than were similarly disabled women. These findings suggest that gender roles may inhibit driving restriction or driving cessation among men. The finding that men with mild and severe cognitive and physical impairments were less likely than women to restrict or cease driving suggests that driving is more important to mens’ self-image. Life expectancy is exceeding driving life expectancy by as much as 6 years for men and approximately 10 years for women (Foley, Heimovitz, Guralnik, & Brock, 2002), during which older adults will require alternative transportation to meet their mobility needs. Currently, drivers and their families do not plan well, if at all, for retirement from driving. Nonetheless, as the population ages, increasing numbers of older adults will need to restrict or stop driving. The decision may be voluntary or involuntary and may place significant stress on family relationships. For many, the transition from driver to restricted or nondriver places additional burdens on adult children assuming a caregiving role for their parent. In our driving clinic, adult children often express discomfort in what they view as a parentechild role reversal. Many have reported that strained relationships continued after driving cessation. Barriers to driving cessation include denial or lack of awareness of declining skills and limited options for alternative transportation, including physical distance from family and friends. The expectation for family members or

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friends to assist the older adult who no longer drives is not viable today due to smaller, geographically distant families and fewer informal caregivers available to provide transportation. Many nondriving seniors must rely on public and community-based transportation services. At issue is that many communities do not offer these services, or if they are available, they are not equipped to accommodate the older person with physical and cognitive impairments. Reducing the negative consequences of driving cessation will require such improvements to existing public transport services to make them more acceptable and accessible for older adults (Marottoli et al., 1997) and the development/ replication of community-owned and -operated transport services that address the mobility needs of older adults who are no longer able to drive (Kostyniuk & Shope, 2003).

3. SUMMARY AND RECOMMENDATIONS The number of Americans surviving into their 80s and 90s and beyond is expected to grow dramatically due to a variety of factors, including advances in medicine and medical technology. Because the incidence and prevalence of Alzheimer’s disease and other dementias, as well as physical impairments and metabolic diseases, increase with age, the number of people with these conditions will also grow rapidly. Motor vehicle injuries are the leading cause of injuryrelated deaths among 65- to 74-year-olds and the second leading cause among 75- to 84-year-olds. Despite increases in safety belt use and advances in technology (driver and passenger air bags), fatality rates for older drivers have consistently remained high. Declining driving competence is associated with impairments in vision, functional abilities, and cognition, all of which have been linked to increased crash risk (Owsley et al., 1998; Sims et al., 1998; Wallace, 1997). Driving cessation can lead to lifestyle losses, reduced social interaction, restricted activity, and depression. Although many older drivers restrict or stop driving voluntarily, a large number continue to drive. To prevent injuries and to promote safety, it is imperative that resources and support be available for determining driver fitness. Equally important are considerations of supporting mobility needs and maintaining quality of life after driving cessation. For example, Independent Transportation Network (ITN; http://itnamerica.org) provides rides with door-to-door service for thousands of seniors nationwide. ITN programs allow older people to trade their cars to pay for rides, and they enable volunteer drivers to bank transportation credits for their own future transportation needs. ITN’s Road Scholarship Program converts volunteer credits into a fund for low-income riders, and the gift certificate program helps adult children support their parents’ transportation needs. As a result, seniors remain

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independent and vital to the economic and social health of their communities. Everyone wins.

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Vulnerable and Problem Road Users

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Chapter 25

Pedestrians Ron Van Houten Western Michigan University, Kalamazoo, MI, USA

1. THE NATURE OF THE PROBLEM Although there are many systems in place to guide and prompt behavior of motorists encountering pedestrians in crosswalks, there were approximately 4378 pedestrian fatalities and 69,000 pedestrian injuries in 2008 (U.S. Department of Transportation (DOT), 2008). It is also the case that more than two-thirds of pedestrian crashes occur in urban areas. In 2007, approximately 73% of fatalities occurred in urban areas. It is most likely this relationship is a function of the higher walking exposure in large cities. For example, Zhu, Cummings, Chu, and Xiang (2008) found that crashes in New York City were four times higher per resident year than the rate in rural areas of New York State. However, when the data were compared based on miles walked, the rates were similar. Data also show that more than two-thirds of fatal pedestrian crashes occur at night (DOT, 2008). Research has documented that drivers’ inability to see pedestrians at a safe distance is a major factor responsible for nighttime pedestrian crashes (Leibowitz, Owens, & Tyrrell, 1998; Rumar, 1990; Wood, Tyrrell, & Carberry, 2005). Alcohol use has also been documented to be a factor in night crashes, particularly on Thursday, Friday, and Saturday nights. Data show that approximately half of pedestrians killed in traffic crashes had been consuming alcohol (Wilson & Fang, 2000). It is interesting to note that the level of impairment of pedestrians in fatal crashes is quite high. For example, in Great Britain, there was a significant increase in crash risk for pedestrians with a blood alcohol content (BAC) higher than 0.20 and a non-significant effect for pedestrians with a BAC between 0.10 and 0.15. (Clayton, Colgan, & Tunbridge, 2000; Ostrom & Erikson, 2001). Blomberg, Preusser, Hale, and Ulmer (1979) showed a similar relative risk, with elevated risk beginning at a BAC of approximately 0.15. Pedestrian crashes are somewhat unique because the responsibility for them is not equally shared between drivers and pedestrians. In the case of pedestrian crashes, there are several reasons why the driver should be assigned a higher proportion of the degree of responsibility. First, drivers must meet minimum standards in regard to knowledge, vision, age, and demonstrated skill level that cannot be legally Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10025-6 Copyright Ó 2011 Elsevier Inc. All rights reserved.

required of walking. Second, it is often said that driving is a privilege and not a right. One cannot say the same for walking. Although it is possible to remove an individual’s driving permit, it would be a gross violation of human rights to deprive someone of the right to walk. Third, pedestrians include individuals who do not have the same physical or cognitive skills as drivers. For example, all children and some seniors do not meet the minimum standards required to operate a motor vehicle. Both these groups have been demonstrated to be deficient in judging gaps. Persons with physical challenges such as the blind pedestrians or persons with mental challenges also have a right to mobility. It cannot reasonably be asserted that a blind pedestrian has the same level of responsibility to avoid a crash as a licensed driver. Many state driving statutes recognize this by stating that motorists must yield to people with canes or guide dogs any time they enter the roadway. Finally, the due care provision of most state motor vehicle statutes make it clear that a motorist must take whatever action is necessary to avoid crashes. This is why a driver is expected to slow if he or she sees young children playing along the roadway and among many parked cars, even if the speed limit permits a higher speed. Another way of viewing this is that the driver has the weapon and therefore should exert due care in the operation of the vehicle. In many states, drivers operate as if the road is the exclusive domain of the automobile and that pedestrians should yield right-of-way to vehicles. Because of the unequal balance in responsibility, it is critical that we develop a safety culture that places the highest level of responsibility on the driver.

2. NEED FOR MULTIFACETED PROGRAMS Although traffic engineering treatments such as signals, signs, and markings, designed to prompt motorists and pedestrians to engage in safe behavior, can influence driving behavior, it is critical that the behavior, as well as the purpose for engaging in that behavior, be understood by the driver. Designed signals, signs, and markings that are intuitive can be helpful in achieving this result; however, 353

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public outreach efforts can also inform the public and potentiate the efficacy of these treatments. Enforcement can further enhance compliance by giving credibility to engineering and educational efforts. Retting, Ferguson, and McCartt (2003) have made the case for separating pedestrians and motorists in space or time and improving the conspicuity of pedestrians as basic strategies for improving pedestrian safety. These basic principles appear repeatedly in a number of effective countermeasures. Other effective countermeasures assist drivers to predict the presence of pedestrians. Traffic calming can also help potentiate the effect of other educational and enforcement programs.

3. ENGINEERING ELEMENTS 3.1. Signs and Markings Traffic control devices, such as signs and markings, are best thought of as stimuli that prompt appropriate driving and walking behavior. These prompts have the weight of law, and failure to respond to them can result in consequences. These devices are best viewed as discriminative stimuli for the availability of citations, point loss, direct loss of driving privileges, and, in the case of negligence, criminal penalties. They may also function to control behavior through rule governance. Several rules should be followed for prompts to be effective or to maintain their efficacy (Van Houten, 1998). One important rule involves the timing of the prompt. Prompts work best when they occur just before the behavior should occur. The second rule is that prompts should be located where they will be seen or heard. For example, a prompt that instructs someone how to use a new crossing feature should be placed where a pedestrian who could take advantage of the feature is likely to be looking when near the feature. Placement of the prompt not only increases the probability that the stimulus will be perceived but also can improve the timing of the prompt. Third, the prompt should be specific. General prompts that do not specify the behavior that is expected are less likely to succeed. An example of a specific prompt would be a sign that reads “Keep Right Unless Passing” versus “Slow Traffic Keep Right.” It is far less subjective to discriminate if you are passing than whether you are traveling too slowly. The fourth rule is that the prompt should guide the behavior over time. Examples are directional arrows or markings and audible signals for the blind. The fifth rule is that the prompt should remind the individual of the consequence of not responding to the stimulus. Placing the fine on the sign that specifies the rule to be followed is more likely to produce compliance than a sign that does not specify the consequence for failure to obey.

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3.2. Signals and Beacons Countdown pedestrian signals have been shown to improve pedestrian compliance at traffic signals and to reduce crashes between vehicles and pedestrians. These signals are more effective than regular pedestrian signals because they provide clear feedback on the time remaining to finish crossing the street. This enables pedestrians to discriminate whether they can cross based on their knowledge of their walking speed and the time remaining (Eccles, Tao, & Mangum, 2007; Markowitz, Sciotino, Fleck, & Yee, 2006). Signals that alert drivers or pedestrians when a danger is present are also more effective than those that are present all the time because stimuli that are always present have less discriminative value. Flashing beacons to warn drivers to look for a pedestrian using the crosswalk that are on all the time are far less likely to be attended to than a warning beacon that is only illuminated when a pedestrian is actually crossing the street.

3.3. Traffic Calming Typically, drivers are more likely to yield when they are traveling at lower speeds. There are many reasons for this effect. First, it is less effortful to stop when traveling slowly. Second, it disrupts drivers’ pace less when they are already traveling slowly. Third, drivers are less concerned about being involved in a rear-end crash when they are traveling slowly. Hence, slowing or “calming” traffic is one way to increase drivers yielding right-of-way to pedestrians. Some common methods involve horizontal or vertical deflections of the roadway. Replacing a regular signal or stopcontrolled intersection with a roundabout is one way to use a horizontal deflection to slow vehicle speed.

3.3.1. Speed Humps and Speed Tables Speed humps and speed tables are two ways to use vertical deflection to slow speeds. One advantage of speed humps and speed tables is that speed humps can be installed just before crosswalks and speed tables can elevate crosswalks. There are three potential drawbacks to these treatments. First, there is moderate cost associated with installing these road features at regular intervals. Second, the planning process leading to the installation of these treatments can be prolonged in many communities and often requires virtually the unanimous agreement of affected residents. Third, many engineers are reluctant to install vertical speed deflections on arterial and collector roads, which are more closely associated with serious pedestrian crashes. These interventions have been documented to be effective in reducing speed and the number of crashes (Hafez-Alavi, 2007; Tester, Rutherford, Wald, & Rutherford, 2004; Zaidel, Hakkert, & Pistiner, 1992).

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3.3.2. Roundabouts Roundabouts are circular intersections that require drivers entering them to yield to traffic in the circle. These devices impart a horizontal deflection to traffic entering the circle, causing a reduction in speed. European studies have reported a 75% reduction in pedestrian crashes following the conversion of conventional intersections to roundabouts (Retting et al., 2003). This intervention has the same drawbacks as those mentioned for vertical deflections, and the blind community is currently involved in litigation to prevent the installation of multilane roundabouts under the Americans with Disabilities Act because they believe that roundabouts are unsafe for use by blind pedestrians unless accessible signals are present.

4. EDUCATIONAL ELEMENTS Educational countermeasures have also proven to be effective in reducing crashes. Delhomme et al. (1999) evaluated road safety mass media outreach using metaanalysis techniques. They found that road safety media campaigns reduced crashes on average by 8.5% during the campaign and 14.8% after the campaign had ended. It was also found that during campaigns focused on lowering speed, speed was reduced 16.9%. One surprising finding was that the use of television in a media campaign generated slightly lower crash reductions compared to those of campaigns in which no television was used. There are very little data evaluating the effects of individual components because most media campaigns have employed multifaceted safety campaigns. Preusser and Blomberg (1984) field-evaluated an educational program designed to reduce child midblock dart-out crashes. The program consisted of an in-class film, television spot advertisements, and posters implemented across the entire community in three cities. The results documented changes in crossing behavior and significant crash reductions in each of the three cities, with midblock dart-out crashes declining by 21% for all children and by 31% for children 4e6 years old. Preusser and Lund (1988) reported similar results for a film shown in a citywide test. Community feedback signs have also been used to reduce speeding behavior (Van Houten & Nau, 1981; Van Houten, Rolider, et al., 1985) and have been included in programs to improve yielding right-of-way to pedestrians. However, feedback signs designed to improve yielding to pedestrian right-of-way have not been evaluated in isolation. Mass mailing of flyers and earned media (printed and electronic coverage of programs) are essentially low- or nocost interventions. However, media outlets are most likely to cover events that will attract the attention of readers and viewers. One way to attract repeated coverage is to

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introduce some degree of novelty or interest to the event to be covered. Mass mailing of flyers with power bills involves only the cost to produce the flyers because mailing costs are saved if the flyers are mailed with city utility bills. Posters are most useful when there is a specific target population that needs to be addressed, such as transit users. In this case, the posters should be most effective where there they will be seen by the target population, such as in bus shelters or on busses. If the goal is to target people driving at night, it may be more useful to place public service announcements on night radio programming.

5. ENFORCEMENT ELEMENTS If drivers do not look for pedestrians, they are unlikely to yield to them. Enforcement is one way to influence drivers to look for and yield to pedestrians. It has been documented that enforcement programs that focus on written warnings rather than citations are more cost-effective in decreasing speeding behavior (Van Houten & Nau, 1983). The results indicated that the effects of a 1-week enforcement program, which relied on citations alone, persisted only while the program was in effect, but the effects of the warning program that stopped large numbers of drivers persisted for up to 1 year. These results were later replicated in Israel (Van Houten, Rolider, et al., 1985) and were associated with significant crash reductions (Scherer, Freidmann, Rolider, & Van Houten, 1985). One reason why warnings may be more effective is that drivers who are traveling only a few miles per hour over the speed limit can be stopped. In 1985, Van Houten, Malenfant, and Rolider evaluated a similar program to increase drivers’ yielding to pedestrians in two Canadian cities. This program consisted of written warnings, the use of decoy pedestrians, and information flyers on the seriousness of pedestrian crashes. The multifaceted program produced a sustained increase in yielding to pedestrians on selected streets in both cities, which persisted for many years. A similar program that included traffic engineering components was evaluated in three Canadian cities (Malenfant & Van Houten, 1989). The engineering components included (1) pavement markings and signs prompting motorists to yield farther back from the crosswalk, (2) signs prompting pedestrians to extend their arm to signal their intention to cross the street, (3) signs prompting pedestrians to thank drivers who yielded, and (4) public posting of the percentage of drivers yielding to pedestrians each week on large community signs along with the record. The introduction of this treatment was associated with major increases in the percentage of drivers yielding to pedestrians and reductions in the number of pedestrian crashes that persisted for at least 1 year. A subsequent study examined the efficacy of enforcement of pedestrian right-of-way laws and earned media on

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FIGURE 25.1 The percentage of motorists yielding to pedestrians along the west and east corridors in Miami Beach, Florida. Weekly data are shown. Follow up dates represent monthly date points. Source: Reprinted with permission from Van Houten and Malenfant (2004).

the percentage of drivers yielding to pedestrians in Miami Beach, Florida (Van Houten & Malenfant, 2004). In this study, a 2-week intensive enforcement program that included the use of warnings and citations for failure to yield right-of-way to pedestrians produced increases in yielding at sites where enforcement was carried out that were sustained over the course of 1 year. Figure 25.1 shows the percentage of motorists yielding right-of-way to pedestrians during each condition of the experiment. The treatment at targeted sites was also associated with an increase in yielding at 10 of 12 generalization sites that did not receive enforcement. These results demonstrated that enforcement with associated publicity alone could increase the percentage of motorists yielding right-of-way to pedestrians. However, the effects produced by enforcement alone were smaller than those produced by a multifaceted program in previous studies. Data also indicated that driverepedestrian conflicts or near-crashes decreased following the implementation of the program. However, the decreasing trend in conflicts during baseline along one of the two treated corridors make this finding difficult to interpret. Although a full component analysis remains to be completed, a number of factors help make pedestrian rightof-way enforcement programs effective. First, the program should include an objective behavioral definition of failing to yield to pedestrians, which employs the use of the signaltiming formula used to time the yellow signal phase at traffic signals. This formula is used to identify the dilemma zone that makes it possible for police to determine whether the driver is far enough away from a crosswalk when a pedestrian enters the crosswalk to allow the driver to react and safely stop. The use of this method is critical if citations

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are to be upheld in court. Second, police should use undercover decoy pedestrians at times when actual pedestrians are not present in sufficient numbers at the crosswalk, which increases the frequency of contact with motorists by reducing downtime when pedestrians are not present in the crosswalk. Third, the frequent use of warnings allows police to contact up to 20 times as many noncompliant motorists. Fourth, the use of community feedback can help to establish community interest and assist in forming new social norms. Fifth, the use of inexpensive engineering components such as advance yield or stop markings and a solid no-pass line from the dilemma zone to the crosswalk may also help to improve motorist behavior and, if installed at the start of enforcement activity, can serve as a discriminative stimulus to motorists that crosswalk laws are being enforced. The primary focus of the most successful programs has targeted motorist rather than pedestrian behavior. There are a number of reasons why the focus has been on driver behavior rather than pedestrian behavior. First, driver compliance with pedestrian right-of-way laws in many jurisdictions is poorer than compliance with other statutes. Second, although it is important to educate pedestrians on safe pedestrian skills, it would be unfair to expect to assign them equal responsibility. Third, if drivers consistently fail to yield to pedestrians in crosswalks, it is unreasonable to expect pedestrians to walk out of their way to use crosswalks when drivers do not yield any better at crosswalks than they do at other locations.

6. SPECIFIC ISSUES REGARDING PEDESTRIAN SAFETY 6.1. Screening Crashes Screening crashes are one of the most serious crash types because motorists and pedestrians do not see each other until the crash is imminent. Because little or no time is available to react, it is nearly impossible to take evasive action under screening conditions. These crashes are much more likely to lead to a pedestrian fatality or incapacitating injury. There are two types of screening crashes. The first type is termed a multiple threat crash (Snyder, 1972). In a multiple threat crash, a motorist on a multilane road yields to a pedestrian very close to the crosswalk. The driver in the next travel lane does not see the pedestrian as he or she steps out from in front of the yielding vehicle and the driver hits the pedestrian, typically at a relatively high speed. The best way to avoid screening crashes is to remove the screen. An example of a multiple threat screening crash is shown in Figure 25.2. Evidence for the seriousness of multiple threat crashes comes from a comparison of marked and unmarked

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FIGURE 25.2 An illustration of the setup for a multiple threat crash. The pedestrian on the right is crossing in front of a vehicle that has yielded close to the crosswalk, and the driver in the next lane is passing the stopped vehicle.

crosswalks. Zegeer, Stewart, and Huang (2001) compared crashes at 1000 marked and 1000 matched unmarked crosswalks in 30 U.S. cities. They observed no significant difference in crashes between marked and unmarked crosswalks with one exception: Marked crosswalks on multilane roads with an uncontrolled approach were associated with significantly more crashes than were unmarked crosswalks if the road had average daily traffic (ADT) of more than 12,000 vehicles. They also found that more pedestrians crossed at marked locations and that this effect was strongest for pedestrians younger than 12 years or older than 64 years of age (more than 70% crossed at marked crosswalks). It has been suggested that marking crosswalks can lead to a false sense of security (Herms, 1972). However, behavioral data collected before and after crosswalks were installed at a number of sites contradict this hypothesis. These data show that marked crosswalks were associated with somewhat higher levels of pedestrian observing behavior (Knoblauch, Nitzburg, & Seifert, 1999). One reason why crashes may increase on multilane roads with a high daily traffic count is the occurrence of multiple threat crashes. Although buses and trucks have always been capable of completely screening pedestrians, the popularity of sport utility vehicles and minivans has resulted in an increased percentage of vehicles on the road that can screen the view of pedestrians crossing the street, and children and persons of short stature can be completely screened by relatively small passenger vehicles. Zegeer et al. (2001) also found that the greatest difference in pedestrian crash types between marked and unmarked crosswalks involved multiple threat crashes. This is consistent with evidence presented previously in that multilane roads with a high ADT are more likely to

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have cars approaching in adjacent lanes (potential passing vehicles) than are roads with a low ADT and, therefore, have greater exposure for multiple threat crashes. The researchers recommended that marked crosswalks should not be installed alone on multilane roads with a high ADT. Instead, marked crosswalks should be enhanced with other traffic engineering measures. One enhancement is the use of signs and markings that encourage motorists to yield in advance of the crosswalk. One way to prevent multiple threat crashes is to install a midblock traffic signal. However, these signals need to meet a specified signal warrant that requires a large volume of pedestrians crossing at the location. A pedestrian traffic signal is also fairly expensive. Data also indicate that pedestrians often violate the signal rather than wait for the Walk indication. A somewhat less costly alternative to a midblock traffic signal is the hybrid beacon (formally called the HAWK signal). The hybrid beacon does not require a high pedestrian flow, and it is slightly less costly than the midblock signal. The hybrid signal rests in the dark phase (no light illuminated). When a pedestrian presses the button, the signal goes immediately to a short period of flashing yellow, then to solid yellow, followed by a solid red phase for 7 s, which is followed by a “wig-wag” flashing red signal (the left and right red beacon alternately flash). Pedestrians receive a Walk indication for the 7 s of solid red indication, and they receive a flashing Don’t Walk clearance phase during the flashing wig-wag red indication. Turner, Fitzpatrick, Brewer, and Park (2006) found 93% yielding compliance with the hybrid beacon. Because beacon operation commences with the pedestrian button press, pedestrian compliance is quite high at hybrid beacon locations. Fitzpatrick and Park (2009) evaluated whether the hybrid beacon reduced pedestrian crashes on multilane roads. The results of a before-and-after study indicated that the hybrid beacons were associated with a 50% reduction in pedestrian crashes at the treated sites. A lower cost strategy that has been documented to reduce conflicts associated with multiple threat crashes is the use of advance stop lines at uncontrolled crosswalks (Van Houten, 1988; Van Houten & Malenfant, 1992). These studies have demonstrated that advance stop lines used in conjunction with signs directing motorists to yield 15.5 m (50 ft) in advance of the crosswalk produce a marked reduction in motor vehicleepedestrian conflicts and an increase in motorists yielding to pedestrians at multilane crosswalks on an uncontrolled approach. These results have been documented at crosswalks with and without yellow flashing beacons. Van Houten and Malenfant also demonstrated that the markings and sign together were more effective than the sign alone. The underlying principle behind advance stop lines is that they increase the safety of pedestrians by reducing the

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screening effect of vehicles yielding to pedestrians. When motorists yield in advance of the crosswalk, they enhance pedestrian safety in three ways. First, the yielding vehicle does not screen the view of motorists in the pedestrian’s next lane of travel. Second, advance stop lines reduce the likelihood that a vehicle traveling behind the yielding vehicle that attempts to pass the yielding vehicle will fail to see the pedestrian crossing in front of the yielding vehicle. Third, they reduce the chance that an inattentive driver who strikes a yielding vehicle from behind will push it into the pedestrian crossing in front of the yielding vehicle (billiard ball crash). The advance markings also provide an added cue to drivers of the crosswalk location. This increase in the conspicuity of the crosswalks might also contribute to the effectiveness of this intervention. Although advance stop lines have proven effective and are being increasingly used in the United States and Canada, they are technically inappropriate in states with a yield rather than stop law. One alternative for crosswalks in states with a law that specifies that a driver is required to yield rather than stop for pedestrians in crosswalks is the use of yield markings. Van Houten, McCusker, and Malenfant (2001) addressed this problem by employing yield markings along with “Yield Here for Pedestrian” signs in advance of the crosswalk. They found marked reductions in conflicts (between 67 and 87%) and a major increase in the distance motorists yielded in advance of the crosswalk following the introduction of the advance yield markings and signs. They did not detect a significant difference between installing advance yield markings and signs 10 or 15 m upstream of the crosswalk, but installing them at either distance produced a significant reduction in conflicts. In addition, whenever advance stop lines or yield markings are used, it is important to prohibit parking between the markings and the crosswalk with appropriate signage. In another study, Van Houten, McCusker, Huybers, Malenfant, and Rice-Smith (2003) evaluated the effects of advance yield markings at 24 sites. The intervention included the following treatments: installing a sign instructing motorists to yield in advance of the crosswalks, supported by yield markings and replacing crosswalk signs at the crosswalk with fluorescent yellow green sheeting. Motorist and pedestrian behaviors measured at the sites (12 urban and 12 rural) were the occurrence of motor vehicleepedestrian conflicts that included evasive action, the distance motorists stopped before the crosswalk when yielding to pedestrians, and the percentage of motorists yielding to pedestrians. Evasive conflicts between pedestrians and motorists showed a marked decline for the two conditions that included advance yield markings and the “Yield Here to Pedestrians” sign. Conflicts declined from 11.1 to 2.7% with the advance yield marking and “Yield Here to Pedestrians” signs and from 12.8 to 2.3% with the

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advance yield marking and “Yield Here to Pedestrians” plus fluorescent yellow green pedestrian sign condition. There was no change in the number of conflicts for the control condition and the fluorescent yellow green sign groups. Thus, changing the pedestrian sign to fluorescent yellow green was not effective in reducing motor vehicleepedestrian conflicts unless it was combined with the advance yield markings, and it added nothing to the effect of these signs using regular sheeting. Data on the percentage of drivers yielding to pedestrians showed a modest increase when the advance yield markings and “Yield Here to Pedestrians” sign were introduced but did not change for the control and fluorescent yellow green sign groups. The mean percentage of drivers yielding to pedestrians at a distance greater than 3 m increased for the conditions that included the advance yield markings but not for the control or fluorescent yellow green sign alone groups. Similar results were obtained for drivers yielding more than 6 m in advance of the crosswalks. These changes all endured during follow-up measurements taken 6 months after the treatments were introduced. Huybers, Van Houten, and Malenfant (2004) examined the use of the “Yield Here to Pedestrians” sign alone, advance yield markings alone, and the sign plus markings together in a study that counterbalanced the sequence in which the two elements of the treatment were introduced. They found that the “Yield Here to Pedestrians” sign produced a modest decrease in conflicts associated with a modest increase in yielding distance. The addition of the pavement markings produced a further improvement in conflicts and yielding distance. However, the introduction of advance yield pavement markings alone produced a marked reduction in conflicts and yielding distance that was not improved further by the addition of the “Yield Here to Pedestrians” sign. This analysis confirmed that the markings were the most important element of the treatment. However, because these markings had been used with the sign at other locations, it is possible that the signs are needed to first educate drivers about the use of the markings when first introduced. Another treatment that has been documented to increase yielding distance along multilane roads is the use of yellow rectangular rapid flashing beacons (RRFBs) attached to the bottom of the pedestrian crosswalk sign installed at the crosswalk (Shurbutt, Van Houten, Turner, & Huitema, 2009; Van Houten, Ellis, & Marmolejo, 2008). This device uses pairs of high-intensity LED beacons that flash in what has been referred to as a stutter flash pattern and luminosity similar to that used on emergency vehicles. The left LED flashes two times in a slow volley each time it is energized (124 ms on and 76 ms off per flash). This is followed by the right LED, which flashes four times in a rapid volley when energized (25 ms on and 25 ms off per flash) and then has a longer flash for 200 ms. Both of these studies documented

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that use of the RRFB produced a marked increase in yielding behavior in multilane crosswalks and that motorists yielded further back when the RRFB was added to the advance yield markings. The results of the Van Houten et al. (2008) study demonstrated that the use of the RRFB produced a marked increase in yielding to both staged pedestrian crossings and local resident crossings. The similar results obtained for staged and resident pedestrian crossings validated the use of staged pedestrian methodology (i.e., having a research assistant act as a pedestrian) to determine levels of yielding and to evaluate the efficacy of treatments designed to increase yielding behavior. It was found that yielding was somewhat higher to local residents than to staged pedestrians because local residents typically were more assertive than the research assistants who made the staged crossings. Research assistants followed a rigorous crossing protocol when they crossed. The use of the RRFB was also associated with major reductions in evasive conflicts between drivers and pedestrians at both treatment sites. Although advance yield markings were in place during baseline and treatment at these sites, the percentage of motorists stopping beyond the advance yield markings showed a major increase at one site and little effect at the other when the RRFB treatment was active. Unfortunately, data were not collected before the advance yield lines were installed, so it was not possible to determine the effect of these signs and markings alone at these sites. Another effect was a major reduction in the percentage of pedestrians trapped in the center of the road while crossing from 44 to 0.5% (Van Houten et al., 2008). It is apparent that increases in yielding to pedestrians should be associated with fewer pedestrians being trapped in the crosswalk. When pedestrians are forced to cross busy roads in gaps because of poor yielding behavior, they often can only get a gap to cross the first half of the roadway. Once they are trapped in the roadway with vehicles passing in front and behind them, they are likely to become uncomfortable and may be less likely to select an adequate gap to finish crossing. The use of the RRFB can reduce the percentage of pedestrians trapped in the crosswalk. One reason why the RRFB device is so effective at increasing yielding and yielding distance is the salience of the stutter flash sequence. Another reason may be the correlation between the flashing beacon and the pedestrian sign, which provides a clear discriminative stimulus that a pedestrian is crossing the street. In the second study, Shurbutt et al. (2009) evaluated the RRFB at 24 sites located in three regions of the United States. The device produced clear and sustained increases in yielding at all sites. In their study, the effects of the RRFB were evaluated at 19 multilane crosswalks in St. Petersburg, Florida, during a 2-year period. The introduction of the RRFB system increased yielding across

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these sites from 2 to 81%, and these results were maintained 2 years after the RRFBs were installed. Similar changes were obtained at a site in Washington, DC, and several sites in a Chicago suburb, although 2-year followup data were not collected at these sites because they were last to receive the treatment. Evaluation of the system at night (night data were only collected at 3 sites) produced even more striking results, with yielding averaging less than 5% during baseline and well over 90% at all 3 sites when the RRFB was in effect. Shurbutt et al. (2009) also compared the use of the RRFB installed under each sign on the right side of the road (two-beacon system) with installing it under each side of the road and on a median island in the middle of the road (four-beacon system). The results of this comparison revealed higher yielding with the four-beacon system (89%) compared to 82% with the two-beacon system and 18% during the baseline condition. The RRFB was also compared with standard overhead yellow incandescent flashing beacons at one site and sidemounted standard incandescent yellow flashing beacons mounted on the pedestrian signs at a second site (Shurbutt et al., 2009). The standard overhead beacon increased yielding from 11 to 16%, whereas the RRFB increased yielding to 88% at the same site. The side-mounted beacon increased yielding from 0 to 15%, and the RRFB increased yielding to 87%. These results demonstrate that the RRFB is a far more effective treatment than the traditional yellow flashing beacon. Yet another interesting finding was a decrease in yielding distance when the traditional beacon was introduced and a marked increase in yielding distance when the RRFB was introduced (Shurbutt et al., 2009). This is important because increasing yielding distance reduces visual screening, which should be associated with declines in multiple threat crashes. The second type of multiple threat crash involves a pedestrian that darts out from behind a parked car or some other type of screen on the side of the roadway. This situation is more difficult to treat with technology because pedestrians can step out between parked cars at almost any location where cars are permitted to park. This type of crash is therefore best addressed by educational interventions. Preusser and Blomberg (1984) showed that the frequency of this type of multiple threat crash could be reduced through the use of a communitywide education program aimed at children. It is critical that pedestrians learn to stop and look before stepping out beyond a vehicle that screens their view of approaching vehicles because looking without stopping may not reduce the chance of a multiple threat crash. Assuming a reaction time of 1 s and a walking speed of 4 ft/s, a pedestrian who looks while stepping out from a screening vehicle will not have time to react before he or she steps into the path of a vehicle.

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6.2. Crashes at Traffic Signals Approximately 37% of pedestrian injury crashes and 20% of fatal pedestrian crashes occur at intersections (DOT, 1996). Studies indicate that pedestrians will violate a Don’t Walk signal to reduce their delay (Braun & Roddin, 1978; Virkler, 1998). Many variables can be hypothesized to influence whether pedestrians will violate a traffic signal, including whether the road carries one-way traffic, the frequency of vehicle gaps, the degree of vehicle congestion, the presence of a median island, the width of the highway, the number of lanes to be crossed, vehicle speed, and weather. Essentially, people will trade risk to avoid effort and delays, with long delays serving as a powerful establishing operation, or motivational variable for rule violation. Van Houten, Ellis, and Kim (2007) found that most pedestrians would wait for the Walk signal to cross at a midblock signalized multilane crosswalk with high traffic volume and few gaps in traffic. They performed a parametric analysis at two midblock crosswalks with traffic signals by varying the length of the minimum green time and found that violations increased as a direct function of pedestrian delay. When minimum green time was set at 30 s, pedestrian signal violations were least frequent. When pedestrians had to wait up to 1 min, nearly 20% of pedestrians violated the signal, and at 2 min nearly 40% of pedestrians crossed against the signal. They also found that the percentage of pedestrians trapped in the middle of the road increased as pedestrian delay increased, with no pedestrians trapped during the 30s minimum green condition and 23% trapped when the wait time was up to 2 min. The results of this experiment are presented in Figure 25.3. These data show that high compliance can be obtained if the wait time is kept short. They also suggest that the trend toward longer signal cycles (the crossing time has been increased for pedestrians in the Manual of Traffic Control Devices, which increases overall cycle length) can be expected to have a negative impact on pedestrian compliance.

FIGURE 25.3 The percentage of pedestrians waiting for the Walk indication plotted against various minimum green durations. Source: Reprinted with permission from Van Houten, Ellis, and Kim (2007).

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Another variable that can impact safety at traffic signals is the use of countdown signals. Several studies have documented conflict or crash reductions associated with countdown signal installation (Eccles et al., 2007; Markowitz et al., 2006). The major value of countdown signals is that they provide clear feedback to pedestrians regarding whether they have enough time to cross and allow them to adjust crossing speed so they are not trapped in the intersection when the signal changes. Countdown signals also increase the number of people who can cross per cycle, which may reduce the number available to cross against the signal. The major crash type at traffic signals is pedestrians being struck by turning vehicles. One approach to reducing these crashes is to prompt pedestrians to look for turning vehicles. In cities, these intersection crashes can constitute up to 25% of pedestrians crashes (Preusser, Wells, Williams, & Weinstein, 2002). Retting, Van Houten, Malenfant, Van Houten, and Farmer (1996) compared signs prompting motorists to look for turning vehicles that showed the direction of each possible threat and “Watch Turning Vehicles” markings painted in the crosswalk just off the curb face. These elements were compared alone and together at several sites, with the order of introduction varied across sites. Results indicated that signs alone and markings alone increased observing behavior and reduced conflicts at all sites, and the signs and markings together produced the largest reduction in conflicts. Follow-up data indicated that these changes persisted when maintenance data were collected 11 months after installation. A follow-up study examined audible warnings that warned pedestrians to look for turning vehicles as part of an accessible pedestrian signal (Van Houten, Malenfant, Van Houten, & Retting, 1998). The results of this study indicated that the audible signals increased pedestrian observing behavior and almost eliminated pedestrianevehicle conflicts. It appeared that the audible message seemed to prime pedestrians to respond to turning vehicles. Data also indicated that a child’s voice might have been more effective in prompting looking than an adult voice. It is also possible to use the pedestrian signal head to prompt pedestrians to look for turning vehicles at traffic signal locations. Van Houten, Retting, Van Houten, Farmer, and Malenfant, (1999)used an animated eyes indication as part of the Walk signal. The eyes appeared at the start of the Walk indication and scanned left and right for the first 2.5 s of the Walk signal. The addition of the animated eyes display was associated with a major reduction in the percentage of pedestrians who did not look for turning vehicles at the start of the Walk from 29 to 3%, and there was a reduction in the percentage of conflicts from 2.7 to 0.5%. Repeating the animated eyes display every 9.5 s reduced the percentage of pedestrians who arrived at the intersection after the start of the Walk signal who did not

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look for turning vehicles from 17 to 2.5% and reduced conflicts from 2.5 to 0.3%. Van Houten, Malenfant, and Steiner (2001) further examined the efficacy of the animated eyes display across different intersection geometries and timing parameters. They found that the animated eyes display was effective at increasing looking and decreasing conflicts at intersections with one-way traffic on both streets, one-way traffic on one street, and two-way traffic on the second street as well as intersections where both streets carried two-way traffic. The effectiveness of steady versus intermittently applied scanning eyes was examined in the second and third experiments. The results indicated that having the scanning eyes display illuminated for the entire Walk interval was no more effective than having it alternately on for 3.5 s and off for 3.5 s. However, having it on for 3.5 s and off for 3.5 s was more effective than having it on for 3.5 s and off for 7 s. Studies have also examined the efficacy of prompting operators of turning vehicles to yield to pedestrians (Abdulsattar, Tarawneh, McCoy, & Kachman, 1996; Karkee, Pulugurtha, & Nambisan, 2006). These studies have consistently shown a reduction in conflicts between turning vehicles and pedestrians following the installation of these signs. An engineering approach to improving traffic safety at traffic signals is to separate pedestrians in time from turning vehicles. One way to use temporal separation is to employ a scramble crossing phase (Bechtel, MacLeod, & Ragland, 2004; Garder, 1989). The scramble crosswalk phase stops all vehicles while releasing all pedestrians. Pedestrians are also permitted to cross the intersection diagonally during this exclusive crossing phase. Bechtel et al. conducted an analysis of pedestrianevehicle conflicts and pedestrian violations before and after the traffic signals were modified to a scramble crosswalk at a very busy intersection in Oakland, California. The results indicated a reduction in conflicts but an increase in pedestrian violations. The increase in violations involved pedestrians crossing on intersection legs parallel to the moving traffic. Kattan, Acharjee, and Tay (2010) reported similar results from two busy intersections in Calgary, Alberta, Canada, and Garder reported similar data from Sweden. Future research should examine the effects of scramble crosswalks on pedestrian crashes. Temporal separation can also be achieved by releasing pedestrians before releasing drivers through the use of a leading pedestrian signal phase (Van Houten, Retting, Farmer, Van Houten, & Malenfant, 2000). A leading pedestrian phase extends the all red signal phase by a short interval (typically 3 or 4 s). During this time, pedestrians receive an exclusive walk phase. When right turns are prohibited during the red signal phase, pedestrians are protected from all turning vehicles for the first 3 or 4 s. If right turn on red is permitted, pedestrians are protected

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from left-turning vehicles, and right-turning vehicles carry less energy because they are required to come to a complete stop before turning right when the signal is red. Van Houten et al. found that the introduction of a 3 or 4s leading pedestrian phase allows pedestrians to increase their visibility by entering the crosswalk before vehicles are released. Data from Van Houten et al. indicated that pedestrians on average were able to cross three-fourths of the first travel lane before vehicles were released. The leading pedestrian phase was also associated with a marked reduction in pedestrianevehicle conflicts and a major reduction in the percentage of pedestrians giving up the right-of-way to motorists by stopping and waiting or waving the vehicle through the intersection. Another study further supported adding a leading pedestrian phase to intersections to reduce crashes. Fayish and Gross (2010) found that the implementation of a 3s lead pedestrian phase was associated with a 59% reduction in pedestrian crashes at the treated intersections. Another way to improve pedestrian safety at traffic signals is to increase the physical separation between pedestrians and motorists by installing stop bars further back than the 4ft (1.22 m) minimum distance. Moving back the stop bar at traffic signals can also decrease crashes between trucks and pedestrians. Pedestrian crashes with trucks at signalized intersections are more likely to lead to fatalities than are crashes with any other type of vehicle (Retting, 1993). Most pedestrians struck by trucks at traffic signals are older than age 60 years and are struck as they complete the crossings after the vehicle signal has changed to green. Retting noted that the major contributing factor to these crashes is poor visibility resulting from the design of truck cabs. One simple countermeasure to reduce this type of crash is moving the stop bar at traffic signals back from the minimum distance of 4 ft to 20 ft. This intervention produces a clear zone, which affords drivers a better view of pedestrians crossing (Retting & Van Houten, 2000). Retting and Van Houten also found that moving the stop line back reduced the percentage of motorists who blocked crosswalks at their four treated intersections from 25 to 7% and increased the percentage of drivers that stopped further in advance of the crosswalk. Data also indicated that moving the stop bar from 4 ft (1.22 m) behind the crosswalk to 20 ft (8.84 m) behind the crosswalk resulted in a significant increase of three-fourths of a second in the time between vehicles being released by the onset of the green signal phase and the first vehicle entering the intersection. This increase could benefit all road users by reducing the risk of crashes caused by vehicles on the cross street running the red light. Pedestrian safety can also be improved by encouraging pedestrians to wait for the Walk signal before crossing. Most pedestrian push buttons fail to provide feedback to the pedestrians that the button is working. Push buttons are

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currently being sold that provide audible feedback with a clicking sound or a tone along with a steady LED light when pressed. Van Houten, Ellis, Sanda, and Kim (2006) evaluated call buttons that provide feedback to the pedestrian using a multiple baseline design across two intersections. They found that adding feedback increased the percentage of pedestrians pressing the buttons over time and reduced the percentage of pedestrians crossing against the pedestrian signals. It would also be interesting to determine whether counting down the time until the Walk indication on a display on the button would increase compliance.

6.3. Crashes at Uncontrolled Crosswalks A number of interventions have been developed to improve yielding at uncontrolled crosswalks. Two very effective countermeasures mentioned in the discussion of screening crashes are the RRFB and the hybrid signal. However, it is not feasible to install these more costly treatments at all crosswalks, particularly crosswalks that only traverse one lane of traffic in each direction. One treatment that has proven effective at this type of crosswalk is the use of instreet “State Law: Yield to Pedestrian” signs. These narrow signs are placed at the center line of the road at the crosswalk. Huang, Zegeer, and Nassi (2000) found that these signs increased the percentage of drivers yielding to pedestrians. Ellis, Van Houten, and Kim (2007) determined the optimum placement of these signs. They compared placing them at the crosswalk, 20 ft in advance of the crosswalk, and 40 ft in advance of the crosswalk using a counterbalanced multi-element design at three locations in Miami Beach, Florida. The data presented in Figure 25.4 show that installing the sign at the crosswalk was the most effective placement. It is also interesting to note that placing a sign at all three locations was no more effective than placing it at the crosswalk alone. Although the signs placed at the

FIGURE 25.4 The percentage of drivers yielding to pedestrians plotted against the distance the sign is placed in advance of the crosswalk. Source: Reprinted with permission from Ellis, Van Houten, and Kim (2007).

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crosswalk increased yielding from 29 to 71% and reduced the percentage of pedestrians trapped in the center of the road while crossing from 5 to 0%, the signs were frequently struck by turning trucks and had a relatively short life. This treatment is most practical at midblock crosswalks or crosswalks with few large truck turning movements. Another approach to improving the percentage of drivers yielding to pedestrians is to teach pedestrians to signal drivers their intention to cross. Crowley-Koch, Van Houten, and Lim (2011) compared a raised hand with an extended arm versus using an extended arm with just standing in the crosswalk on driver yielding behavior at 10 crosswalks in four cities. Both the raised arm and the extended arm methods were superior to not signaling the driver, and the raised arm produced the best yielding behavior at each site.

6.4. Improving the Safety of Pedestrians with Visual Impairments Williams, Van Houten, Ferroro, and Blasch (2005) compared two types of accessible signals on the street crossing performance of 24 totally blind pedestrians. One pedestrian signal used an audible sound paired with a vibrating button to signal the start of the walk interval. The second device used a handheld receiver that received a message encoded in the imperceptibly fast flash rate of the Walk pedestrian indication and a second message encoded in the flash rate of the Don’t Walk indication. The handheld receiver repeated the verbal message “Wait” during the Don’t Walk indication and the verbal message “Proceed with caution” during the Walk indication. The control condition was crossing using standard wayfaring (using traffic sounds and a cane to cross) methods without either accessible signal. Start time was significantly shorter with the handheld device than with the audible push button or control condition. There was no significant difference between the two accessible signals in crossing times once the crossing was initiated, and both accessible signals were superior to the control condition. The number of missed cycles was significantly lower when the participant used either accessible pedestrian signal (APS) device than when the participant crossed without an APS device, and there was no difference in the number of missed cycles between the two APS devices. To use an APS, the pedestrian must be able to locate the signal. This task would be relatively easier if all push buttons were placed at the same relative location at each intersection. Another approach to aid blind pedestrians in finding the push button is the use of a locator tone. Scott, Myers, Barlow, and Bentzen (2005) evaluated the use of locator tones. Their study compared push button location and the type of audible Walk indication for visually

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Pedestrians

impaired pedestrians to determine which of two streets had the Walk indication. Pedestrians were also more accurate when the locator tone was a fast tick at a rate of 10 clicks per second. Verbal messages that informed pedestrians which crosswalk had the Walk indication were also superior to using two different sounds for each crossing leg. The authors also reported that pedestrians were able to find the push button most rapidly when it was mounted so that it was aligned with the outer crosswalk line and each pole was located close to the curb.

7. SUMMARY Because pedestrians are vulnerable road users, it is critical that we develop methods to improve the safety of walking. One way to improve the safety of pedestrians is to teach drivers and pedestrians to follow the rules of the road. It is also critical that we teach motorists to look for pedestrians, and that pedestrians learn to consistently look for drivers before entering the travel way. New technologies such as the hybrid signal, RRFB, and advance yield markings can help reduce one of the most serious crash typesdmultiple threat crashes. The use of countdown timers and a lead pedestrian signal phase can help protect pedestrians crossing at traffic signals. However, shorter wait times for pedestrians are needed to improve pedestrian compliance at traffic signals. Education and enforcement can also be employed to improve compliance with pedestrian right-ofway laws. Reducing speeds in urban communities can reduce the incidence and severity of pedestrian crashes. It might be worth considering that in areas where most drivers speed, it is likely the design of the roadway that is most at fault. Traffic calming measures are probably the best countermeasure at these locations. Traffic calming can improve pedestrian safety by reducing speeds. When vehicles travel slowly, the driver and pedestrian have more time to react when in conflict, and the severity of pedestrian crashes is greatly reduced.

REFERENCES Abdulsattar, N. A., Tarawneh, M. S., McCoy, P. T., & Kachman, S. D. (1996). Effect on vehicleepedestrian conflicts of “Turning Traffic Must Yield to Pedestrians” sign. Transportation Research Record, 1553, 38e45. Bechtel, A. K., MacLeod, K. E., & Ragland, D. R. (2004). Pedestrian scramble signal in Chinatown neighborhood of Oakland, California: An evaluation. Transportation Research Record, 1878, 19e26. Blomberg, R. D., Preusser, D. F., Hale, A., & Ulmer, R. G. (1979). A comparison of alcohol involvement in pedestrians and pedestrian casualties: Final report to the National Highway Traffic Safety Administration. (Report No. ED 79-11). Darien, CT: Dunlap and Associates. Braun, R., & Roddin, M. (1978). Quantifying the benefits of separating pedestrians and vehicles. (NCHRP Report No. 189). Washington, DC: Transportation Research Board.

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Clayton, A. B., Colgan, M. A., & Tunbridge, R. J. (2000). The role of the drinking pedestrian in traffic accidents. Birmingham, UK: British Institute of Traffic Education Research. Crowley-Koch, B., Van Houten, R., & Lim, E. (2011). Pedestrians prompt motorists to yield at crosswalks. Journal of Applied Behavior Analysis 44. 121e126. Delhomme, P., Vaa, T., Meyer, T., Harland, G., Goldenbeld, C., Ja¨rmark, S., Christie, N., & Rehnova, V. (1999). Evaluated road safety media campaigns: An overview of 265 evaluated campaigns and some metaanalysis on accidents. (EC, Deliverable 4. Gadget project. Contract No. RO-97-SC.2235). Arcueil, France: INRETS. Eccles, K. A., Tao, R., & Mangum, B. C. (2007). Evaluation of pedestrian signals in Montgomery County, Maryland. Transportation Research Record, 1878, 36e41. Ellis, R., Van Houten, R., & Kim, J. L. (2007). In-roadway “Yield to Pedestrians” signs: Placement distance and motorist yielding. Transportation Research Record, 2002, 84e89. Fayish, A. C., & Gross, F. (2010). Safety effectiveness of leading pedestrian intervals evaluated by a beforeeafter study with comparison groups. Transportation Research Record, 2198, 15e22. Fitzpatrick, K., & Park, E. S. (2009). Safety effectiveness of HAWK pedestrian treatment. Transportation Research Record, 2140, 214e223. Garder, P. (1989). Pedestrian safety at traffic signals: A study carried out with the help of a traffic conflicts technique. Accident Analysis and Prevention, 21, 435e444. Hafez-Alavi, S. (2007). Analyzing raised crosswalks dimensions influence on speed reduction in urban streets. Paper presented at the third Urban Street Symposium, Seattle, Washington. Herms, B. (1972). Pedestrian crosswalk study: Accidents in painted and unpainted crosswalks. (No. 406). Washington, DC: Transportation Research Board. Huang, H., Zegeer, C., & Nassi, R. (2000). Effects of innovative pedestrian signs at unsignalized locations: Three treatments. Transportation Research Record, 1705, 43e52. Huybers, A., Van Houten, R., & Malenfant, J. E. L. (2004). Reducing conflicts between motor vehicles and pedestrians: The separate and combined effects. Journal of Applied Behavior Analysis, 37, 445e456. Karkee, G., Pulugurtha, S. S., & Nambisan, S. (2006). Evaluating the effectiveness of “Turning Traffic Must Yield to Pedestrians (R 10e15)” sign. American Society of Civil Engineers Proceedings 400e405. Kattan, L., Acharjee, S., & Tay, R. (2010). Pedestrian scramble operations. Transportation Research Record, 2140, 79e84. Knoblauch, R. L., Nitzburg, M., & Seifert, R. L. (1999). Pedestrian crosswalk case studies. Philadelphia: Center for Applied Research, for the Federal Highway Administration. Leibowitz, H. W., Owens, D. A., & Tyrrell, R. A. (1998). The assured clear distance ahead rule: Implications for traffic safety and the law. Accident Analysis and Prevention, 30, 93e99. Malenfant, L., & Van Houten, R. (1989). Increasing the percentage of drivers yielding to pedestrians in three Canadian cities with a multifaceted safety program. Health Education Research, 5, 274e279. Markowitz, F., Sciortino, S., Fleck, J. L., & Yee, B. M. (2006). Pedestrian countdown signals: Experience with an extensive pilot installation. ITE Journal, 76, 43e48.

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Ostrom, M., & Eriksson, A. (2001). Pedestrian fatalities and alcohol. Accident Analysis and Prevention, 33, 173e180. Preusser, D. F., & Blomberg, R. D. (1984). Reducing child pedestrian accidents through public education. Journal of Safety Research, 15, 47e56. Preusser, D. F., & Lund, A. K. (1988). And keep on looking: A film to reduce pedestrian crashes among 9 to 12 year olds. Journal of Safety Research, 19, 177e185. Preusser, D. F., Wells, J. K., Williams, A. F., & Weinstein, H. B. (2002). Pedestrian crashes in Washington, DC and Baltimore. Accident Analysis and Prevention, 34, 703e710. Retting, R. A. (1993). A study of fatal crashes involving pedestrians and trucks in four cities. Journal of Safety Research, 24, 195e203. Retting, R. A., Ferguson, S. A., & McCartt, A. T. (2003). A review of evidence-based traffic engineering measures designed to reduce pedestrianemotor vehicle crashes. American Journal of Public Health, 93, 1456e1463. Retting, R. A., & Van Houten, R. (2000). Safety benefits of advance stop lines at signalized intersections: Results of a field evaluation. ITE Journal, 70, 47e54. Retting, R. A., Van Houten, R., Malenfant, L., Van Houten, J., & Farmer, C. M. (1996). Special signs and pavement markings improve pedestrian safety. ITE Journal, 66, 28e35. Rumar, K. (1990). The basic driver error: Late detection. Ergonomics, 32, 1281e1290. Scherer, M., Freidmann, R., Rolider, A., & Van Houten, R. (1985). The effects of saturation enforcement campaign on speeding in Haifa, Israel. Journal of Police Science and Administration, 12, 425e430. Scott, A. C., Myers, L., Barlow, J. M., & Bentzen, B. L. (2005). Accessible pedestrian signals: The effect of push-button location and audible “Walk” indications on pedestrian behavior. Transportation Research Record, 1939, 69e76. Shurbutt, J., Van Houten, R., Turner, S., & Huitema, B. (2009). An analysis of the effects of stutter flash LED beacons to increase yielding to pedestrians using multilane crosswalks. Transportation Research Record, 2073, 69e78. Snyder, M. B. (1972). Traffic engineering for pedestrian safety: Some new data and solutions. Highway Research Record, 406, 21e27. Tester, J. M., Rutherford, G. W., Wald, Z., & Rutherford, M. (2004). A matched caseecontrol study evaluating the effectiveness of speed humps in reducing child pedestrian injuries. American Journal of Public Health, 94, 646e650. Turner, S., Fitzpatrick, K., Brewer, M., & Park, E. S. (2006). Motorist yielding to pedestrians at unsignalized intersections: Findings from a national study on improving pedestrian safety. Transportation Research Record, 1982, 1e12. U.S. Department of Transportation. (1996). Traffic safety facts 1995. (DOT-HS-808-471). Washington, DC: National Highway Traffic Safety Administration. U.S. Department of Transportation. (2008). Traffic safety facts, 2005. Washington, DC: National Highway Transportation Safety Administration. (DOT HS 811 163). Van Houten, R. (1988). The effects of advance stop lines and sign prompts on pedestrian safety in crosswalk on a multilane highway. Journal of Applied Behavior Analysis, 21, 245e251. Van Houten, R. (1998). How to use prompts. Austin, TX: Pro Ed. Van Houten, R., Ellis, R., & Kim, J. L. (2007). Effects of various minimum green times on percentage of pedestrians waiting for

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midblock “Walk” signal. Transportation Research Record, 2002, 78e83. Van Houten, R., Ellis, R., & Marmolejo, E. (2008). The use of stutter flash LED beacons to increase yielding to pedestrians at crosswalks. Transportation Research Record, 2073, 69e78. Van Houten, R., Ellis, R., Sanda, J., & Kim, J. L. (2006). Pedestrian push button confirmation increases call button usage and compliance. Transportation Research Record, 1982, 99e103. Van Houten, R., & Malenfant, J. E. L. (2004). Effects of a driver enforcement program on yielding to pedestrians. Journal of Applied Behavior Analysis, 37, 351e363. Van Houten, R., Malenfant, J. E. L., & Steiner, R. (2001). Scanning “eyes” symbol as part of the Walk signal: Examination across several intersection geometries and timing parameters. Transportation Research Record, 1773, 75e81. Van Houten, R., & Malenfant, L. (1992). The influence of signs prompting motorists to yield 50 feet (15.5 m) before marked crosswalks on motor vehicleepedestrian conflicts at crosswalks with pedestrian activated flashing lights. Accident Analysis and Prevention, 24, 217e225. Van Houten, R., Malenfant, L., & Rolider, A. (1985). Increasing driver yielding and pedestrian signalling with prompting, feedback and enforcement. Journal of Applied Behavior Analysis, 18, 103e115. Van Houten, R., Malenfant, L., Van Houten, J., & Retting, R. A. (1998). Auditory pedestrian signals increase pedestrian observing behavior and reduce conflicts at a signalized intersection. Transportation Research Record, 1578, 20e22. Van Houten, R., McCusker, D., Huybers, S., Malenfant, J. E. L., & RiceSmith, D. (2003). An examination of the use of advance yield markings and fluorescent yellow green RA 4 signs at crosswalks with uncontrolled approaches. Transportation Research Record, 1818, 119e124. Van Houten, R., McCusker, D., & Malenfant, J. E. L. (2001). Reducing motor vehicleepedestrian conflicts at multilane crosswalks with uncontrolled approach. Transportation Research Record, 1773, 69e74. Van Houten, R., & Nau, P. A. (1981). A comparison of public posting and increased police surveillance on highway speeding. Journal of Applied Behavior Analysis, 14, 261e271. Van Houten, R., & Nau, P. A. (1983). Feedback intervention, and driving speed: A parametric and comparative analysis. Journal of Applied Behavior Analysis, 16, 253e281. Van Houten, R., Retting, R. A., Farmer, C. M., Van Houten, R., & Malenfant, J. E. L. (2000). Field evaluation of a leading pedestrian interval signal phase at three suburban intersections. Transportation Research Record, 1734, 86e91. Van Houten, R., Retting, R. A., Van Houten, J., Farmer, C. M., & Malenfant, J. E. L. (1999). The use of animation in LED pedestrian signals to improve pedestrian safety. ITE Journal, 69, 30e38. Van Houten, R., Rolider, A., Nau, P. A., Freidmann, R., Becker, M., Chalodovsky, I., & Scherer, M. (1985). Large-scale reductions in speeding and accidents in Canada and Israel: A behavioral ecological perspective. Journal of Applied Behavior Analysis, 18, 87e93. Virkler, M. R. (1998). Pedestrian compliance effects on signal delay. Transportation Research Record, 1636, 88e91. Williams, M. D., Van Houten, R., Ferroro, J., & Blasch, B. B. (2005). Field comparison of two types of accessible pedestrian signals. Transportation Research Record, 1939, 91e98. Wilson, R.J., & Fang, M. (2000). Alcohol and drug impaired pedestrians killed or injured in motor vehicle collisions. Paper presented at

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the 15th International Conference on Alcohol, Drugs and Traffic Safety. Wood, J. M., Tyrrell, R. A., & Carberry, T. P. (2005). Limitations in drivers’ ability to recognize pedestrians at night. Human Factors, 47, 644e653. Zaidel, D., Hakkert, A. S., & Pistiner, A. H. (1992). A matched caseecontrol study evaluating the effectiveness of speed humps in reducing child pedestrian injuries. Accident Analysis and Prevention 24e56.

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Zegeer, C. V., Stewart, J. R., & Huang, H. (2001). Safety effects of marked versus unmarked crosswalks at uncontrolled locations: Analysis of pedestrian crashes in 30 cities. Transportation Research Record, 1773, 56e68. Zhu, M., Cummings, P., Chu, H., & Xiang, H. (2008). Urban and rural variation in walking patterns and pedestrian crashes. Injury Prevention, 14, 377e380.

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Chapter 26

Bicyclists Ian Walker University of Bath, Bath, UK

1. INTRODUCTION An engineer colleague sent me a query about the distracting effects of mobile telephones on drivers. I was able to provide him with a detailed answer to his question, full of references to published research. My reply not only addressed his question but also went on to explore various subtleties surrounding the issue: From my reading of the literature, I could begin to break down the task to explain to him which components of a remote conversation most affect driving. I was able to give him some idea of how the effects come about and what would be the likely impact of a telephone call on a driver’s performance and safety. In contrast, the next day a bicycling campaigner asked me several different questions about bicycling safety issues, and to each I was forced to reply, “I don’t know: There’s no evidence either way.” I mention this to help explain the scope of this chapter. My original aim here was to summarize the state of the art in bicycling research. However, after much thought, it became clear that there really is not a clear message about bicycling to describe just now. With most aspects of bicycling research, the best we currently have are hints and incomplete stories. That the transport research community has long focused its attention more on driving than on bicycling is largely understandable, given the much higher amount of driving seen in most countries and the larger role driving plays in most people’s lives. However, with Western populations already heavily urbanized and set to become more so, it is difficult for me to imagine a (nondystopian) future in which human-powered vehicles do not have a larger role than they do now. I worry that when more people start to realize the value of human-powered transport for addressing our transport and health problems, and so seek to encourage more widespread bicycling, they will, like the campaigner whose queries I was unable to answer, find they lack the basic evidence they need to justify and effect this change. In suggesting that human-powered vehicles will become more important, I am predicting the future and this is, of course, notoriously difficult. It is easy to mock those people in the 1950s and 1960s who imagined that in our Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10026-8 Copyright Ó 2011 Elsevier Inc. All rights reserved.

present age we would be commuting with jetpacks and spending weekends at our second homes on the Moon. However, I argue that the spirit underlying those predictions, particularly the belief that the onward march of technology will inexorably lead to a better future, still dominates the transport world to a literally unhealthy degree. Asked to solve the manifold predicaments arising from private cars and the way we use them, far too many people are still responding with the following solution: better cars. They look to a future in which cars run on nonpolluting fuels and have awareness of their surroundings, such that they anticipate and manage hazards. Too few people are considering a future in which we respond to today’s problems with the following solution: less driving. But it is a solution that must be considered. Even if cars were powered by wizardly forces, such that they ran forever without fuel and were quite unable to be involved in collisions, they would still suffer from fundamental design flaws. Our putative nonpolluting, noncrashing car would still congest our roads, would still clutter our urban landscapes when not in use, would still encourage sprawling city planning, and would still fail to provide its user with physical exercise. This last point is perhaps the most important. The human body has not changed in any notable way for millennia, and it is not going to change any time soon. It is well-known that it needs more exercise than our current lifestyles provide, and active transport is in many cases the best place for people to get this (Morris, 1994; Ogilvie, Egan, Hamilton, & Petticrew, 2004). We make enough short journeys that even a partial shift to humanpowered transport would provide many people with all the exercise they need. Even in cases in which the short journeys alone failed to provide sufficient exercise, they would often make the difference between people getting enough each week and getting too little (Tudor-Locke, Ainsworth, & Popkin, 2001; Tudor-Locke, Neff, Ainsworth, Addy, & Popkin, 2002). Given that a coherent narrative, summarizing what we know about bicycling in a readily digested format, was not really feasible, I chose instead to discuss some of the more 367

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intriguing issues to have emerged in recent years. Specifically, I describe three areas of research that I believe are incomplete but potentially important if we want to support more bicycling. Because the overall focus of this book is on traffic psychology, I have chosen subjects that touch upon cognitive, social, and behavioral processes affecting bicyclists’ safety. First, in order to set the context of these topics and help them be properly understood, a short history lesson might be useful.

2. BICYCLES, CARS, AND PUBLIC ACCEPTANCE When bicycles first made their appearance in the nineteenth century, the public reacted with open disgust. Riders endured taunts and, in towns and cities, were sometimes sent flying over their handlebars after passersby jammed sticks into their wheels. A trip into the countryside was likely to be no better because riders could find themselves on the wrong end of a shower of stones, courtesy of the same rosy-cheeked farmworkers we now romanticize when looking back from our post-industrial age (McGurn, 1987). These attacksdwhich in Britain were one of the driving forces behind the formation of bicycle clubs, with riders seeking safety in numbers as much as companionshipdwere of course illegal. However, the fact that these attacks were against the law should not be taken to imply that the world’s legislatures were on the side of the bicyclist. In the nineteenth century, cities from New York to Berlin and Moscow swiftly introduced laws to restrict bicycling, often banning it altogether. Moscow’s 1881 ban in particular comes across as a trifle hysterical with hindsight, given that there were perhaps only five bicyclists in the city at the time (McGurn, 1987). Then, as is the way of such things, the fuss subsided and after a while bicycling came first to be tolerated, then accepted, and then popular. This state persisted for some time until, a few decades later, motor vehicles appeared and the entire process repeated itself: The car was initially loathed until, over time, economic and social changes meant that it established itself in the niche once held by the bicycle. The initial reaction against the bicycle was primarily motivated by conservatism coupled with class prejudice: Velocipeding was a well-to-do activity, unaffordable to the typical working family, and it was distrusted on those grounds as much as any other (McGurn, 1987). Similarly, the subsequent resistance to the car arose because by that time, mass production and increased popularity had worked to reduce the price of bicycles dramatically. The public had come to love the personal mobility that bicycles provided and did not wish to share the roads with these new

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motorized contraptions, particularly because high prices again made car ownership unattainable to most. Once more, the public aversion to the new transport mode was shared by officials who by that time were mostly ardent converts to bicycling. The initial instinct of nonplussed authorities when faced with motor vehicles was to protect all the other road users from what they saw as a new and unprecedented threat. Of the various road safety procedures introduced in the very early days of motoring, certainly the most infamous was the system of red flag bearers, obliged to precede traction engines as they motored around Britain’s lanes, sometimes at speeds as fast as a brisk stroll. This law was rescinded soon after petrol-driven cars were introduced (ironically, often by bicycle makers wanting to sell vehicles with more cachet than the now commonplace bicycle; Wilson, 1973). However, as the red flag law was repealed, so it was replaced with a raft of new measures, many of which, such as speed limits and compulsory driver licensing, remain with us today (Hindle, 2001). It was in this spirit of seeking to protect the public that, as motor vehicle ownership expanded, well-intentioned governments abandoned the millennia-old system in which roads were shared equally by everybody in favor of an approach whereby roads were divided, typically with a large central portion for people using vehicles and wholly separate areas for pedestrians (Hamilton-Baillie, 2008a, 2008b). This ultimately led to the situation found in most developed countries today, in which different travel modes automatically confer different sets of rights and limitations on their users. Pedestrians know that they are not permitted on roads as a matter of course; they are expected instead to take themselves to designated places if they wish to apply for permission to cross. Motorists know that they cannot drive along the zone reserved for pedestriansdhence the dramatic effect of this behavior when it is shown in action films. This sort of system, in which each mode’s permissions are clearly demarcated, provides a great deal of certainty: A pedestrian can walk along a road without constantly needing to search for potentially hazardous motor vehicles; similarly, a driver can travel in the knowledge that a pedestrian should not appear in front of him or her without warning, and that if a pedestrian does, it will likely be the pedestrian who is held to account for any collision (Carsten, Sherborne, & Rothengatter, 1998).

3. BICYCLING, INFRASTRUCTURE, AND DRIVER ATTENTION The demarcated road system just described is at the heart of one of the major problems facing bicycling today. Specifically, compared to more popular modes of transport,

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bicycling is neither one thing nor the other from the perspective of infrastructure provision. Faster than walking, the bicycle does not always sit easily among pedestrian space, often stirring fear and resentment from people on foot; but slower than driving, bicycling does not mix easily with motor traffic either. The most obvious answer to this conundrum is to provide a literal “third way”ddedicated bicycle infrastructure of the sort famously popular in countries such as The Netherlands. Segregation should not be unquestioningly embraced, however, even where there is the public and political will to pay for it and to take space from elsewhere to build it. One issue is that often bicyclists have more accidents on off-road bicycle paths than when riding on the road (Aultman-Hall & Adams, 1998; Aultman-Hall & Hall, 1998; Aultman-Hall & Kaltenecker, 1999; Garder, Leden, & Thedeen, 1994; Moritz, 1998). Picking apart such accident data is always controversial, and in particular it is possible that a bias is introduced into these data through off-road paths attracting more children and inexperienced riders, who then injure themselves in relatively minor falls. Certainly, such an interpretation would be consistent with the finding that bicyclists’ most serious injuries tend to involve motor vehicles (Atkinson & Hurst, 1983; McCarthy & Gilbert, 1996; Olkkonen, La¨hderanta, Tolonen, Sla¨tis, & Honkanen, 1990; Rodgers, 1995; Stone & Broughton, 2002). The involvement of motor vehicles in the more serious bicyclist injuries tends very often to follow a predictable pattern in which the bicycle is struck at an intersection by a turning car that fails to yield priority appropriately (Atkinson & Hurst, 1983; Stone & Broughton, 2002). Because the mechanism underpinning these collisions appears in most cases to be a perceptual failure on the part of the driver (Ra¨sa¨nen & Summala, 1998; Summala, Pasanen, Ra¨sa¨nen, & Sieva¨nen, 1996), similar attentional issues in drivers will even affect segregated infrastructure to some extent, given that each segregated cycleway must have at least entrance and egress intersections, and many are designed with far more conflict points. To illustrate the extent to which intersections disproportionately threaten bicyclists, I once asked the United Kingdom’s Department for Transport (personal communication) for some data. These showed that whereas 62% of all recorded road accidents happened at intersections, when the subset of collisions involving a bicycle and a motor vehicle are specifically considered, the figure rises to 75%. A lesson for infrastructure providers, then, might be that the fewer intersections one has, the better it is for bicyclists. The perceptual and attentional lapses causing drivers to overlook bicyclists near intersections are not well understood. However, there is good reason to believe that the process involves a substantial top-down processing

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component in the drivers. “Looked but failed to see” errors, in which a driver fails consciously to perceive another road user despite being in a position to do so, are more common in experienced drivers than in novices (Herslund & Jørgensen, 2003). These attentional lapses particularly endanger bicyclists, and because they become more frequent when people are familiar with the driving task, the problem clearly does not simply lie in the bicyclists being difficult to see; if that were the case, it would be the novices who overlooked them more often. As such, it is far from obvious that the best solution to the problem of drivers hitting bicyclists is to make the bicyclists more conspicuous. A plausible mechanism behind these perceptual failures, which so far appears to be untested, is that drivers often fail to notice bicyclists at intersections because of misplaced expectations about what they will encounter. Specifically, because bicycles are relatively rare in many settings, drivers, based on their experiences, might not expect to meet bicycles at any given junction. This expectation might cause drivers not to search for bicycles or not to attend to them when they are present. This argumentdthat bicyclists’ relative scarcity biases drivers’ expectations and so impacts on perceptiondhas been suggested (e.g., Walker, 2009) as an explanation of the “safety in numbers effect,” in which the risk of being involved in a bicycle accident declines as the number of bicycles in a locale increases (Jacobsen, 2003; for a different view of safety in numbers, see Bhatia & Wier, 2010). A proper test of this idea, in which drivers’ attentional patterns in bicycle-rich and bicycle-poor environments are compared or in which drivers’ encounters with bicyclists are manipulated to determine how their expectations and attention change as the probability of encounters changes, would be very useful so that we can decide whether this account is likely to be correct. Another reason to believe that bicycle collisions involve a top-down cognitive process in the driver comes from drawing an analogy with the motorcycling literature. From analysis of a substantial accident database, Magazzu`, Comelli, and Marinoni (2006) found that people who could ride motorcycles were less likely to collide with other motorcyclists even when driving cars. The finding that drivers who have ridden motorcycles are more likely to perceive a motorcycle on the road and deal with it appropriately, even when in a car, suggests again that such collisions cannot primarily be attributed to the rider: Motorcyclists are there to be seen if only drivers are sufficiently prepared to see them. It is simply that many are not prepared this way. Of course, were we being slightly flippant, we might be tempted to explain away Magazzu` et al.’s (2006) data in Darwinian terms and suggest that given the extent to which motorcycles do not protect their users from mistakes (Lynham, Broughton, Minton, & Tunbridge, 2001),

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a motorcyclist who has survived for any length of time might simply have abnormally good perceptual ability and reflexes. However, such an argument certainly falls down when we consider the work of Brooks and Guppy (1990). These researchers found that merely having a motorcycling friend or relative was sufficient to make drivers better at perceiving motorcycles on the road. Again, these findings suggest that drivers’ failures to perceive motorcycles cannot simply be explained away by the suggestion that motorcycles are intrinsically difficult to see. Because the circumstances of motorcycle and bicycle collisions are very similar in many cases (Atkinson & Hurst, 1983; Harrison, 2004; Lynham et al., 2001; Stone & Broughton, 2002), it would clearly be useful to determine whether, as in the studies of Magazzu` et al. and Brooks and Guppy, direct or indirect experience of bicycling makes drivers more likely to perceive bicyclists on the road. As yet, however, this has not been studied. If such research is eventually carried out, we might gain insights into remedial actions to prevent collisions arising from drivers’ perceptual failures. We might also avoid the unfortunate state of affairs whereby vulnerable road users struck following drivers’ perceptual failures are blamed for failing to make themselves sufficiently conspicuous (Miller, Kendrick, Coupland, & Coffey, 2010). In summary, mixing with motor vehicles around intersections is a particular source of danger for bicyclists and one that is not entirely removed even with segregated bicycling facilities. Moreover, it is likely that this is more a product of drivers’ perceptual failures than of anything to do with the bicyclists. More research is certainly needed to further explore these issues.

4. THE MINORITY STATUS OF BICYCLING, STEREOTYPES, AND DRIVER BEHAVIOR I can understand the pedestrians’ point of view because of the amount of times I’ve been in Oxford city centre and I’ve just walked out and thought “Hell, this is actually a road!” but have just walked straight out. I can understand thatdI’m always aware of thatdbecause of the amount of times I’ve done it. So I can forgive pedestrians, but cyclists I cannot.

This statement, from a professional bus driver (Walker, 2005a), illustrates the problem of what we might as well call modal empathy. The driver expresses a willingness to forgive the lapses of a group with which she identifies but refuses to tolerate the same behaviors from a group to which she does not feel belonging. Bicycling is a minority behavior in many countries, and there are surely none left on Earth where bicycling is a more aspirational activity than driving. In a context whereby most road users do not identify with or aspire toward bicycling, might the outsider status of bicycling be working against bicyclists, generating

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resentment or even threats to their safety? To date, as noted previously, there has been little research specifically examining whether drivers’ lack of bicycling experience directly affects their treatment of bicyclists. However, there is a small body of work on nonbicyclists’ internal representations of bicyclists that might be relevant. Basford, Reid, Lester, Thomson, and Tolmie (2002) appear to be the first group to have seriously considered the idea that bicyclists might be viewed somehow as “other” by the majority of road users, discussing driverebicyclist interactions in terms of the in-group and out-group effects well-known in social psychology. Gatersleben and Haddad (2010) probed into these grouping effects using a factoranalytic approach to search for consistent patterns in people’s concepts of bicyclists. Specifically, a group of bicyclists and a group of nonbicyclists were presented with short descriptions of bicyclists, each of which mentioned a behavioral, a motivational, or a visual characteristic (e.g., “They wear tight clothing” or “They bicycle to work”). By assessing how often descriptions were selected together, the authors could identify groups of characteristics that seemed to fit together in people’s minds. They found, among other things, that the typical driver perceives only a very limited range of bicyclist stereotypes, including the “die-hard” bicyclist (who rides as fast as possible, helmeted, on an expensive bicycle) and the “necessity” bicyclist (who rides for functional transport and does not enjoy it). These perceptions appeared in many cases to act as a barrier to the uptake of bicycling, with nonbicyclists apparently finding it difficult to view themselves bicycling because they did not share the identities and motives they perceived among existing bicyclists. Interestingly, a slightly earlier study of mine suggested that such stereotypes might cause measurable behavioral changes in drivers that could affect riders’ safety (Walker, 2007). Using an instrumented bicycle, which kept accurate records of how close vehicles passed to it, I was able to log more than 2200 instances of vehicles overtaking me on city streets, all the time keeping my riding behavior as constant as possible while manipulating two key variables: my position on the road and whether or not I wore a helmet. All other things being equal, donning a helmet was, on average, associated with a significant reduction in the space left by overtaking drivers. Why should simply putting a helmet on my head have led to drivers overtaking more closely, and why should hiding the helmet have led to them leaving more space? Both Basford et al. (2002) and Gatersleben and Haddad (2010) found that bicycle helmets were seen, by many nonbicyclists, as an index of experience and skill. As described previously, Gatersleben and Haddad found the helmet to be associated with the die-hard type of bicyclist for many nonriders. Similarly, Basford et al. found that “pictures of cyclists wearing helmets were generally considered to be more serious and

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sensible on the road than those without” (p. 9) and often “it was felt that people who had arranged appropriate and/or specialist cycling equipment and clothing were more likely to have also the experience and/or training to employ correct cycling behavior” (p. 9). I am saying nothing here about the efficacy of bicycle helmets, merely that many nonbicyclists seem to have a certain schema invoked by seeing one, which apparently leads to measurable changes in their behavior that might endanger bicyclists. It is clear that more research on these issues, as well as the more general issue of how out-group status might influence the way bicyclists are treated, would be highly valuable. As a final note, before leaving the “out-group” status of bicycling, alert readers might have noticed that I have been perpetuating just such a grouping throughout this chapter. Using the normal discourse surrounding this topic, I have written of “bicyclists” and “nonbicyclists” almost as though these are castes to which one is born. In reality, of course, a person can be both a bicyclist and a driver on the same day, and terms such as “bicycle user” and “car user” would probably be preferable to reflect this. The highly pervasive nature of the term “bicyclist” is fascinating, and I would be delighted to read an analysis of this from a sociolinguist or other suitably qualified person.

5. THE HUMAN NATURE OF BICYCLISTS This final issue relates back to the first, and it concerns another aspect of bicyclists and drivers sharing road space. I once, in an exploratory study with no particular hypothesis, took a series of photographs showing street scenes and asked participants to describe, in their own words, what they saw in the pictures (Walker, 2005b, 2005c). When the descriptions were analyzed, a surprisingly clear story emerged. Whenever a picture showed a motor vehicle such as a car, the language people used to describe it was always inhuman: The words chosen were “car,” “vehicle,” and so on. So viewers would say “A car is turning left” or “A car is waiting for pedestrians to cross,” not “A driver is waiting for pedestrians to cross,” which would of course be more logical. In contrast, when a picture showed a bicycle and its rider, the words used were human: “A cyclist is .,” “A man is .,” and never “‘A bicycle is ..” This effect remained even when the driver of a car was clearly visible: Even when the driver could easily be seen, the words people chose referred to the car and not the driver, perhaps suggesting that the most salient component of the scene, for the viewer, was the machine and not the person controlling it. However, in the case of the bicycle, the words suggested that the salient component was the person, not the machine the person was piloting. Might this matter? Might bicyclists be treated in a qualitatively

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different way on the road, as they are in people’s descriptions, simply because they are so clearly human to other road users? Some follow-up experiments attempted to explore this further, and they found intriguing signs that because bicyclists remain clearly humandundisguised by a vehicle, as motorists usually aredthis might indeed affect how other road users interact with them. For example, it has long been known from laboratory studies that making eye contact with another person causes distinct and powerful neural responses (Gale, Spratt, Chapman, & Smallbone, 1975), indicative of eye contact’s important status as a social signal. Accordingly, an experiment suggested that eye contact between a driver and a bicyclist led to the driver making decisions substantially more slowly than in equivalent situations in which there was no eye contact (Walker, 2005d). Somehow, the eye contact seemed to interfere with, or delay, drivers’ decisions, perhaps because it invokes an extra, involuntary stage of cognitive processing. Bicycle users will clearly invoke eye contact in other road users far more than motorists. In addition, when we later used eye tracking equipment to examine drivers’ attentional patterns as they made decisions about a bicyclist, we saw a powerful tendency for drivers to fixate immediately on the bicyclist’s face the moment the bicyclist appeared and to linger on the face longer than any other part of the scene (Walker & Brosnan, 2007). These data on the human appearance of bicyclists, and how this might influence driver attention and decision making, are still at the point of being intriguing but inconclusive. They suggest that there might be a fundamental asymmetry among people sharing the road: When a driver and a bicyclist meet, it might be that the bicyclist largely has the experience of interacting with a machine, whereas the driver largely has the experience of interacting with a person. The consequences of this asymmetry, combined with the additional asymmetry arising from the car’s greater ability to cause damage in a collision, certainly seem worthy of additional study.

6. SUMMARY In principle, bicycling is just as diverse an activity as driving, but its many facets have received less behavioral research than other areas of traffic psychology. (The engineering aspects of bicycling, in contrast, have enjoyed a considerable amount of attention; Wilson, 2004.) In this chapter, instead of attempting to outline a clear narrative or present definite findings about the psychology or behavior of bicycling, I highlighted three areas of bicycling research that I believe are sufficiently intriguing and important to merit further attention. I hope that researchers take up the challenge posed by such topics and work toward providing

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the scientific literature needed to encourage higher levels of bicycling in the future.

ACKNOWLEDGMENT I thank Heidi Bailey for her help with finding the references.

REFERENCES Atkinson, J. E., & Hurst, P. M. (1983). Collisions between cyclists and motorists in New Zealand. Accident Analysis and Prevention, 15, 137e151. Aultman-Hall, L., & Adams, M. F. (1998). Sidewalk bicycling safety issues. Transportation Research Record, 1636, 71e76. Aultman-Hall, L., & Hall, F. L. (1998). OttawaeCarleton commuter cyclist on- and off-road incident rates. Accident Analysis and Prevention, 30, 29e43. Aultman-Hall, L., & Kaltenecker, M. G. (1999). Toronto bicycle commuter safety rates. Accident Analysis and Prevention, 31, 675e686. Basford, L., Reid, S., Lester, T., Thomson, J., & Tolmie, A. (2002). Drivers’ perceptions of cyclists. Report No. TRL549. Wokingham, UK: Transportation Research Laboratory. Bhatia, R., & Wier, M. (2010). “Safety in numbers” re-examined: Can we make valid or practical inferences from available evidence? Accident Analysis and Prevention, 43, 235e240. Brooks, P., & Guppy, A. (1990). Driver awareness and motorcycle accidents. Proceedings of the International Motorcycle Safety Conference, 2(10), 27e56. Carsten, O. M. J., Sherborne, D. J., & Rothengatter, J. A. (1998). Intelligent traffic signals for pedestrians: Evaluation of trials in three countries. Transportation Research Part C: Emerging Technologies, 6, 213e229. Gale, A., Spratt, G., Chapman, A. J., & Smallbone, A. (1975). EEG correlates of eye contact and interpersonal distance. Biological Psychology, 3, 237e245. Garder, P., Leden, L., & Thedeen, T. (1994). Safety implications of bicycle paths at signalized intersections. Accident Analysis and Prevention, 26, 429e439. Gatersleben, B., & Haddad, H. (2010). Who is the typical bicyclist? Transportation Research Part F: Traffic Psychology and Behavior, 13, 41e48. Hamilton-Baillie, B. (2008a). Shared space: Reconciling people, places and traffic. Built Environment, 34, 161e181. Hamilton-Baillie, B. (2008b). Towards shared space. Urban Design International, 13, 130e138. Harrison, W. A. (2004). Avoidance learning and collisions with motorcycles at intersections. Paper presented at the Third International Conference on Traffic and Transport Psychology, Nottingham, UK, 5e9 September. Herslund, M.-B., & Jørgensen, N. O. (2003). Looked-but-failed-to-see errors in traffic. Accident Analysis and Prevention, 35, 885e891. Hindle, P. (2001). Roads and tracks for historians. Chichester, UK: Phillimore. Jacobsen, P. L. (2003). Safety in numbers: More walkers and bicyclists, safer walking and bicycling. Injury Prevention, 9, 205e209.

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Lynham, D., Broughton, J., Minton, R., & Tunbridge, R. J. (2001). An analysis of police reports of fatal accidents involving motorcycles. Report No. 492. Wokingham, UK: Transport Research Laboratory. Magazzu`, D., Comelli, M., & Marinoni, A. (2006). Are car drivers holding a motorcycle licence less responsible for motorcycleecar crash occurrence? A non-parametric approach. Accident Analysis and Prevention, 38, 365e370. McCarthy, M., & Gilbert, K. (1996). Cyclist road deaths in London 1985e1992: Drivers, vehicles, manoeuvers and injuries. Accident Analysis and Prevention, 28, 275e279. McGurn, J. (1987). On your bicycle: An illustrated history of cycling. London: Murray. Miller, P. D., Kendrick, D., Coupland, C., & Coffey, F. (2010). The use of conspicuity aids by cyclists and risk of crashes involving other road users: A protocol for a population based caseecontrol study. BMC Public Health, 10, 39. Moritz, W. E. (1998). Adult bicyclists in the United StatesdCharacteristics and riding experience in 1996. Transportation Research Record, 1636, 1e7. Morris, J. (1994). Exercise in the prevention of coronary heart disease: Today’s best buy in public health. Medicine & Science in Sports and Exercise, 26, 807e814. Ogilvie, D., Egan, M., Hamilton, V., & Petticrew, M. (2004). Promoting walking and cycling as an alternative to using cars: A systematic review. British Medical Journal, 329, 763e766. Olkkonen, S., La¨hderanta, U., Tolonen, J., Sla¨tis, P., & Honkanen, R. (1990). Incidence and characteristics of bicycle injuries by source of information. Acta Chirurgica Scandinavica, 156, 131e136. Ra¨sa¨nen, M., & Summala, H. (1998). Attention and expectation problems in bicycleecar collisions: An in-depth study. Accident Analysis and Prevention, 30, 657e666. Rodgers, G. B. (1995). Bicyclist deaths and fatality risk patterns. Accident Analysis and Prevention, 27, 215e223. Stone, M., & Broughton, J. (2002). Getting off your bike: Cycling accidents in Great Britain 1990e1999. Accident Analysis and Prevention, 35, 549e556. Summala, H., Pasanen, E., Ra¨sa¨nen, M., & Sieva¨nen, J. (1996). Bicycle accidents and drivers’ visual search at left and right turns. Accident Analysis and Prevention, 28, 147e153. Tudor-Locke, C., Ainsworth, B. E., & Popkin, B. M. (2001). Active commuting to school: An overlooked source of childrens’ physical activity? Sports Medicine, 31, 309e313. Tudor-Locke, C., Neff, L. J., Ainsworth, B. E., Addy, C. L., & Popkin, B. M. (2002). Omission of active commuting to school and the prevalence of children’s health-related physical activity levels: The Russian Longitudinal Monitoring Study. Child: Care, Health and Development, 28, 507e512. Walker, I. (2005a). Oxford and Cambridge bus drivers: An exploratory study of opinions and experiences. Oxford: Oxfordshire County Council. Walker, I. (2005b). Road users’ perceptions of other road users: Do different transport modes invoke qualitatively different concepts in observers? Advances in Transportation Studies, A6, 25e33. Walker, I. (2005c). Vulnerable road user safety: Social interaction on the road? In L. Dorn (Ed.), Driver behaviour and training, Vol. 2 Aldershot, UK: Ashgate. Walker, I. (2005d). Signals are informative but slow down responses when drivers meet bicyclists at junctions. Accident Analysis and Prevention, 37, 1074e1085.

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Walker, I. (2007). Drivers overtaking bicyclists: Objective data on the effects of riding position, helmet use, vehicle type and apparent gender. Accident Analysis and Prevention, 39, 417e425. Walker, I. (2009). Successful community road safety programmes. Edmonton, Canada: Keynote address at the International Conference on Urban Traffic Safety. March 17, 2009, as guest of City of Edmonton/Edmonton Police.

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Walker, I., & Brosnan, M. (2007). Drivers’ gaze fixations during judgements about a bicyclist’s intentions. Transportation Research Part F: Traffic Psychology and Behaviour, 10, 90e98. Wilson, D. G. (2004). Bicycling science (3rd rev. ed.). Cambridge, MA: MIT Press. Wilson, S. S. (1973, March). Bicycle technology. Scientific American 81e91.

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Chapter 27

Motorcyclists David J. Houston University of Tennessee, Knoxville, TN, USA

1. INTRODUCTION In comparison to the amount of research attention paid to transport safety in four-wheeled vehicles, relatively little research has focused on motorcyclist safety. This is understandable considering that motorcycles comprise only a small proportion of crash fatalities in the industrialized nations where most research has focused. However, during the past two decades, the popularity of motorcycles has increased. At a time when most motor vehicle users are experiencing greater safety, motorcyclist fatalities are increasing in the United States and in many other nations. Substantial global gains in traffic safety will likely require an improvement in motorcyclist safety. Motorcyclists are the most vulnerable motor vehicle users on the road because they are at greater risk to be involved in a crash and to sustain a severe or fatal injury. An early study of motorcycle crashes in the United States reported that 96% of motorcyclists involved in a crash sustained some sort of injury (Hurt, Ouellet, & Thom, 1981). Similarly, Diamantopoulou, Brumen, Dyte, and Cameron (1995) determined that 50% of Australian motorcycle crashes resulted in a fatality or a severe injury compared to only 35% for crashes involving other vehicles. The heightened vulnerability of motorcyclists is due to characteristics of the machine, the environment, and human behavior. In the event of a crash, motorcycles afford riders little physical protection, and their “single track” design and higher power-to-weight ratio make stability and handling more challenging. They can easily become unstable, making it difficult to keep the vehicle upright and under control. This is especially a concern during braking because motorcycles are susceptible to skidding and a loss of control if brakes are not properly applied (e.g., locking up the brakes). The higher power-to-weight ratio means that motorcycles can accelerate quickly and reach high speeds (Elliott, Armitage, & Baughan, 2003). Thus, operating a motorcycle is a more complex task than driving a car because it requires superb coordination, balance, and motor skills (Mannering & Grodsky, 1995). Furthermore, the small size of a motorcycle reduces its conspicuity (i.e., it is difficult for motorists to see), Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10027-X Copyright Ó 2011 Elsevier Inc. All rights reserved.

leading to “looked but failed to see” errors in which another motorist violates the rider’s right of way. Even when it registers to another motorist that a motorcycle is approaching, a motorcycle’s small mass makes it difficult to accurately judge its speed and time of arrival (Caird & Hancock, 1994). The environment (e.g., weather, road condition, and road design) poses greater challenges for riders than for other vehicle users. Weather conditions are likely to make motorcycle travel more hazardous. The stability or handling of a motorcycle can be affected by poor and uneven road conditions or slick lane and road markings. Typically, roads and highways are designed to promote efficient, fast travel for larger vehicles, and they can place motorcyclists at greater risk. Multilane roads, the presence of an uncontrolled left turning lane, and wide medians have been found to increase the risk of a motorcycle crash (Haque, Chin, & Huang, 2010). In addition, crash barriers and guardrails designed to keep heavier vehicles from crossing into oncoming traffic or from leaving the roadway have been found to increase the severity of injury suffered when collided with by a motorcyclist (Brailly, 1998; as cited in Elliott et al., 2003). Frequently, an engineering perspective is adopted to promote safer vehicle and road designs. Although technological safety developments for motorcycles have lagged behind developments for cars and trucks, several can be identified. Gains in safety have come in the form of enhanced stability offered by antilock braking systems (Watson, Tunnicliff, White, Schonfeld, & Wishart, 2007). Compensation for the lack of physical protection has led to the design of leg fairings that reduce the risk of a lower limb injury, increased motorcyclist conspicuity through the use of daytime running lights, and the development of helmets that substantially reduce the risk of a head injury or fatality (Bayly, Regan, & Hosking, 2006; Elliott et al., 2003; Watson et al., 2007; Wells et al., 2004). Efforts to redesign the roadway environment entail altering road design and materials, designing safer crash barriers, and the placement of road signage that reduces obstructions. Although an engineering perspective is very important for enhancing safety, behavioral factors play a major role in most 375

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traffic crashes (Evans, 2004; Peden et al., 2004). In 2004, the Association des Contructeurs Eruope´ens de Motocycles (ACEM) conducted the Motorcycle Accident In-Depth Study (MAIDS), which was an investigation of a sample of 921 motorcycle crashes that occurred in France, Germany, The Netherlands, Spain, and Italy. Behavioral factors were determined to be the primary cause in 87% of these crashes, with 37% being attributed to the motorcycle rider and 50% to the driver of another vehicle. In contrast, environmental and vehicle factors were considered the primary cause in only 8 and 4% of these crashes, respectively. However, relatively little is known about the role of human behavior in motorcyclist safety. Early periods of research focused on crash analysis (1970s) and the process of riding (1980s) (Chesham, Rutter, & Quine, 1993a). Although correlates of crashes and the types of risky behavior that increase the likelihood of a crash have been identified, only recently have scholars turned attention to understanding the psychology of risky or unsafe motorcycle riding. Toward this end, in the early 1990s a conception of the rider as an “active agent” began to emerge, highlighting the importance of psychosocial influences on riding behavior (Chesham et al., 1993a). Improving safety requires understanding the importance of the three components in motorcycle transport: the machine, the environment, and human behavior. Whereas the first two of these components are largely the realm of engineering, arguably it is human behavior that is the most important factor in motorcyclist crashes, and this is the realm of transport psychology. The purpose of this chapter is to identify key issues relevant to motorcyclist safety with special attention to theoretical developments in transport psychology. The following section discusses key trends in motorcyclist safety. To better understand why crashes occur, the most frequent types of crashes are presented along with descriptive correlates of these crashes. Research on the psychosocial influences on riding behavior is then summarized. The final section discusses the implications of research in transport psychology on motorcyclist safety and suggests future topics to explore.

2. TRENDS IN MOTORCYCLE USE AND SAFETY At a time when traffic safety is improving for most motorists, motorcyclists generally have experienced increased risk. For instance, a study of trends in 30 nations revealed that from 2000 to 2009, the overall number of annual road fatalities decreased for all but 1 of these nations. However, in 13 of these nations, the annual number of motorcyclist fatalities increased. Even in those nations that experienced a reduction in annual motorcyclists fatalities, these reductions lagged behind those of other motorists.

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Vulnerable and Problem Road Users

The United States provides an example of the trend of increased motorcyclist fatalities. From 1980 to 1997, the number of rider deaths declined from 5144 to 2116. However, motorcyclist fatalities have increased each year since and reached a recorded high of 5312 in 2008. During this same 30-year period (1980e2010), total traffic fatalities decreased from 51,091 to 37,423 (National Highway Traffic Safety Administration (NHTSA), 2010). Several factors underlie these trends. First, the popularity of motorcycles began to increase in the mid-1990s and these vehicles now comprise a larger, albeit still small, proportion of the motorized vehicles in many developed nations. Second, motorcycles have gotten more powerful. Engine displacement (in cubic centimeters (cc)) is a common measure of the power of a motorcycle, with larger engines generally being able to produce more power. In the United States, Shankar and Verghese (2006) reported that in 1990, approximately 21% of on-highway motorcycles had an engine displacement less than 350 cc and 41% had more than 750 cc. By 2003, the smaller motorcycles accounted for only 7% of on-highway motorcycles, whereas 76% of motorcycles had an engine displacement more than 750 cc. Other nations have experienced a similar shift in the motorcycle fleet. Third, rider demographics have changed. Although motorcyclists are still predominantly male, they are becoming an older age group as more individuals in their 40s are picking up motorcycling as a new hobby or are returning to it after some time away (Elliott et al., 2003; Jamson & Chorlton, 2009). As a consequence, motorcycle crashes involve proportionally more older riders than in past years. In Australia, for instance, in 1985, riders younger than age 26 years comprised 71% of motorcyclist fatalities, a figure that declined to 25% in 2008. Conversely, whereas riders 40þ years old accounted for only 5% of fatalities in 1985, they accounted for 39% in 2008 (Department of Infrastructure, Transport, Regional Development, and Local Government (DITRDLG), 2009). A similar trend is apparent in the United States: From 1998 to 2008, the percentage of fatally injured riders younger than age 30 years decreased from 40 to 33%, whereas fatalities for riders 40þ years old increased from 33 to 51% during this period (NHTSA, 2010). Motorcycling safety is no longer just a young person’s issue. Fourth, the issue of motorcyclist safety is important because riders are overrepresented in crash statistics. Data from three nations in which motorcyclist crashes are frequently studied illustrate this point. In the United States, motorcycles comprise only 3% of registered motor vehicles and account for 0.5% of vehicle miles traveled, but they account for 14.2% of all traffic fatalities (NHTSA, 2010). In the United Kingdom in 2008, 3.8% of licensed vehicles were motorcycles, which accounted for 1% of vehicle miles traveled and 19.4% of all road fatalities

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(Department for Transport, 2009). Data from 2007 for Australia indicate that 3.4% of registered vehicles were motorcycles. It is estimated that motorcyclists accounted for 0.9% of kilometers traveled but comprised 14.8% of road fatalities (Australian Bureau of Statistics, 2008; DITRDLG, 2009). A better picture of rider vulnerability is gleaned from comparing motorcyclist fatality rates to those experienced by other road users. For the years just referenced, based on the amount of travel, the motorcyclist fatality rate is 39.4 times higher than the car fatality rate in the United States and 39.4 and 17.5 times higher in the United Kingdom and Australia, respectively. Furthermore, in the European Union as a whole, the risk of a motorcyclist fatality is 20 times that of a car passenger (Organization for Economic Co-operation and Development, 2010). Similar trends are occurring in developing nations as well. Due to their low cost, convenience, and ability to maneuver on congested roads, motorcycles are an especially important mode of transportation in middleand low-income nations. In China, motorcycles accounted for 23% of total motorized vehicles in 1987, a figure that rose to 54% by 2005 (Xuequn, Ke, Ivers, Du, & Senserrick, 2011). Motorcycles account for 67% of vehicles in Taiwan (Chang & Yeh, 2007), 52% in Nigeria (Oluwadiya et al., 2009), 60% in Malaysia (Radin-Umar, Mackay, & Hills, 1996), and 95% in Vietnam (Hung, Stevenson, & Ivers, 2006). Consequently, related injuries and fatalities have increased in these medium- and low-income nations (Ameratunga, Hijar, & Norton, 2006; Lin & Kraus, 2009). In Singapore, motorcyclists account for 54% of traffic fatalities and 51% of injuries (Haque, Chin, & Huan, 2010). They account for more than 50% of fatalities in Malaysia and Taiwan (Radin-Umar et al., 1996), 80% of traffic injuries in Thailand (Ichikawa, Chadbunchachai, & Marui, 2003). In China, 22.2% of all 2007 road fatalities were motorcyclists, a three-fold increase over the 7.5% recorded in 1987 (Xuequn et al., 2011).

3. CHARACTERISTICS OF CRASHES One emphasis in research on motorcycle safety has been to identify situations in which crashes tend to occur. One notable typology was offered by Preusser, Williams, and Ulmer (1995) based on an examination of fatal crashes in the United States during 1992. Through their analysis, Preusser et al. identified five common crash types. The most common involved a motorcyclist running off the road and overturning or striking an off-road object. These crashes comprised 41% of the sample, often occurred on a curve, and tended to be due to excessive alcohol consumption or riding too fast for the conditions. The rider was at fault in nearly all these crashes.

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The second most common type involved another vehicle that failed to stop or yield the right-of-way to a motorcyclist (Preusser et al., 1995). These accounted for 18% of the sample of crashes and often occurred at controlled road intersections. The primary fault for these crashes tended to be other motorists, who were responsible for 66% of these crashes. The third type of crash involved a head-on collision, usually a rider who crossed over into oncoming traffic. These were more likely to occur in rural areas, on higher speed roads, and on curves. In approximately 73% of these cases, it was the motorcycle rider who was primarily at-fault. A fourth type of crash involves one vehicle turning across the path of another, which occurred in 9% of the cases (Preusser et al., 1995). In nearly all these crashes (approximately 99%), the other motorist was primarily at fault. These crashes are an example of “looked but failed to see” errors, in which the driver of the other motor vehicle claims to have looked but did not see the motorcycle approaching. The last type of crash that Preusser et al. identified involved a rider who lost control and came off the motorcycle. This last crash may involve a rider falling off a motorcycle when engaged in a risky maneuver or when trying to avoid a collision. The primary liability for these crashes could not be determined based on the available data. An important distinction among these crashes is whether other vehicles are involved. In general, motorcycle-only crashes are less frequent than those that involve a motorcycle and another motor vehicle. For instance, an in-depth study of more than 900 crashes that occurred in Los Angeles during 1976 and 1977 revealed that 26% were single-vehicle crashes (Hurt et al., 1981). Similarly, Tunnicliff’s (2005) study of fatal motorcycle crashes in Australia reported that 41% did not involve another vehicle. Other studies have indicated that only slightly more than one-third of fatal crashes are in the single-vehicle category (Christie & Harrison, 2002). Riders largely are at fault in single-vehicle crashes, which are frequently due to rider error and often involve a motorcycle leaving the road. Shankar (2001) indicated that 80% of fatal single-vehicle crashes occurred on the shoulder, median, or off the side of the road. Riding off the road on a curve is common, and it has been found to be the cause in approximately one-third of single-vehicle crashes (Christie & Harrison, 2002). Excessive alcohol is more commonly found in these crashes than in multivehicle ones (ACEM, 2004). In contrast, multivehicle crashes are more common, and most of the time the driver of the other motor vehicle is at fault. For instance, the MAIDS study concluded that the motorcycle rider was primarily at fault in only 32% of the European crashes that were examined (ACEM, 2004). Most typically, these crashes involve a nonrider failing to yield the right-of-way to the motorcyclist. Hurt et al. (1981)

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indicated that in two-thirds of multivehicle crashes, the other driver violated the motorcyclist’s right-of-way. Clarke, Ward, Bartle, and Truman (2007) reported that crashes involving a right-of-way violation comprised 38% of UK crashes during 1997e2002, and in less than 20% was a motorcyclist either partly or fully at fault. The majority of these crashes occur at a T-junction or a fourlegged intersection (Clarke et al., 2007; Haque, Chin, & Huang, 2010). These crashes also occur when a motorist turns in front of a motorcycle approaching from the opposite direction. The cause of most of these crashes is a “looked but failed to see” error in which a driver states that he or she looked but failed to see the approaching motorcycle. Clarke et al. estimated that 65% of crashes involving a right-of-way violation were caused by a driver failing to see an approaching motorcycle that was clearly visible to others at the scene, and they seemed to occur with greater frequency among drivers 65þ years of age. In terms of roadway type, motorcyclists are especially vulnerable to other motorists in complex traffic situations such as intersections (e.g., four-legged intersection and Tjunctions) and uncontrolled left-turning lanes (or right-turn lanes in the United Kingdom) (Sexton, Baughan, Elliott, & Maycock, 2004). Curves or bends in roads also present a risk to motorcyclists because crashes in these road sections increase the likelihood of a motorcyclist death or serious injury by 2 and 1.5 times, respectively, compared to other crashes (Clarke et al., 2007). On these curved road sections, typically it is the leisure-oriented rider who places him- or herself at risk due to speeding and inexperience. Regarding population density, accidents that occur in rural areas are more likely than not to be the result of poor handling skills and risk taking (Lin, Chang, Huang, Hwang, & Pai, 2003; Lin, Chang, Pai, & Keyl, 2003), whereas accidents that occur in urban areas are more likely to be the fault of the other motor vehicle driver and typically involve a right-of-way violation (Clarke et al., 2007; Sexton et al., 2004). For instance, in Singapore, 58% of motorcyclists involved in crashes that occurred at intersections were the victims in the crashes, whereas only 33% of riders in crashes on expressways were the victims (Haque, Chin, & Huang, 2009). Lastly, motorcyclist crashes are more common on rural roads with high posted speed limits. For instance, Savolainen and Mannering (2007) report that crashes on U.S. roads with speed limits exceeding 50 mph have a 132% higher likelihood of a fatal injury than crashes that occur on roads with lower posted speed limits. When a crash does occur, the most common injuries that motorcyclists sustain in a nonfatal crash are to the lower limbs. Watson et al. (2007) report 38% of injuries to be to the legs, 30% to the arms, 18% to the torso, and 12% involve the head and neck. Similarly, Elliott et al. (2003) state that 40e60% of all nonfatal injuries are to legs, approximately 25% to the head, and 20% to the arms.

PART | IV

Vulnerable and Problem Road Users

Although head injuries are less frequent in nonfatal motorcycle crashes, typically these are more serious, require greater time for recover, are more likely to be incapacitating, and require a greater financial expenditure (Eastridge et al., 2006). Furthermore, head injuries are the major cause of death following a motorcycle crash (Talving et al., 2010). The most important behavior that riders can perform to reduce the risk of serious injury and death due to a head injury is to wear helmets, which have been found to reduce the risk of sustaining a head injury by 69% and decrease the risk of death by 42% (Liu et al., 2008). For this reason, mandatory helmet use laws have been adopted that require helmets to be used by all motorcyclists. In states with these compulsory laws, helmet use rates are higher and rider fatality rates are lower (Houston & Richardson, 2008). However, in the absence of more fundamental behavioral change, the effectiveness of a helmet law is in part a function of perceived enforcement effort. For this reason, helmet use often is lower in rural areas where enforcement is lax (Xuequn et al., 2011).

4. CORRELATES OF CRASHES Beyond developing profiles of common crash circumstances and sustained injuries, investigations of motorcycle crashes have identified demographic and other attributes that correlate with an increased likelihood of a crash. The vast majority of motorcyclists are male (approximately 90% in many nations), who are also overrepresented in crash statistics (Christie & Harrison, 2002; Haworth, Smith, Brumen, & Pronk, 1997; Mannering & Grodsky, 1995; Watson et al., 2007). The overrepresentation is a function of the amount of travel that males account for and a stronger propensity for risky behaviors associated with increased crash risk (Fergusson, Swain-Campbell, & Horwood, 2003; Lin, Chang, Pai, et al., 2003; Rutter & Quine, 1996; Savolainen & Mannering, 2007). In addition, the risk of a crash has consistently been found to decline with rider age (Harrison & Christie, 2005; Rutter & Quine, 1996), a relationship that is especially pronounced among male riders (Maycock, 2002). For instance, riders younger than 25 years of age are more likely than older riders to be involved in a motorcycle crash and to experience a moderate or fatal injury (ACEM, 2004; Haworth et al., 1997; Sexton et al., 2004; Watson et al., 2007). Similarly, a New Zealand study found that riders younger than 25 years of age had more than double the risk of a fatal or moderately serious injury when involved in a crash (Mullin, Jackson, Langley, & Norton, 2000). Zambon and Hasselberg (2006) conclude that the majority of riders involved in crashes are young men who typically adopt unsafe attitudes and behaviors that increase the risk of a crash and injury.

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Motorcyclists

It is not just the risk of a crash that is associated with age: The likelihood of being at fault also declines with rider age (Clarke et al., 2007; Elliott, Armitage, & Baughan, 2007). This can in part be explained by a heightened propensity for risky behavior (Lin, Chang, Pai, et al., 2003; Rutter & Quine, 1996). Younger riders possess an increased willingness to break the law (Chang & Yeh, 2007; Rutter & Quine, 1996), such as riding faster than the posted speed limit (Fergusson et al., 2003; Teoh & Campbell, 2010) and riding without a valid operator’s license (Watson et al., 2007). However, in recent years, the number of fatalities suffered by riders older than 40 years of age has increased substantially in nations such as the United States (Shankar, 2001; Stutts, Foss, & Svoboda, 2004), the United Kingdom (Sexton et al., 2004), and Australia (Australian Transport Safety Bureau, 2007), raising concerns that the nature of the “motorcycle safety problem” has evolved from one that primarily is concerned with young riders to one that is about older riders. The concern is with the middle-aged riders who are returning to motorcycling after years away (“born-again riders”) or who are picking it up for the first time. This age group possesses the economic means to afford larger, more powerful motorcycles but may have diminished physical skills that make them ill-suited for the larger sized machines. A few studies have found that older riders do have an increased risk of severe or fatal injury from a crash (Quddus, Noland, & Chin, 2002; Savolainen & Mannering, 2007; Shankar & Mannering, 1996). However, the risk of a crash is still higher for younger riders (Mullin et al., 2000; Sexton et al., 2004), and Sexton et al. found that returning riders are not at greater risk than others. Although new riders have a higher crash risk regardless of age, the fatality trends regarding older riders appear to more appropriately reflect the growing number in this age group who now enjoy motorcycling (Haworth, Mulvihill, & Simmons, 2002; Watson et al., 2007). In addition to rider demographics, risk exposure and riding experience are also correlated with the likelihood of a crash. The risk of a crash increases with the number of miles ridden (Lin, Chang, Huang, et al., 2003; Lin, Chang, Pai, et al., 2003; Mannering & Grodsky, 1995; Sexton et al., 2004). Conversely, the more years that an individual has been riding, the lower his or her likelihood of being in a crash (Chesham, Rutter, & Quine, 1993b; Haworth et al., 1997; Lin, Chang, Huang, et al., 2003; Lin, Chang, Pai, et al., 2003; Savolainen & Mannering, 2007; Sexton et al., 2004) and the less likely the individual is to be at fault for a crash in which he or she is involved (Sexton et al., 2004). In fact, a significant number of motorcycle accidents occur within the first 6 months of riding (Elliott, Armitage, et al., 2007; Sexton et al., 2004). Although age and riding experience are correlated, studies that have sought to disentangle these effects have concluded that experience and age

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have independent influences on motorcyclist safety (Elliott, Armitage, et al., 2007; Maycock, 2002; Sexton et al., 2004). Regarding behavior, much of the literature has found that engaging in risky behavior (e.g., speeding, overtaking, and performing stunts) increases the likelihood of a motorcyclist crash and death. For instance, speed is the most frequently cited contributing factor to motorcycle crashes. Studies of Australian accidents have found that speed is a factor in more than half of multivehicle crashes (Federal Office of Road Safety, 1999). Similarly, the difference in the traveling speed of motorcycles compared to surrounding traffic was found to be a direct contributory factor in 66% of crashes involving a motorcycle in Europe (ACEM, 2004). Fast speeds also increase the likelihood that the rider is at fault (Elliott, Armitage, et al., 2007), a conclusion that is consistent with the finding that riders are more likely to be at fault in crashes that occur on roads with higher speed limits (Clarke et al., 2007). It is the very nature of the thrill that can come from speed that attracts some motorcyclists. For instance, a survey of U.S. motorcyclists found that 70% of those sampled had ridden at more than 100 mph on a public road, and approximately 40% of these anticipated doing so again (Mannering & Grodsky, 1995). In a qualitative study of motorcyclist motivations and attitudes, Watson et al. (2007) found that some ride a motorcycle because of the thrill that it can evoke, including “testing one’s limits,” which frequently involves traveling at fast speeds. Consequently, motorcyclists do indeed travel at faster speeds than car drivers (Horswill & Helman, 2003), and crashes involving motorcycles tend to occur at higher speeds than those involving only cars (Carroll & Waller, 1980). Predictably, the safety effect of higher speeds in motorcycle crashes is more severe injuries and a greater likelihood of a fatality (Lin, Chang, Huang, et al., 2003; Lin, Chang, Pai, et al., 2003; Quddus et al., 2002; Savolainen & Mannering, 2007; Shankar & Mannering, 1996). The consumption of alcohol before riding is another risky behavior that has been identified. Studies in the United States and the United Kingdom show that alcoholrelated crashes more commonly involve motorcyclists than drivers of other vehicles (Bednar et al., 2000; Fell & Nash, 1989; NHTSA, 2010; Soderstrom, Dischinger, Ho, & Soderstrom, 1993; Subramanian, 2005). Whereas in Australia, several studies did not find an increased likelihood of impairment among motorcycle crashes (Diamantopoulou et al., 1995; Queensland Department of Transport, 2003), Haworth et al. (1997) did find that alcohol was involved in 26% of single-vehicle crashes. The effects alcohol has on riders is that it increases risktaking behavior (e.g., traveling at higher speeds) (Haworth et al., 1997; Soderstrom et al., 1993) and impairs basic handling skills (Colburn, Meyer, Wrigley, & Bradley, 1993; Creaser, Ward, Rakauskas, Shankwitz, & Boer, 2009).

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Consequently, drinking riders are more likely to be in crashes that involve running off the road, especially on curves, and a loss of control (Kasantikul & Ouellet, 2005; Ouellet & Kasantikul, 2006). Inattentiveness and slower response times are other effects alcohol has on rider performance (Creaser et al., 2009). The size of a motorcycle, in terms of engine capacity, is positively correlated with the likelihood of a crash and injury severity (Broughton, 1988; Lin, Chang, Huang, et al., 2003; Lin, Chang, Pai, et al., 2003; Quddus et al., 2002; Shankar & Verghese, 2006; Teoh & Campbell, 2010). Also, riders of motorcycles with large engine sizes are more likely to be at fault in an accident. Lynam, Broughton, Minton, and Tunbridge (2001) conclude that although riders of large motorcycles have more experience than others, they are more likely to travel at faster speeds and be at risk for loss of control. Crashes involving large motorcycles are most likely to occur on rural expressways and result from the capability to travel at faster speeds (Clarke et al., 2007). However, two literature reviews conclude that engine size/power does not increase risk of an accident (Mayhew & Simpson, 1989; TNO, 1997). Instead, it is the amount and the type of travel that increase crash risk. In comparison to smaller bikes, larger ones are more likely to be used by the recreational rider, who is likely to travel greater distances during a trip, and are more likely to travel on rural expressways that accommodate faster travel (Elliott et al., 2003). Instead of being an accident risk, it may be that the type of motorcycle reflects the riding style of the rider. Thus, riding style may be the true causal factor that explains the higher crash and casualty statistics associated with more powerful engines. As Teoh and Campbell (2010) stated, “Riders prone to higher risk driving behavior may choose more powerful and performanceoriented motorcycles” (p. 507). Previous research has found that supersport or racing design motorcycles have crash rates that are four times higher than that for touring motorcycles (Kraus, Arzemanian, Anderson, Harrington, & Zador, 1988). A similar finding was reported by Teoh and Campbell, who also reported that the supersport motorcycles are more likely to be ridden by younger riders more prone to risky behavior.

5. UNDERSTANDING RIDING BEHAVIOR Research on motorcyclist safety has largely focused on understanding the factors that contribute to crashes and the profile of individuals who are likely to be involved. Although this knowledge is useful for identifying behaviors that must be modified to enhance safety and the groups that are the most likely targets for these efforts, it does not provide insight to modify the unsafe, risky behavior. This point was made in reference to speeding, frequently

PART | IV

Vulnerable and Problem Road Users

identified as a contributing factor in crashes, but it applies to unsafe riding generally: “There is a notable dearth of studies that have sought to identify variables that both underpin motorcyclists’ speeding behavior and are potentially amenable to change via safety interventions” (Elliott, 2010, p. 718). Toward this end, recent research is devoted to identifying the psychosocial determinants of unsafe riding behaviors that increase crash risk. The importance of examining the determinants of riding behaviors is illustrated by research that has identified the various motivations individuals have for riding. Motorcycling is not just an instrumental activity. It also has a strong affective, expressive component that has implications for risky behavior. In reference to the relationship between an affinity for speed and committing behavioral errors, Sexton et al. (2004) write, “Such relationships lend support to the view that an important part of the motorcycle safety problem stems directly from the motivations that lead people to ride motorcycles in the first place” (p. 31). Early efforts to understand motorcyclist behavior sought to identify the motives that characterize riders. Based on 100 in-depth interviews with riders in Wales, Walters (1982; as cited in Sexton et al., 2004) classified riders into three categories: those riding for practical reasons (cost and convenience), riding enthusiasts (pleasure), and those who enjoy the excitement and freedom of motorcycling. Those who were motivated by practical reasons comprised 35% of the sample and tended to ride small bikes for short distances, often for commuting. Riding enthusiasts comprised 48% of the interviewees, tended to be younger, rode for work and pleasure, and were confident in their riding skills. The smallest group (10%)dthose motivated by excitement or to gain attentiondtended to be young, were overconfident in their skills, perceived themselves as “invincible,” and engaged in riding behavior that most others would regard as irresponsible. The remaining 7% could not be easily classified. A study of 376 German motorcyclists identified three general motivational categories: biking for pleasure, biking as a fast competitive sport, and control over the motorbike (Schulz, Gresch, & Kerwien, 1991). These categories differed in terms of rider age and bike type. Young riders were found to be more influenced by pleasure and thrill seeking. Riders of sports bikes were more influenced by motives relating to competition and exhibition. In addition, riding for pleasure was more likely to characterize riders of sports bikes and other specialized bikes (e.g., choppers). Christmas, Young, Cookson, and Cuerden (2009) administered surveys to 66 riders in the United Kingdom using 30 “motivations to ride” statements and reported two dimensions that distinguished respondents. The first dimension is passion, which had three levels (from high to low: disciples, hobbyists, and pragmatists), whereas the

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Motorcyclists

second is performance, which relates to the power of the bike and the challenge of riding (high and low). Based on these two dimensions, seven distinct segments of riders were identified: look-at-me enthusiasts, riding disciples, riding hobbyists, performance disciples, performance hobbyists, car aspirants, and car rejecters. These differing segments were found to correlate with scores for accident propensity. Performance disciples had the highest accident propensity score average and also traveled the most distance via motorcycles. The groups with the next highest accident propensity averages are car aspirants and look-atme enthusiasts. Thus, riders have different reasons for taking up motorcycling, and these motives appear to correlate with riding behavior. Recent research has sought to understand the psychosocial determinants of these motives and behaviors. Among the concepts that have been employed in these studies are intention, attitudes, norms, personality, and social influences. Some of the first research on this topic was published by Rutter and Quine (1996) and Rutter, Quine, and Chesham (1995), who conducted two surveys of riders in the United Kingdom. The first survey asked riders about their attitudes and behaviors, whereas the second survey queried the same respondents 1 year later about riding crashes and mishaps they experienced during the intervening period. Analysis of survey responses revealed four categories of riding behavior: breaking laws and rules, taking care, carelessness, and safety equipment and training. Of these behaviors, breaking the laws and rules of safe riding was found to be positively related to accident involvement. Elliott, Baughan, and Sexton (2007) created the 43-item Motorcycle Rider Behaviour Questionnaire (MRBQ) to measure the self-reported frequency of riding behaviors. The MRBQ was based on the Driver Behaviour Questionnaire developed by Reason, Manstead, Stradling, Baxter, and Campbell (1990), which identified types of driving behavior. Five categories of riding behavior were identified by Elliott, Baughan, et al.: traffic errors, control errors, speed violations, stunts, and safety equipment. Traffic errors were the most consistent predictor of crash involvement, likely reflecting the physical challenge of motorcycle riding, whereas speed violations were associated with involvement in a crash that a rider admitted to at least be partly at fault. Among the psychosocial determinants of risky riding behavior that have been considered in research are the attitudes, beliefs, intentions, and personality traits of riders. The most pronounced theoretical influence on this research comes from the theory of planned behavior developed by Ajzen (1991), which has been demonstrated to be useful for explaining risky driving behavior (e.g., drinking and driving and also speeding) (Armitage & Conner, 2001; Elliott & Armitage, 2009; Elliott, Armitage, et al., 2007;

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Elliott & Thomson, 2010; Lawton, Parker, Stradling, & Manstead, 1997; Manstead & Parker, 1995). The premise of the theory of planned behavior is that intentions are the best predictors of individual behavior. Intentions are formulated by a reasoned process whereby, either implicitly or explicitly, an individual considers the consequences of his or her actions and chooses the action that is most likely to generate a desired outcome. These intensions are themselves determined by an individual’s attitudes and subjective norms regarding the behavior. The attitudes represent the positive and negative assessments of a behavior that an individual possesses. Subjective norms represent the social pressure an individual feels and are determined by whether “important others” would approve or disapprove of the behavior. However, in some instances an individual may feel as though they have little volitional control over their actions. For example, a rider who wants to perform a “wheelie” may not possess the skill or may be limited by the bike’s mechanical condition (Watson et al. 2007). Thus, perceived behavioral control is a third determinant of intentions. In addition to influencing intentions, perceived behavioral control may also directly influence behavior. When an individual does not feel in complete volitional control, attitudes and subjective norms determine intentions along with perceived behavioral control. Conversely, in situations where an individual feels that they have control over what they do, attitudes and subjective norms alone will determine intentions. One of the earliest efforts to apply the theory of planned behavior to motorcyclists was by Rutter and Quine (1996) and Rutter et al. (1995). In addition to identifying that the involvement in a crash is related to traffic errors, they also examined the structural relationships among concepts at the base of an early version of the theory of planned behavior that Fishbein and Ajzen (1975) labeled the theory of reasoned action. Rutter et al. and Rutter and Quine found that attitudes toward obeying laws and rules, and attitudes toward taking care while riding, predicted the self-reported breaking of laws and rules of safe riding. Consistent with the theory of reasoned action, they concluded, “Beliefs about safe riding do predict riding behavior, which in turn predicts accident involvement, and that beliefs are best seen as mediators between demographic inputs, such as age and experience, and behavioral outcomes” (Rutter et al., 1995, p. 369). The theory of planned behavior has been applied to motorcycle riding in several other studies. One of these was based on a survey of 4929 motorcyclists in the United Kingdom (Jamson, Chorlton, & Conner, 2005). The questionnaire asked respondents about their intention to engage in seven risky riding behaviors: speeding, closely following another vehicle, “going for it” on a rural road, poor awareness in busy traffic, fast cornering, riding while over

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the legal alcohol limit, and riding fast to keep up with a group. It was found that attitudes toward a risky behavior were related to the intention of performing that behavior. To measure subjective norms, instead of asking about “important others” as is common in most studies of the theory of planned behavior, respondents were asked for their perceptions about how four specific groups would feel about them engaging in each risky behavior: police, other road users, family, and other bikers. Those not intending to perform the risky behaviors were likely to perceive more pressure from each of these groups. Also, the general measure of perceived behavioral control was found to be related to intentions to perform several of the risky behaviors examined. Thus, the study provides support for the use of the theory of planned behavior to study motorcyclist behavior. Watson et al. (2007) and Elliott (2010) provide two additional examples that invoke the theory of planned behavior to explain risky riding. In the study by Watson et al., surveys administered to 227 motorcycle riders in Australia were analyzed using hierarchical regression analysis. Key dependent variables were intentions to engage in three safe behaviors (handle the motorcycle skillfully, maintain 100% awareness, and avoid riding impaired) and three unsafe behaviors (bend road rules, push limits, and perform stunts and/or ride at extreme speeds), along with self-reported behaviors (handling errors, awareness errors, ride impaired, bend road rules, push limits, and stunts or speed). In general, it was found that attitudes had a significant influence on risky behavior intentions, whereas intentions to perform safe behaviors were more likely to be influenced by perceived behavioral control. In addition, self-reported behavior was consistent with intentions, confirming the thesis of the theory of planned behavior that other influences on behavior are mediated through intentions. Elliott (2010) sampled 110 riders from motorcycle clubs in Scotland who were asked about their intent to speed on roads with low and high speed limits. In general, the findings are consistent with the theory of planned behavior in that attitudes and perceived behavioral control were found to be important predictors of speeding intentions. However, several modifications and extensions to the theory of planned behavior are suggested by these and other studies. For instance, Elliott (2010) draws a distinction between instrumental and affective attitudes. Instrumental attitudes represent cognitive evaluations about the benefits of performing a behavior, whereas affective attitudes are an emotional evaluation. Affective attitudes are strong predictors of intention across most social behaviors (Trafimow et al., 2004), and motorcycle riding is recognized as having a strong affective motivational component (Christmas et al., 2009). Previous research on risky motorcycle riding has not

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Vulnerable and Problem Road Users

drawn this distinction. For instance, Watson et al. (2007) measured only instrumental attitudes, whereas Jamson et al. (2005) used a mixed measure that combined instrumental and affective attitudes into one scale that they found to be positively correlated with risky riding behavior. In contrast, Elliott (2007) used a specific measure of affective attitudes and reported it to be significantly correlated with motorcyclist intentions to speed on roads with either low or high speed limits. In terms of its application to explain safety behavior, at times the concept subjective norm has not had the predictive power that is expected according to the theory of planned behavior. It is speculated that this is because the concept is understood as pertaining to “what important others would think” (Elliott, 2010; Watson et al., 2007). Given the social nature of motorcycle riding, it may be that the norms that matter are those that are set by the group in which the riding takes place. The opinions of parents, spouses, and significant others in a rider’s life may matter less than what the other riders one rides with think about certain behaviors. When operationalized in this way, subjective norms have been more useful for predicting intentions (Watson et al., 2007). In addition to the subjective norms hypothesized to affect intentions, Watson et al. (2007) included a measure of personal norms. Drawing upon the study of traffic violations by Parker, Manstead, and Stradling (1995), who contended that internalized moral norms are useful for explaining deviant behavior, Watson et al. hypothesized that personal norms affect risky behavior in addition to the effect of socially derived norms. However, the personal norms construct proved difficult to measure and consequently was not included in the final analysis (Watson et al., 2007). A last suggested extension of the theory of planned behavior is the inclusion of additional concepts to represent the weight of social influence on intentions. Although subjective norms tap the influence of groups, Elliott (2010) contends that Ajzen’s theory of planned behavior does not go far enough to represent the importance of the social context of motorcycle riding. For this reason, Elliott includes self-identity and social identity constructs. Selfidentity refers to an individual’s self-concept that is defined in terms of the societal roles with which an individual identifies and associated role-appropriate behavior. In contrast, social identity theory highlights the role of group memberships and explains that social identities stem from the social groups to which individuals belong or with which they identify. Because of this group identity, an individual is likely to align behavior so that it is consistent with the norms that characterize a salient group. Elliott reports that social identity has a significant influence on speeding intentions for individuals with strong group identifications. However, Watson et al.

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(2007) did not find self-identification as a “risky rider” to be an important influence on intentions. Beyond being the result of planned or intentional behavior, personality traits have long been thought to influence a rider’s tendency to engage in risky behavior that may lead to a crash. This approach builds off the work of Zuckerman (1994), who regarded sensation seeking as a biological trait that predisposes one to either underestimate risk or regard risk as merely the cost that must be paid to enjoy a thrilling experience. A correlation between a sensation-seeking trait and risky driving behavior is wellestablished (Jonah 1997a, 1997b). However, sensation seeking alone is unlikely to distinguish risky riders from safer ones because the population of motorcyclists is regarded as being higher in sensation seeking compared to the nonriding population. Thus, sensation seeking in combination with high aggression is likely to be more predictive of risky riding behavior (Watson et al., 2007; Zuckerman, 1994). In addition, Ulleberg (2001) contends that high-risk populations are characterized by anger, normlessness, and sensation seekingdtraits frequently found to be related to crash involvement (Ulleberg & Rundmo, 2003). The causal relationship is likely to be such that personality traits influence risky driving behavior through individual attitudes about unsafe behavior (Ulleberg & Rundmo, 2003). As hypothesized, Watson et al. (2007) found both sensation seeking and a propensity for aggressive behavior to be significant predictors of self-reported risky riding behaviors. A measure of risk taking was also included in a study by Haque, Chin, and Lim (2010), who found both impulsive sensation seeking and aggression to be correlated with having been involved in a motorcycle crash. Defining personality types based on impulsive sensation seeking and aggression, Haque, Chin, and Lim concluded that “extrovert” and “follower” personality types of motorcyclists are more prone to crashes. The association of personality traits with both attitudes and behavior was examined in a study of 257 students at a Taiwanese university (Chen, 2009). Attitudes toward unsafe riding were found to directly influence self-reported risky riding behavior (speeding, rule violations, and selfassertiveness). It was reported that the role of personality traits on behavior is indirect because they are mediated by attitudes. Specifically, the traits of anger, sensation seeking, and normlessness are positively associated with risk-taking riding attitudes, whereas anxiety is negatively associated. Of these traits, normlessness had the largest influence on attitudes. Although not addressed by the author, the influence of normlessness on attitudes about risky riding supports the need to consider personal norms for understanding unsafe behavior, as suggested by Watson et al. (2007). A last finding from the study by Chen is that altruism (i.e., a concern for others) was the only personality

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trait that had a direct influence on behavior, indicating that a being predisposed to think of others diminishes the likelihood of risky riding. Similarly, a study of young Taiwanese motorcyclists by Wong, Chung, and Huang (2010) concluded that personality traits (sensation seeking, amiability, and impatience) only indirectly influence self-reported risky riding behavior (fast riding and riding violation) because personality traits are mediated through affective risk perception and utility perception. In addition, personality traits have direct effects on riding confidence, which indirectly affects behavior through attitudes toward unsafe riding and being aware of traffic conditions. This latter finding suggests that confident riders are more likely to perform risky riding behavior but are attentive to the hazards involved. These results are consistent with the theory of planned behavior in that attitudes toward unsafe riding are related to self-reported risky behavior. The constructs of utility perception and affective risk perception suggest that a distinction between instrumental and affective attitudes is useful, and planned control behavior (similar to Wong et al.’s riding confidence construct) is also relevant for risky riding behavior. In summary, transport psychology research devoted to understanding risky motorcycle riding is in a nascent stage. Although considerable attention has been devoted to risky driving behavior, relatively little research has considered explaining the risky behavior of motorcyclists. Based on the research that has been conducted, it is clear that a variety of motives attract motorcyclists, including affective ones. The theory of planned behavior provides a promising theoretical framework to direct future research. Coupled with an understanding of personality traits, the theory of planned behavior may provide a sound understanding of unsafe motorcycle riding and inform the development of interventions that will create safer behavior.

6. CONCLUSION Motorcyclists are the most vulnerable motorized vehicle users on the road. At a time when safety is improving for other road users, motorcyclists are not enjoying the same gains in safety. The motorcyclist safety problem is even more serious in developing nations, where these vehicles are more important for commuting and for commerce. Substantial gains in global traffic safety will require reducing the risk that motorcyclists face. Early periods of motorcycle safety research focused on crash analysis and the technical facets of motorcycle travel. As a result, much has been learned about factors that contribute to crashes and the characteristics of the individuals and behaviors involved. This knowledge has suggested the need to engineer safer motorcycles, to redesign roads that more safely accommodate riders alongside other

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vehicles, and to design safety equipment (e.g., helmets and protective clothing) to protect riders involved in a crash. The emphasis of this most recent period of research has been to understand why motorcyclists engage in risky behaviors. Toward this end, the field of transport psychology has begun to identify the psychosocial determinants of unsafe riding behavior. The theory of planned behavior has served as a useful theoretical framework to guide this research. Findings suggest that attitudes toward unsafe riding influence an individual’s intention to perform risky behaviors that lead to an increased risk of a crash. Also, relevant attitudes are in part determined by personality traits. In terms of specific risky behavior, much of the attention has been paid to speeding. Drinking and riding and forgoing the use of a helmet are two other risky riding behaviors that are worthy of study from this perspective. Several implications for safety interventions can be drawn from the extant transport psychology literature. First, attitudes toward risky riding are likely the key to modifying behavior, meaning that interventions should focus on altering personal and social norms or encouraging the development of alternative group identities that value less aggressive riding styles (Chen, 2009; Elliott et al., 2007). Second, rider training programs not only need to focus on rider skill and road rule knowledge but also should address the attitudinal and motivational influences on rider behavior to encourage greater personal control (Watson et al., 2007). Third, motorcyclists are a heterogeneous population. Different riders pose different hazards, requiring interventions to be tailored to the target group and behavior (Wong et al., 2010). In addition to the study of unsafe or risky riding behavior, rider/driver performance is in need of attention by transport psychology. Only recently has research begun to shed light on the way in which riders process information when faced with potential hazards (Hosking, Liu, & Bayly, 2010; Liu, Hosking, & Lenne, 2009). Given that motorcycle riding is such a challenging activity that exposes the rider to a wide variety of dangers, a greater understanding of hazard detection and response is needed to assist motorcyclists in developing these skills. Less obvious is the importance of research to study driver performance. Motorcyclists are more often the victim in a crash, and “looked but failed to see” errors committed by the driver of a motor vehicle are a common cause. An engineering approach has dominated efforts to reduce these errors by attempting to increase the visibility of motorcycles by making bikes more readily stand out from the background. This approach treats conspicuity as a trait inherent in the motorcycle and the rider. Although some gains in rider conspicuity have been achieved through motorcycle design (e.g., daytime running lights and vehicle and rider apparel color and reflectivity), physical

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conspicuity aids have not solved the problem posed by “looked but failed to see” errors. Wulf, Hancock, and Rahimi (1989) distinguished between “sensory conspicuity” and “cognitive conspicuity,” but it is the former that has characterized most research on this issue and encouraged an engineering perspective. Studying cognitive conspicuity entails thinking of the automobile driver as an information processor that is central to these errors and necessitates a perspective rooted in transport psychology. Langham (1999) provides one example of the type of research needed to address “looked but failed to see” errors. Given the role that behavioral factors play in motorcyclist crashes, transport psychology has much to contribute for improving rider safety. The increased attention that motorcyclist safety has received in recent years is timely given trends in both developed and developing nations. However, much more needs to be done to understand rider behavior and rider/driver performance to sufficiently inform interventions and strategies that will make riders on the road safer.

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Chapter 28

Professional Drivers Tova Rosenbloom Bar-Ilan University, Ramat Gan, Israel

1. INTRODUCTION Truck drivers throughout the world have a particular demographic profile. They are involved in a disproportionately high number of road crashes. Other drivers besides truck drivers are responsible for most of these accidents. In crashes in which truck drivers are responsible, the crashes are most often triggered by an error in operating the vehicle associated with the vehicle’s physical and operational characteristics, such as size, weight, braking distance, blind spots, and turning radii, or they are due to errors in driver perception, anticipation, or estimation, driving at a nonadaptive speed, or due to driver fatigue and sleep deprivation. Buses are one of the most popular modes of public transport worldwide. Bus drivers are much less involved in crashes than are truck drivers. The main cause of bus crashes is human error committed by bus drivers or by others. Driver age, driving experience, previous accidents and their severity, work conditions, the type of bus (public light bus/charter bus/school bus/minibus), and route are correlated with the risk of being involved in another accident. The bus driving profession is one of the worst professions with regard to poor health, high labor turnover, and early retirement. Bus drivers suffer from cardiovascular disease, gastrointestinal disorders, musculoskeletal problems, psychosomatic disorders, and fatigue. Taxi driving is one of the most dangerous driving occupations because of the many risks involved, including physical, environmental, and health-related risks. Taxi drivers are victims of nonsexual robbery at a rate higher than that of the average community. Aspects of driving alone and while transporting passengers, working day/night shifts, familiarity with the area, seat belt use, and antilock brake (ABS) systems are discussed in this chapter. Truck drivers, taxi drivers, bus drivers, and other drivers whose profession is to drive in a vehicle for working purposes differ from other road users due to the very fact that they drive for a living. This chapter presents the unique characteristics of each category of professional drivers, data about their involvement in crashes, typical Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10028-1 Copyright Ó 2011 Elsevier Inc. All rights reserved.

physiological and psychological features (cognitive, personality, and social), and the dynamic between these drivers and other road users. In the literature of work-related versus non-workrelated road accidents (Charbotel, Martin, & Chiron, 2010), evidence indicates that accidents that occur while at work have lower fatality rates (1.4% among women and 3.4% among men in 2003e2006) than accidents that occur elsewhere. In the case of accidents while at work, professional drivers have a highest risk compared to other drivers. Professional drivers differ from nonprofessional drivers in many respects; for example, they have higher annual mileage, longer working hours, more demanding driving tasks, and differ with regard to their commitment to companies, culture and policy, and other organizational factors. These factors lead to the assumption that professional drivers are at a high risk of being involved in road accidents (Dorn & Brown, 2003). Rosenbloom and Shahar (2007) assert that professional ¨ z, O ¨ zkan, and drivers differ in the level of risky driving. O Lajunen (2010) indicated that professional drivers drove slower on both city roads and highways compared to nonprofessional drivers. This may be due to traffic rules/ regulations and the roles of the companies/organizations that employ professional drivers (Caird & Kline, 2004). Professional drivers travel at slower speeds than other drivers, especially due to the requirements of their job (e.g., some stop frequently to allow passengers to disembark). Minibus drivers tend to be more aggressive than private drivers. They are usually exposed to more difficulties and stress caused by traffic. Due to the intensive exposure to risky situations on the road, they get used to risks in traffic and perceive certain traffic situations as less risky. Thus, professional drivers get “desensitized” to traffic hazards. As a consequence, the frequency of speeding of taxi and ¨ z et al., 2010). minibus drivers on highways increases (O Likewise, professional drivers are more exposed to traffic for long hours, which may make them more prone to fatigue (Matthews, Tsuda, Xin, & Ozeki, 1999). It has also been found that aggressive drivers choose higher speeds on 389

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city roads and are involved in a higher number of accidents than nonaggressive drivers. Matthews et al. found that professional drivers with high thrill-seeking scores drove slower on city roads, and that the drivers with high hazard monitoring scores were involved in a higher number of accidents. Rosenbloom (2000) found that the correlation between leniency in driving (having short headways or unsafe merging into an occupied road) and two measures of sensation seeking (Thrill and Adventure (TAS) and Boredom Susceptibility (BS)) demonstrate opposite trends in terms of a shift from age 40 to 50 years. For TAS, this correlation decreased from 0.66 to 0.21, whereas for BS it increased from 0.23 to 0.62. The impact of TAS on risk taking in driving is apparently much stronger until the age of 45 years. Later, after many years of professional driving, drivers seem to develop higher mastery of both vehicle and road use and therefore allow themselves to take more risk, independent of their personality inclinations. Boredom susceptibility, however, differentiates successfully between high and low risk takers, even among the professionals. The following sections discuss specific groups of professional driversdtruck drivers, bus drivers, and taxi drivers.

2. TRUCK DRIVERS Trucking is regarded as the most successful mode of freight transportation in the United States and in other countries (American Trucking Associations, 2009). In many countries, the trucking industry is a major component of the economy. Truck drivers throughout the world have a particular set of demographics, skill base, and possibly also different attitudes than car drivers. Their mean age is 46.8 years (SD ¼ 9.4 years), mean annual truck mileage is 49,524 miles (SD ¼ 39,092 miles), and mean truck driving experience is 19.95 years (SD ¼ 11.58 years). Furthermore, they engage in driving for a different purpose and spend more time on the road than the general public (Poulter, Chapman, Bibby, Clarke, & Crundall, 2008). In many countries, professional truck drivers are mostly male, and they have gone through some selective processes and have had to attain certain physical, psychological, and educational standards (National Highway Transportation Safety Administration (NHTSA), 2005a). Truck drivers are more practiced at driving and have had more training than the vast majority of car drivers (Sullman, Meadows, & Pajo, 2002). Health problems are quite frequent among truck drivers, apparently due to the special working conditions. Ha¨kka¨nen and Summala (2001) presented some typical health problems of truck drivers, such as cigarette smoking, being overweight, and having high blood pressure. They reported that diabetic truck drivers were involved in more accidents than drivers in good health.

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Dinges and Maislin (2006) reported that half of drivers had a body mass index in the obese range, which is nearly double the prevalence of obesity in the general population, including males aged 45e64 years (26.6% obese). There is evidence that truck drivers are prone to higher rates of road crashes. Approximately 4,700 people were killed each year between 1992 and 2002 in the United States in accidents involving large trucks, of which 85% were the occupants of nontruck vehicles (Federal Motor Carrier Safety Administration, 2005). The NHTSA (2005b) reports that occupational vehicular accidents involving truck drivers are considered a serious work safety problem. It indicates that truck driving is among the occupations with the highest risk for fatal injuries. This report includes information about the gap between the proportional share of large trucks of the total registered vehicles in the United States (4%) and their involvement in fatal crashes (8%). Sullman et al. (2002) reported that a truck driver has one crash every 4 years (M ¼ 0.56, SD ¼ 0.96 biennially). A total of 33.9% of truck drivers reported having a crash in the past 2 years, with 52.6% of crash-involved drivers partly or totally to blame for the crash (Ha¨kka¨nen & Summala, 2001). Hanowski, Hickman, Wierwille, and Keisler (2007) assert that in the United States, as well as in other countries, there are 7e10 times more crashes associated with trucks than with private vehicles. In the category of heavy trucks, which weigh more than 34 tons, the data show 30 times more crashes than with private vehicles (Haworth & Symmons, 2003; Israeli National Authority of Road Safety, 2008; Traffic Injury Research Foundation, 2009). Large trucks are overrepresented in terms of both the number of fatal accidents with passenger vehicles and the number of fatal accidents with other trucks (Khorashadi, Niemeier, Shankar, & Mannering, 2005). In their survey on occupational vehicular accident claims of Oregon truck drivers in the years 1990e1997, McCall and Horwitz (2005) found that trucker injuries due to vehicular accidents occurred disproportionately among young workers and female truck drivers. The number of lost workdays and total claim costs associated with the injury claims were higher for male truck drivers than for female truck drivers. Thus, although female truck drivers were more likely than males to be injured in a vehicular accident, injury severity was greater for males. Furthermore, younger truck drivers involved in accidents took less time to recover from their injuries and returned to work sooner than older truck drivers. Regarding the age of risky truck drivers, Duke, Guest, and Boggess (2010) reported higher involvement of both younger and older heavy-vehicle drivers in road accidents (a characteristic U-shaped curve). Furthermore, injury claim rates were lowest during the morning hours, and the least severe injuries (as measured

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by claim costs) occurred during the evening. Injuries occurring on Friday were the most severe. Regarding injury types, McCall and Horwitz (2005) reported that sprains were the most frequently experienced injury type claimed, comprising more than half of all claims. Of all nonfatal injuries, fractures were the most severe, requiring almost 8 weeks of indemnity per claim. Fatalities constituted 1.6% of all accident claims, and most were not the result of collision with another vehicle. Five fatalities occurred because the truck overturned or jackknifed, five occurred because the truck ran off the road, and one occurred because the truck struck a stationary object. There is evidence that heavy-vehicle drivers exceed speed limits less often, and by smaller margins, than drivers of light vehicles, and that truck drivers involved in crashes are less likely than passenger vehicle drivers to drive under the influence of alcohol (Tardif, 2003). Craft and Blower (2004) reviewed 287 two-vehicle crashes for the Federal Motor Carrier Safety Administration (FMCSA)/NHTSA Large Truck Crash Causation Study. The “critical reason” for crashes between trucks and light vehicles was attributed to the other vehicle or driver in 70% of the cases and to the truck or truck driver in 30% of the cases (Thieriez, Radja, & Toth, 2002). According to Summala and Mikkola (1994), trailer truck crashes often occur between two vehicles driving the opposite way. Only 17% of the truck drivers involved in these accidents are considered to be principally responsible. The reliability of the assessment regarding responsibility is questionable because often only the truck driver is the one who survives to tell his or her story of what happened. In cases in which the trailer truck driver was principally responsible for the accident, the crash was most often triggered due to an error in operating the vehicle or in the driver’s perception, anticipation, or estimation. Unfortunately, even the current data, based on in-depth analysis of the accidents, cannot provide more definitive information regarding the causal factors. Horne (1992) asserts that some of the accidents involving inattention or wrong anticipation might be related to driver fatigue, and that accident investigators should further inquire why such a cognitive error occurred. Hanowski, Keisler, and Wierwille (2004) indicated that in light vehicleeheavy vehicle (LVeHV) interactions, LV drivers are more likely to be responsible for the LVeHV interaction than are HV drivers. Contrary to this approach, Council, Harkey, Nabors, Khattak, and Mohamedshah (2003) asserted that truck drivers were found to be at fault more often than car drivers (48% compared to 40.2%, respectively) in truckecar accidents. It is worth noting that the majority of truck-fault accidents were either less severe rear-end crashes or accidents involving the trucks’ dead zones, whereas car drivers were at fault in the majority (71.2%) of the deadly head-on crashes. In fact, there is

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a small high-risk subgroup responsible for the majority of drowsy episodes, with a similar subgroup in the long-haul drivers’ population (et al., 2004). Hanowski et al. (2004) noted four key factors that typified the interaction between light and heavy vehicles. First, most of the crashes were the fault of light vehicles’ drivers. Second, crashes between light and heavy vehicles were typified by different kinds of frequent incidents. For LV driver at-fault incidents, the most frequent incident types were late braking for stopped/stopping traffic (41.3%), lane change without sufficient gap (21.7%), and aborted lane change (8%). In contrast, the most frequent incident types for HV at-fault drivers were lane change without sufficient gap (26.6%), lateral deviation of through vehicle (21.5%), and left turn without clearance (13.9%). Third, the most prevalent primary maneuvers for LV driver at-fault incidents were decelerating or stopped, whereas for HV drivers at-fault incidents were changing lanes and crossing the lane line. Fourth, 55.1% of the LV driver at-fault incidents involved a rear-end approach, whereas 41.8% of the HV driver at-fault incidents involved a sideswipe angle. In addition to truck drivers’ fatigue and aberrant behaviors, their attitudes toward safe driving were found to be related to their involvement in crashes. Truck drivers are generally aware of the size and weight of their vehicles and of the enormous potential damage to life and property they may cause. Moreover, they are well trained to avoid dangerous situations (Israeli Road Safety Authority, 2006). Ajzen’s (1985, 1988) theory of planned behavior (TPB) has been used to predict drivers’ crash involvement (Poulter et al., 2008). According to the theory, the best predictor of a person’s behavior is his or her intention to perform the behavior. Three factors determine these behavioral intentions: attitudes, subjective norms, and perceived control (Ajzen, 1991). In the context of truck drivers, Newnam, Watson, and Murray (2004) found that drivers had a lower intention to speed in work vehicles than in their own personal vehicles. Poulter et al. (2008) found that a stronger perception that other drivers would expect them to obey driving laws directly led drivers to be more obedient to driving laws. Regarding perceived behavioral control, they found that the easier the truck driver found it to obey driving laws, the more likely he or she was to follow them. As would be expected from the TPB perspective, subjective norm, attitude, and perceived behavior control were all found to relate positively to intention. Overall, the model accounted for 28% of the variability in self-reported driving behavior. Poulter et al. (2008) also found that the more control a driver has over his or her work, the more likely his large goods vehicle will be compliant with vehicle traffic laws. Unlike driving behavior, there is no direct effect of subjective norms on compliance behavior. Intention has

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a smaller direct effect on compliance behavior. The greater the intention to comply with driving regulations, the more likely the driver is to report compliance. Attitudes have a small but significant direct effect on compliance behavior. Therefore, the better a driver’s attitude is to ensuring his or her vehicle is compliant, the more likely his LGV will be compliant with the road traffic laws. Rosenbloom, Shahar, and Eldror (2009) found differences between attitudes toward reckless driving among truck driver types; tip truck (a truck whose contents can be emptied without handling) drivers reported more cautious driving approaches than did concrete mixer truck drivers. This finding may be related either to the different characteristics of the truck (either tip or mixer) or to the sample of the truck drivers in the study, in which tip truck drivers enjoyed steady work conditions, whereas concrete mixer truck drivers worked as freelancers. Thus, explanations for the differences that were found between the two groups of truck drivers may come from the organization’s safety climate (Zohar, 1980) related to the type of truck organization. Safety climate is defined by Zohar (1980, p. 96) as “a set of molar perceptions, shared by individuals with their work environment, which are valid as references for guiding behavior in the execution of tasks during day-to-day eventualities.” Companies in which higher management is commitment to safety have many fewer accidents (Diaz & Cabrera, 1997). Because tip truck drivers usually enjoy steady work conditions, they may feel more committed to their workplace and display greater compliance with safety regulations compared to the less committed concrete mixer truck drivers. Alternatively, the characteristic irregular working shifts might affect concrete mixer truck drivers, leading to fatigue and resulting in carelessness and greater risk taking. Trucks contribute disproportionately to traffic congestion, infrastructure deterioration, and crashes due to their physical and operational characteristics, such as size, weight, braking distance, blind spots, turning radii, and driver fatigue (Peeta, Zhang, & Zhou, 2005). Poulter et al. (2008) asserted that trucks are more likely to be involved in a crash that results in a fatality due to the weight and relative size of the vehicle compared to those of other road users, as well as increased length of stopping distances. They based their information on a review of the literature and detailed pilot work with the Vehicle and Operator Services Agency (VOSA), the regulatory body for the UK truck industry, and with UK truck operators. They demonstrated that driving violations are significantly correlated with crash involvement in truck drivers. Specifically, reckless driving and improper turns were violations associated with the highest increase in crash likelihood (325 and 105%, respectively), and improper/ erratic lane changes and failures to yield right-of-way were

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the convictions associated with the greatest increase in crash likelihood (100 and 97%, respectively). Furthermore, noncompliance with statutory regulations has been one of the central issues concerning the involvement of truck drivers in accidents. In 465,000 roadworthiness tests conducted on trucks in the United Kingdom in 2006 and 2007, 22.1% failed (VOSA, 2007). The main offenses were driving hours and tachograph-related, followed by overloading and driver and operator license violations, among others (VOSA, 2007). Khorashadi et al. (2005) described various factors that are known to affect accident frequency and severity, including driver and vehicle characteristics, roadway geometrics, traffic conditions, environmental conditions, and vehicle condition. The European Truck Accident Causation (ETAC) study by the International Road Transport Union (IRU, 2007) presents in detail the frequent circumstances involving truck accidents, including intersections, queues (collision with a vehicle in the same direction), lane departure (including U-turns), overtaking, and single-truck accidents. When pedestrians were involved in the accident (6.2% of total cases), in most of cases there was a dominant cause of blind-spot mirror, which did not allow the drivers to have a full view. Most of the accidents between trucks and other vehicles or pedestrians are caused by nonadaptive speed, failure to observe intersection rules, and changing lanes with improper maneuvers. Single-truck accidents occur mainly because of nonadapted speed, overfatigue or falling asleep during traveling, and loss of road friction. When analyzing the accidents in the ETAC study (IRU, 2007), it is obvious that immediately before accidents occurred, truck drivers were usually not driving in a straight line. Rather, they were changing direction or negotiating a bend (particularly in roundabouts and on- and off-ramps). To better understand the factors that contribute to the severity of road accidents of trucks, it is necessary to make a distinction between accidents in rural versus urban areas (Khorashadi et al., 2005). Rural and urban areas differ from each other with regard to driver behavior, characteristics of driver populations, and the effect of driver behavior as a function of the visual “noise” (Lee & Mannering, 2005). Horrey and Wickens (2003) found that the perceptual, cognitive, and response demands placed on drivers are significantly higher in urban compared to rural areas. In a rural area (especially in intersections), accidents involving trucks result in a 725% increase in the likelihood of severe/fatal injury (compared to all other highway locations), whereas such accidents in urban areas result in a 10.3% decrease in the likelihood of a severe/fatal injury (Khorashadi et al., 2005). The severity of accidents in urban and rural areas also depends on the time when they occur. Approximately 40% of accidents that happen in the early hours of the morning in urban places are severe, whereas

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only 4% of accidents that occur during the same hours in rural places are severe. Furthermore, driving under the influence of alcohol in urban areas is four times more dangerous than in rural areas (Khorashadi et al., 2005). Khorashadi et al.’s (2005) conclusion from the analysis of the differences between rural and urban accidents is that complex interactions between driver behavior and measurable factors such as geometrics and environmental conditions play a significant role in driver injury severity. The effect of differences in driver demands, coupled with different traffic conditions, roadway design characteristics, and driver populations between rural and urban locations, can be expected to impact the severity of vehicular accidents (Khorashadi et al., 2005). In addition, it is well worth noting some of the most prevalent issues regarding truck drivers’ behavior, namely violations, errors, and driving in a state of fatigue. Sullman, Gras, Cunill, Planes, and Font-Mayolas (2007) reported that the most aberrant driving behavior of truck drivers leading to involvement in accidents was speeding. In their study, driving while over the legal alcohol limit was only the fourth least common type of aberrant driving behavior. In addition, safety belt use by truck drivers is much lower than that of the general population of drivers, as reported in 2003 by the National Occupant Protection Use Survey. NHTSA (2005b) reported 63% usage of safety belts by truck drivers versus 85% usage in the total population. Sullman et al. also reported that the most frequent aggressive driving violation by truck drivers was expressing hostility (e.g., making an obscene gesture toward a driver and shouting and beeping). The two errors reported most frequently by Sullman et al. were “hit something when reversing that you had not previously seen” (due to the larger blind spots of truck drivers) and “nearly hit a cyclist coming up on turning left.” Morad et al. (2009), as well as many other researchers, assert that fatigue and sleep deprivation are among the major problems in occupational activities. Truck driving can involve late-evening and night shifts, which require vigilance and attention over an extended period of time. Excessive daytime sleepiness, reduced number of sleep hours, shift work, excessive driving time, and use of alcohol and other drugs are predictive factors of automotive accidents. Furthermore, failure to comply with regulations (e.g., sleeping time regulations) is correlated with a high accident record. For example, in the United States, the FMCSA developed regulations preventing drivers from driving excessive hours without appropriate rest (FMCSA, 1992). Gander, Marshall, James, and Le Quesne (2006) found that the role of fatigue must be considered as an integral part of accident analysis. They also asserted that many truck drivers involved in crashes are working with at least some degree of acute and chronic sleep restriction.

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According to Souza, Paiva, and Reima˜o (2005), sleep is an active, cyclic biological phenomenon vital for life. One of the most frequent sleep disorders is excessive daytime sleepiness, which affects 4e12% of the general population and approximately 50% of truck drivers. People with this syndrome are typified as having stress, decreased vitality, and increased risk of accidents. The IRU (2007) studied this phenomenon further. In cases in which fatigue played a role in the accident, 68% involved a truck and another vehicle (car, bicycle, or motorcycle), and in 29% of the cases the accident was a single-truck accident. Most accidents happen between 2:00 and 2:59 a.m., probably the time when the driver’s biorhythm is at a low point, and from 3:00 to 3:59 p.m., when it is nearly the end of the working day, another low point. Most of the fatigue-related accidents happen on highways or on interurban roads and much less in cities. Dinges and Maislin (2006) found that relative to nonmillion milers (less safe drivers), million miler status (the safer drivers) was associated with more years of driving experience, less variable work schedules, less night driving, earlier driving breaks, less smoking, and less caffeine and cell phone/CB use to manage fatigue while driving. Their study highlights the importance of regular work schedules whenever possible, reduced night driving, encouraged first rest breaks sooner when driving, and reduced smoking. Regarding caffeine and cell phone usage, it is difficult to determine whether their use should be reduced because these factors were reported by drivers as coping techniques for dealing with fatigue while driving. However, cell phone/CB use could also be an added distraction for drivers. Rosenbloom et al. (2009) found significantly higher reckless driving approach scores for nonprofessional passenger vehicle drivers compared to truck drivers, and this supports the hypothesis that truck drivers (in general) report more cautious driving and that nonprofessional passenger vehicle drivers report a more permissive approach toward reckless driving. This finding supports the notion that the training, expertise, and experience of professional drivers (Glendon, 2005), such as truck drivers, underlies the differences between their views toward reckless driving compared with those of nonprofessional drivers. However, several findings indicate that being a professional driver does not necessarily imbue safe driving behaviors or safe attitudes toward reckless driving. Other professional driver groups, such as taxi drivers, display permissive reckless driving compared to nonprofessional automobile drivers (Dalziel & Soames Job, 1997; Rosenbloom & Shahar, 2007). These findings suggest that truck drivers are more positive regarding safety compared to other professional drivers, possibly due to their awareness of the deadly implications of their involvement in traffic crashes.

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Rosenbloom et al. (2009) recommend that driver training for both truck drivers and private car drivers should include references to the special on-road dynamics, with an emphasis that truck drivers must cope with other road users and attempt to better understand and anticipate those other users’ on-road decisions. At the same time, private car drivers must also take into consideration the sudden appearance of a truck and leave safety margins in order to prevent crashes. Rosenbloom et al. also recommend the use of advanced technology such as automatic evasive carfollowing actions or other actions to make it more difficult for truck drivers to tailgate, run red lights, speed, or look away from the roadway for more than a critical time period.

3. BUS DRIVERS Bus drivers are another important group of professional drivers. Their three primary tasksdto drive safely, to maintain the schedule, and to serve the public in a professional and courteous mannerdare heavy psychosocial demands (Tse, Flin, & Mearns, 2006). Evans (1994) asserts that the bus is one of the most popular modes of public transport worldwide, and that there is a strong likelihood of this transport enduring for the foreseeable future. According to the U.S. Department of Transportation (DOT, 2009), buses are much less involved in crashes than trucks. In 2009, 104,631 large trucks compared with 12,537 buses were involved in nonfatal crashes, 41,634 large trucks compared with 6,636 buses were involved in injury crashes, there were 57,695 injuries in crashes involving large trucks compared with 14,739 injuries in crashes involving buses, 62,997 large trucks compared with 5,901 buses were involved in two-away crashes, and 2,412 large trucks compared with 13 buses were involved in HazMat placard crashes. An in-depth examination of bus crashes, although with a small sample, was conducted by the DOT (2009). This survey (“The Bus Crash Causation Study Report to Congress”) included information on 40 buses involved in 39 fatal and severe injury crashes in New Jersey in 2005 and 2006. The results showed that in crashes in which the bus was the cause of the crash, in most cases it occurred because of human behavior, namely errors committed by the bus driver. In the cases in which the non-bus factors were responsible for the crash, human error was the most prevalent reason as well. Bus drivers’ errors mostly included inattention, distraction, haste, and misjudgments, not necessarily violations of laws or regulations. Other behaviors were chargeable offenses such as making illegal maneuvers and tailgating. Age and driving experience of bus drivers have been found to be negatively correlated with crash involvement (Greiner, Ragland, Krause, & Syme, 1997). The demands of work and work conditions (e.g., scheduling, time

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pressure, dealing with passengers, lack of breaks, and shift rotations) influence the involvement in road accidents as well (Dorn, 2003). Hamed, Jaradat, and Easa (1998) found that demographic variables such as family status, driving habits, and accident history could predict the occurrence of the next accident. Higher accident rates were associated with drivers who were unmarried, took too few rest breaks, and had short time intervals since previous accidents. Lower accident rates were associated with drivers who had long busdriving and private vehicle-driving experience. The results indicate that the longer a minibus (that has seating capacity of 8e30 passengers) driver goes without an accident, the less likely it is that he or she will have an accident. The results also indicate that previous accident type affects the risk of being involved in another accident. Greater accident severity was associated with single-vehicle accidents, rural intercity routes, and speeding. Accident severity decreased and the time between two accidents increased when the previous accident was severe. In 2000, the DOT initiated a survey called Buses Involved in Fatal Accidents (BIFA) by Jarossi (2005). The survey was based on a distinction among five different carrier types, namely school, transit, intercity, charter/tour, and “other” bus operators. Transit and school buses operate typically on predictable, regular schedules and usually in urban areas on low-speed roads. Intercity buses have longer hauls and travel more on roads that may have lower traffic density but higher speeds. Charter/tour buses may also have long hauls but less predictable schedules. School buses have a higher proportion of female drivers, whereas the collection of “other” buses may include very inexperienced drivers whose main occupation is something other than driving. While testing the contribution of the variables related to the driver, such as errors while driving and previous driving record, it was found that school bus drivers had the best driving records and were coded with relatively few driving errors in the crash, compared with the other bus carrier types. Drivers with a record of driving violations or who had been involved in a crash were more likely to contribute to the current crash than were other drivers. Furthermore, both intercity and charter/tour bus drivers had much higher percentages of traffic violations than school bus drivers on most of the measures. Almost half of charter bus drivers had a conviction, suspension, or crash in the 3 years prior to the crash, compared with only approximately 30% of school bus drivers. Also, 31% of charter bus drivers were coded with a driving error in the current crash, compared with 24.1% of school bus drivers. Wong, Wong, and Sze (2008) reported that public light bus drivers are one of the most problematic driver groups in Hong Kong because of their high rate of driving offenses and involvement in crashes. Also, Greiner et al. (1998) noted that risky practices of bus drivers usually include

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speeding, driving off from a stop before passengers have time to get seated, running red and amber traffic lights, and not stopping completely at stop signs. The findings of Greiner et al. suggest the use of guaranteed rest breaks and flexible timing for accident prevention and the removal of work barriers for reducing absenteeism. School bus crashes account for the greatest share of fatalities, but only a small proportion involve occupants of buses (approximately 8%). In contrast, 34% of fatalities in crashes involving charter buses occur to bus occupants, and 36.7% of the fatalities in “other” bus crashes occur to bus passengers (Jarossi, 2005). The type of crash was also associated with the kind of victims and carrier type. For example, transit and school buses are much more likely to be struck in the rear than to be the striking vehicle, whereas the other bus types are equally likely to be striking or struck (Jarossi, 2005). This can be explained by the working nature of transit and school bus drivers, who usually have pickup and drop-off operations in which the bus stops frequently in traffic, whereas the other types travel in a point-to-point mode without frequent intermediate stops in traffic. On the other hand, when traveling long intervals on high-speed roads (typical for intercity and charter/tour operations), the likelihood that run-off-road crashes will occur is higher. Similarly, “hit object in road”dtypically collisions with pedestrians or other nonmotoristsdaccount for 20e23% of the fatal involvements for most bus types but for more than 40% of the fatal crash involvements of transit buses due to the exposure to urban areas. Due to the demanding work of bus drivers described previously, this vocational group comprises one of the professions with the worst health (Tse et al., 2006). The main syndromes they suffer from are cardiovascular disease, gastrointestinal disorders, and musculoskeletal problems (Winkleby, Ragland, Fisher, & Syme, 1988). Tse et al. found that bus drivers report hypertension and stress and attribute them to both repression of anger as a coping strategy and elevated blood pressure. According to Gustavsson et al. (1996), the main stressors of their occupation are either psychological (as a consequence of coping with time pressure, work shifts, and traffic load) or physiological (noise and air pollution). Many shift drivers cited financial need as their most important reason to work long-term shifts (Chen et al., 2010). This indicates that the association between shift work and arteriosclerosis risk may reflect the emotional burden of being marginally employed. Regarding the gastrointestinal disorders suffered by bus drivers, Tse et al. (2006) report many complaints of stomach, ulcer, and digestive problems, apparently due to the irregular work shifts that do not allow regular nutritional activity to be managed. The musculoskeletal problems are manifested in complaints of pains originating from the neck, shoulder, and knee. The main reason for these

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complaints is permanent muscle tension caused by inflexible sitting behind the wheel for many hours. In particular, neck pain has been attributed to the frequent sharp turns of the head to the left and right when boarding passengers and driving. In addition to the health problems mentioned previously, bus drivers frequently complain about psychosomatic disorders. Meijman and Kompier (1998) reported higher rates of psychosomatic complaints that were associated with those temporarily/permanently disabled from bus driving and/or absence frequency. Interestingly, illness (psychosomatic complaints and musculoskeletal problems) contributing to absenteeism was related to the driver’s priority to either safety or schedule maintenance. Drivers who viewed safe driving as a priority suffered less absenteeism. Those who tried to maintain the running schedule at the expense of safety had higher absenteeism levels. Moreover, drivers who were disabled from working as a result of the job were found to favor balancing both safety and schedule (Meijman & Kompier, 1998). Hennessy and Wiesenthal (1997, 1999) found relationships among driver stress, frustration, irritation, negative mood, and aggressive driving such as tailgating in conditions of traffic congestion, particularly in situations of time urgency. They also studied the relationship between stress and aggression of bus drivers in different levels of traffic congestion. They found that both stress and aggression of the driver were greater in high- than in lowcongestion conditions. In low congestion, time urgency predicted state driver stress, whereas aggression predicted driver stress in high congestion. In both conditions, the driver’s perception of the driving task as stressful could predict the level of stress of the driver for both males and females. High rates of labor turnover and early retirement are one of the consequences of the demanding job of bus drivers (Tse et al., 2006). Drivers in their 40s were at particular risk of early retirement as a result of hypertensive morbidity. Moreover, only 1 out of 10 drivers leaving one transport company had reached official retirement age (60 years). One of the salient phenomena related to driving impairment is fatigue. According to Brown (1994), fatigue is considered to be a subjectively experienced disinclination to continue performing the task at hand. It generally impairs worker efficiency. Fatigue is created by (1) long shifts that may cause longer response time for even simple tasks; (2) missed rest and food breaks; (3) tiredness that may result in falling asleep on the job, which may be dangerous; and (4) the possibility that circadian rhythms, which dictate the daily cycle of activity in organisms, may be disturbed by rotating shift patterns, which may aggravate fatigue symptoms (Brown, 1994). Robertson (2003) asserted that sleepiness while driving might elevate the rate of both slips (wrong gear selection)

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and mistakes (misjudging the situation and attempting to overtake other vehicles when there is insufficient time to complete the maneuver). Sleep deprivation, in his opinion, reduces vigilant attention and may cause 30% of total road crashes. In addition to physical health, mental health is a crucial factor to bus drivers’ well functioning (Tse et al., 2006). Low job dissatisfaction, low supervisor support, high psychological demands, and frequency of specific job problems may be predictive of higher crash involvement. One particular stressor that has been implicated in poor psychological health for bus drivers is negative passenger interaction, involving obnoxious behavior, fare evading, or even physical assault. Longer term psychological distress in the form of post-traumatic stress disorder may be a rare symptom for bus drivers, but it is nevertheless an insidious one. Problems in mental health may produce behavioral outcomes such as alcohol and drug use, which may have a negative influence on functioning as bus drivers. Ragland et al. (1987) found a positive association between the number of years driving buses and the average weekly alcohol consumption, job stress, and strain reactions. The same was found regarding drivers who reached service tenure. Furthermore, drivers often used stimulants during shift work to remain alert at work and consumed sleeping tablets when attempting to sleep during the day. In summary, bus drivers cope with various challenges involving organizational and safety features and hence develop physical and mental problems that cause high labor turnover and early retirement.

4. TAXI DRIVERS Taxi drivers are an essential part of a public transportation system (Dalziel & Soames Job, 1977). They are seen as a special group with specific behavioral characteristics (Rosenbloom & Shahar, 2007), but they also belong to one of the most dangerous driving occupations because of the many risks involved (Machin & De Souza, 2004). Burns and Wilde (1995, p. 276) claim that taxi drivers have “highrisk personalit[ies] who prefer to drive in excessive speeds and carelessly change lanes.” In this occupation, there are physical, environmental, and health-related risks. Machin and De Souza (2004) found that taxi drivers were victims of nonsexual robbery at a rate higher than that of the average community. In a report about taxi drivers in New South Wales, Australia, Lam (2004) ascertained that gender (i.e., being female) was related to greater risk for injury and mortality. Environmental factors affect taxi drivers. Lam (2004) found that taxi drivers were less prone to accidents when they were transporting passengers than when they drove on their own. One explanation given for this trend is that taxi

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drivers tend to rush to waiting passengers for pickup, and this increases their speeding and risky driving and hence increases the force of impact should a crash occur. Working the night shift also increased the relative risk, as did the degree of familiarity of the area in which the drivers drove: The less familiar they were with the area, the greater the relative risk. Adapting to existing systems in the car and configuring technology for their own needs, such as GPS-type devices, showed their desire to reduce stress rather than to improve their driving efficiency (Gerardin & Blat, 2010). Seat belt use also yielded interesting results. Whereas seat belt compliance for the general population of drivers in China, Australia, Scandinavia, and South Africa is between 80 and 90%, the percentage for taxi drivers in the same countries is 6e60% (Routley, Ozanne-Smith, Qin, & Wu, 2009). Routley et al. found that more than 50% of the interviewed male taxi drivers who spent more hours in their vehicles and had more experience than other drivers reported that they always used seat belts. However, roadside observation of whether or not they were wearing seat belts yielded a result less than 45%. Observation of belt use within the taxi was 36.2%. On the one hand, the drivers claimed that they felt “trapped and uncomfortable” (Routley et al., 2009, p. 452) and thus did not wear a seat belt. On the other hand, reasons for wearing a seat belt included “fine avoidance, safety, high speed, and long trips.” Some taxi drivers never wore seat belts because they had a fear of assault, and the wearing of a seat belt would prevent them from a fast escape. Having ABS in the taxi also played a role in the behavior characteristics of taxi drivers. The results of this study indicate that drivers show behavioral adaptation to safety equipment in the car. Drivers of cars with ABS tend to compensate by closer following. This most likely reflects an inclination to drive faster with ABS (Sagberg, Fosser, & Sætermo, 1997). Taxi drivers spend most of their time on roads. This could explain their dominance of road knowledge and cognitive skills that are more developed than in private drivers. Peruch, Giraudo, and Garling (1989) found that taxi drivers more efficiently used knowledge they had acquired of routes. Furthermore, taxi drivers estimated travel distances as shorter, possibly due to their greater familiarity with shortcuts compared to private drivers. Taxi drivers were found to have greater gray matter volume in posterior hippocampi and less gray matter volume in anterior hippocampi compared with control subjects (Woollett & Maguire, 2009). In this context, Maguire, Woollett, and Spiers (2006) found a negative correlation between anterior hippocampal gray matter volume and the number of years spent taxi driving and a positive correlation between their experience as taxi drivers and posterior hippocampal gray matter volume.

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This finding is possibly explained by the acquisition, storage, and use of the “mental map” of a large complex environment (Maguire et al., 2006). The negative correlation between anterior hippocampal gray matter volume and experience as taxi drivers may explain the reduced performance of taxi drivers on several anterograde visual associative memory measures (Woollett & Maguire, 2009). Of course, cognitive skills of drivers may be related to safer driving (Anstey, Wood, Lord, & Walker, 2005). Some taxi drivers “might be vulnerable to the effects of fatigue . and circadian rhythm disruption” (Dalziel & Soames Job, 1997, p. 492). Dalziel and Soames Job also found that there was a significant negative correlation between taking time out for resting and accidents: The longer the rest break, the less likely fatigue would set in. They concluded that errors that occur when tired were less likely to occur if the drivers took sufficient rest breaks. Fatigue also contributed to a clearer self-assessment of their driving capabilities at any given time; they were more aware of the effects of fatigue than other drivers. Health factors among taxi drivers are intriguing. Machin and De Souza (2004) demonstrated that the number of dangers that taxi drivers encounter in their work play a role in the prediction of their physical and emotional health. They found that hazards, displaying aggression, and perception of management’s commitment to health and safety were all significant predictors of the amount of drivers’ emotional well-being, while aversion to risk-taking, aggression, and perception of management’s commitment to health and safety were significant predictors of drivers’ unsafe behavior. (p. 266)

They also reported that the greater the dangers to which the taxi drivers were exposed, the more symptoms of illnesses they had and the more negative emotional reactions to work they carried. Aggression was also a predictor of their health. The more aggressive they were, the more their wellbeing was affected negatively, the more they acted out unsafe behaviors, and the more they were negative about their work. Dalziel and Soames Job (1997) also found that taxi drivers who were high risk takers continued to work when they were fatigued, knowing that they were taking higher risks on the road. In addition, Figa-Talamanca et al. (1996, p. 755) reported that male taxi drivers had “lower prevalence of normal sperm forms,” especially those who worked as taxi drivers for a long period of time. Male taxi drivers also showed a different attitude to traffic violation penalties (Rosenbloom & Shahar, 2007). Non-professional drivers judged the penalties as slightly higher than did taxi drivers and the judgment of penalties as slightly higher as a function of penalty severity. Nonprofessional drivers judged the penalties as slightly more than did taxi drivers in low and medium severity of penalty conditions but not in high-severity conditions. The attitudes

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of minibus taxi drivers as well as those of law enforcement officials showed lack of respect for each other as well as poor communication between the two groups (Botes, 1997). In summary, based on the frequency and severity of hazards ranging from verbal abuse to homicide, the working environment of the taxi industry is one of the most demanding (Machin & De Souza, 2004). Such environments are typified by less supervision, more flexible hours, and less perceived control from management. This has an impact on high levels of risk-taking behavior among taxi drivers (Peltzer & Renner, 2003). Improving the safety of taxi drivers should be driven from the organizational view and by establishing a safety culture.

5. SUMMARY As discussed throughout this chapter, each group of professional drivers has unique features and others that are shared with other groups of professional drivers. Prolonged time spent on the road, for all professional drivers, determines the most common characteristics, but the unique task of each group dictates and shapes the specific characteristics that distinguish them from each other. Drivers’ accumulated experience contributes to higher control of the vehicle in which they drive but at the same time reduces safe driving. The hours of driving have long-term and short-term effects on the health of professional drivers, especially fatigue and even acute or chronic sleep restriction. These symptoms have implications not only for their health and quality of life but also for road safety. In addition to fatigue, bus and taxi drivers are prone to suffer from other diseases resulting mainly from the mental and physical intensity of their work. There is a need to consider the effects of this morbidity typical of these professions to prevent and minimize it as much as possible. Also, it is recommended that driver training for both truck drivers and private car drivers include reference to the special on-road dynamics between the two groups.

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Part V

Major Countermeasures to Reduce Risk 29. Driver Education and Training 30. Persuasion and Motivational Messaging

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Chapter 29

Driver Education and Training Esko Keskinen and Kati Hernetkoski University of Turku, Turku, Finland

1. INTRODUCTION Traffic crashes are the major cause of death of 15- to 24year-olds in Organisation for Economic Cooperation and Development (OECD) countries. Data from various countries indicate that crashes involving young drivers account for between 20 and 30% of total road traffic fatalities. Clearly, young drivers play a disproportionate role in the overall public health problem of road traffic safety risk (OECD, 2006). Different countermeasures have been created to reduce the number of fatalities as well as serious injuries and crashes with only material damage. Licensing, generally including driver education or training and testing, aims at ensuring that those who drive in traffic are able to do so in a safe way. Regulations concerning licensing may include age limits and certificates concerning health as well as other kinds of limits or curfews (e.g., lower blood alcohol level for novice drivers and nighttime driving curfews). Regulations concerning behavior in traffic (e.g., speed limits) and their enforcement represent an attempt to guarantee that drivers behave in a safe way also when driving solo. Driver education and training has an interesting history. It started from the self-evident needs of new car owners who had to receive instructions on how to use their vehicles, and it took a long time before training became a society-regulated activity that was seen to also have possibilities for increasing safety. Not even the driving license was self-evident in the beginning (Hatakka et al., 2003). At first, driver education and training concentrated on the technical maneuvering of the car and driving in traffic situations. Only the immediate needs of drivers were answered by driver education and training. Safety as we understand it nowadays was not a major issue at the beginning of the twentieth century. There are some conceptual problems when we try to discuss and reflect upon the literature concerning driver training and driver education both in Europe and in the United States, Canada, and Australia. Both driver training and driver education seem to have partially different contents in Europe and in these other countries. In Europe, Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10029-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

driver education covers many different ways to enhance driving skills and safety on the road. Professional driving schools offer driver education, but in Europe accompanied driving may also be defined as some sort of driver education. In the United States, Canada, and Australia, driver education is often defined as formal instruction, which includes in-class education and in-vehicle training (Mayhew & Simpson, 2002). Often, the definition of driver education in the United States includes the idea that this education takes place in high school (Williams & Ferguson, 2004). In the European Union, the licensing age for driving a car is 18 years, and only exceptionally 17 years, and there are not usually programs in schools aimed at getting a driver’s license. Mayhew and Simpson (2002) from Canada even use the expression “education/training.” McKenna (2010) argues that it would readily be possible to distinguish training (skill acquisition) from education (knowledge acquisition) in the driving field; there is little evidence that people note the difference. Siegrist (1999) and Christie (2001) defined driver training as referring to a specific instructional program or set of procedures that relate to car control or car “craft.” Driver education refers to a more contemplative and value-based instruction of knowledge and attitudes relating to safe driving behavior. According to Senserrick and Haworth (2005), driver training can be viewed as a specific component of the broader field of driver education. In this chapter, we use the expression driver education as defined by Senserrick and Haworth (2005) to cover any kind of effort by teaching and learning aimed at increasing driver candidate’s skills in traffic and motives to use these skills in safety-enhancing ways. Both “formal” (professional) learning in driving schools and “informal” learning with nonprofessional supervisors and the combinations of these means are considered in this chapter to be part of driver education. In addition, because getting a driver’s license often requires passing a driving test, we include the driving test as one part of driver education. In Europe and other countries, there has been an increased interest in recent years in driver education and 403

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training and other measures to try to increase young drivers’ safety (OECD, 2006). In the United States, Canada, and Australia (Christie, 2001; Senserrick & Haworth, 2005), interest concerning novice drivers has been increasing, but the problems and the focus have been quite different from those in Europe. Whereas in Europe interest has been in theoretically based driver education models, such as Goals for Driver Education (GDE; Hatakka, Keskinen, Gregersen, Glad, & Hernetkoski, 2002), and the curricula and the driving test developed according to these models, in other countries the interest has been mainly in developing driver education based on graduated driver licensing (GDL)-type, supervised, long-lasting training models (Shope, 2007; Waller, 2003). The main driver education in the United States, Canada, and Australia is GDL, but driving school and curriculum-based education have been focus upon more than in the past (Gandolfi, 2009; Lonero et al., 1995). Williams and Ferguson (2004) have warned of this trend, “Of considerable concern is that scarce resources continue to be spent in the name of safety on programs (driver education) that have no benefit or may even make things worse” (p. 6).

2. EDUCATION AND TRAINING WITHOUT A THEORY One might think that in such an applied area such as traffic psychology, there would be plenty of theories or models on how beginning drivers learn to drive and how they should be taught to decrease the risk of accidents during the first months of their driving career. In fact, this is not the case: Driver education and training is not theory driven (McKenna, 2010). The first 3e6 months is fatal for many novice drivers. In the past, it was difficult, if not impossible, to find theories concerning driver behavior that would lead to practical applications. Driver education and training was based mainly on three ideas: “Driving/practice makes perfect,” “solo driving is safer when novice drivers are older” (Keating, 2007), and driving is a complex skill and professionals teach skills effectively. The different applications (nonprofessional training, accompanied driving, and GDL) of the first idea are based on the theory of “learning by doing” originally described by U.S. philosopher John Dewey (1859e1952). The idea and its applicationdthe more you drive, the better you will be at drivingdis very popular among those who favor the nonprofessional way of obtaining the skills for licensing. Nyberg, Gregersen, and Wiklund (2007) called it “quantity training” compared to quality training. However, it is popular not only among laymen but also among professionals in traffic psychology. One of the most cited figures in traffic psychology is the learning curve from Maycock, Lockwood, and Lester (1991).

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Accompanied driving or nonprofessional instruction or the learner phase in GDL models are more or less applications of this idea: Driving makes perfect. Of course, the idea is intuitively sound: Human beings learn many things just by doing them. Another issue concerns what a learner is learning by this kind of massive training. Keating (2007) points out a self-evident fact concerning learning by doing. He writes that “it is important to note, however, that unsafe habits can be automated as readily as safe ones (Keating, 2007, p. 153).” If practice makes perfect, we can just wonder what perfect may mean in driving. Does it necessarily mean safe, and if it means safe, what does safe really mean? When discussing safety associated with novice drivers, it is important to explicitly express the content of the concept. For example, Waller (2003) speaks only of “good” or “proficient” drivers without defining the concept in detail. Do we mean that drivers do not have fatal accidents, or do we mean that drivers are not involved in less serious crashes? There is a major difference between measuring safety by self-reporting methods, which are often concentrated on property damage accidents of slight injuries, and using fatal accidents as the database. Another issue concerning this “quantity training” is that it is done usually without any model or concept of driving. The lack of propositions concerning the concept of driving in applied scientific fields such as traffic psychology is surprising. Questions concerning what is driving and what makes driving safe are important when considering driver education (Keskinen, Hatakka, Laapotti, Katila, & Pera¨aho, 2004). We discuss this question and also our proposition of a hierarchical driver model (Hatakka et al., 2002) more thoroughly later. However, when we examine the beginning of the GDL framework, we notice that the arguments supporting GDL are coming not from the theories of driver behavior but, rather, from the theories of learning. In her influential work, Waller (2003) made it clear that “graduated licensing is not designed to address deliberate risk-taking behavior. Rather, it is aimed at the inexperience component of young driver’s crash risk” (p. 19). The central factors affecting learning that Waller describes and what she uses as arguments for GDL are that (1) distributed learning is more effective than mass learning, (2) learning should proceed from simple to complex, (3) all beginners are at higher risk, and (4) demonstration of skill is not a substitute for extended practice. On this last point, she argues, as do many others (Williams & O’Neill, 1974), that “high levels of skill do not necessarily translate into good performance on the road” (Waller, 2003, p. 19). Waller’s proposal for GDL, which is based on earlier literature concerning factors that affect safety, has four elements: (1) Initial experience should occur under low-risk conditions, (2) the system should be based on an extended supervised practice, (3) learning conditions should gradually move from simple to more

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complex, and (4) the system should punish beginning drivers’ deliberate risk taking using harsher penalties. At least the first three elements are widely accepted. The applications of the second idea, that young age creates risks in solo driving, are not discussed as much in Europe, where the age limit for licensing is 18 years, with some exceptions. However, if we examine GDL model driver licensing, we can see that pushing solo driving toward older age is one of the main goals (Keating, 2007), and it has been discussed since the implementation of the GDL system (Waller, 2003). The reason for this is that many jurisdictions in the United States, Canada, and Australia have traditionally accepted solo car driving for teenagers as young as 14 or 15 years of age (Waller, 2003). Keating argues that the GDL system fits well with the facts from developmental psychology. However, Keating starts his case from the existing GDL system and looks at evidence supporting that system and not vice versa. The GDL system’s origins are not based on theoretical models but, rather, on the need to prevent young novice drivers from driving solo too early. Evaluation findings lend support to delaying licensure among teenagers in the United States, where licensure is commonly allowed at 16 years of age (McCartt, Mayhew, Braitman, Ferguson, & Simpson, 2009). The third case, professional driver training, is also not an application of any explicit theory of driving, or at least the idea of driving has been very limited: maneuvering the car and managing the traffic situations (Hatakka et al., 2002). Professional driver education, at least in Europe, has been based on a “skills view of driving”: Driving is a psychomotor task and, like any other psychomotor task, it is trainable. Skill theory of driving is connected to measurable contents of driving that can be tested (Baughan, Gregersen, Hendrix, & Keskinen, 2005; Goldenbeld, Baughan, & Hatakka, 1999). Driving school training has concentrated on teaching skills that give candidates a chance to pass the driving test. The licensing system may even be test driven: Tasks in driving test control what is taught and learned in driver education. However, Baughan et al. (2005) show that driver testing can only be testing of “maximal behavior” and it is not possible, in a reliable way, to measure “typical behavior.” By maximal behavior, the authors mean skills that can be defined as being made in a “right or a wrong way,” such as changing gears or selecting and keeping the lane and obeying rules. Either a candidate can behave in an appropriate way or not when he or she is asked to do something. Psychological intelligence testing is an example of maximal testing. By typical behavior, Baughan et al. mean testing in which the aim is to get information about how the candidate usually or typically behaves. This distinction is another traditional dichotomy in traffic psychology at least since 1991 when Evans, and later Keating (2007), described it by saying that

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safe driving is not only a matter of how well one drives but also a matter of how one drives in the real world. Motivational aspects are important with regard to safe behavior, and they control what we do in a situation. In the skilloriented approach to drivingdas discussed, for example, by Groeger (2000)dthere is not even such a subject in the subject index as motivation; instead, Groeger speaks about goals. For Groeger, driving is a complex psychomotor skill that is controlled via different levels of goals while at the same time starting from more general goals, such as the purpose of the trip. Overviews have shown that in Europe, the primary focus of current training systems is on the lower order car driving skills, whereas there is a lack of training on more strategic issues (Hatakka et al., 2002). Driver training should address all aspects that contribute to the high accident risk (Twisk & Stacey, 2007) and not just on technical skills that make driving possible but not safe. As an answer to this demand, a theoretical framework was developed. The framework had its origin in Finnish research within the field of traffic psychology (Keskinen, 1998). The GDE framework was introduced in its current extended form within the European Union (EU)-funded research project GADGET (Hatakka, Keskinen, Gregersen, & Glad, 1999) and published internationally by Hatakka et al. (2002). In the GDE framework, the authors offered a theory-based goal structure for improving driver education. The idea was to describe important goals and contents but also to examine learning methods to improve factors that have a central effect on novice drivers’ safety. The GDE framework has been widely acknowledged within the European traffic research community as a fruitful starting point for developing traffic education (Pera¨aho, Keskinen, & Hatakka, 2003). On the basis of this framework, several countries are redesigning their driving courses and testing procedures (Twisk & Stacey, 2007).

3. CONNECTIONS BETWEEN TYPICAL NOVICE DRIVER ACCIDENTS AND THE GOALS AND CONTENTS OF DRIVER EDUCATION As previously noted, before the GDE model (Hatakka et al., 2002), there was no theoretical starting point for the goals and contents of driver education beyond technical car maneuvering and managing of traffic situations, which basically means knowing and following traffic rules. There is much knowledge concerning novice drivers’ accidents, but this knowledge has not affected the goals and contents of driver education in an organized way as a model. However, one of the central questions is what are those accidents we are talking about, because fatal and injury accidents have a different background than property

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damage accidents (Clarke, Ward, & Truman, 2002; Moe, 1999). Tronsmoen (2010) argues that minor accidents may be caused by lack of driving skills, whereas more serious accidents happen due to violations in the form of speeding and drunk driving. Parker, Reason, Manstead, and Stradling (1995) have even shown that violations are associated with accidents in traffic, but errors and slips and lapses are not. This different background for different accidents means that in order to try to consider all types of accidents, different methods have to be used. Another difficult issue is to examine the facts of accidents and determine the important factors “causing” them. On the surface, the cause may be error in maneuvering the car, but at the same time the speed may be high, revealing problems in making predictions and decisions concerning speed but also possible problems in safety motives or lifestyle. This is always the basic problem when trying to determine the factors affecting accidents (Keskinen, 1982; Reason, 1990). Slippery driving accidents are a good example of this problem (Katila, Keskinen, & Hatakka, 1996; Laapotti & Keskinen, 1998). In Norway, a slippery driving course was made mandatory for car drivers (1979) but also for drivers of heavy goods vehicle (1993) (Christensen & Glad, 1996; Glad, 1988). The course content can be considered as technically oriented. The aim was to increase drivers’ skills in handling a vehicle in risky situations. Both driver education cases were evaluated, and both gave clear negative results: Accidents on a slippery surface increased after the introduction of the obligatory courses (Christensen & Glad, 1996; Glad, 1988). The evaluation study by Glad is particularly often referred to in the literature. One reason is that both Norwegian studies are especially well done. Both are based on an experimental design, which is unusual in evaluations concerning education (Keskinen & Baughan, 2003). One important factor in developing driver education is to consider the learning principles supporting the learning aims of the driver education. However, there must be a close connection between selected learning principles and the aims and contents of learning in driver education. Waller (2003) relied on four learning principles that are known to support learning of perceptualemotor skills and rote learning. Her idea was that practice makes perfect, but she did not rely so much in her argument on the quality of novice drivers’ accidents. However, relying on the quantity of rehearsals and counting only the number of accidents is not enough. We have to know what the quality is and the nature of the problems we are trying to solve. What is needed is the thorough understanding of factors behind novice drivers’ accidents of different kinds and a model that brings this knowledge together to create as whole a picture of the phenomenon as possible. The OECD (2006) defined the main factors behind novice drivers’ risk in traffic using five classes of

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phenomena. The idea has been to define factors from different perspectives and from different time frames. Some factors are closely connected to driving; others are connected to person. Some factors are connected to the immediate accident situation; others have a more distant connection. The factors in the OECD report are (1) general “nature” and “nurture” aspects of young, novice drivers, not directly related to drivingdbiological factors, differences between men and women, personality, social norms, driving behavior, and vehicles as tools for achieving goals in life; (2) acute impairments in the situationdalcohol, drugs, fatigue, distraction in car and outside, and emotions; (3) the acquisition of driving skillsdgeneral learning factors, skill acquisition and mental workload, visual search skills, and hazard perception skills; (4) the willingness to drive safely and self-assessmentdmotivation to drive safely, overconfidence, and risk assessment; and (5) risk-enhancing circumstances, elements of exposuredtask demands/exposure and vehicle choice. Here, we have a full picture of novice drivers’ risks (OECD, 2006). We have background factors (1) such as age, gender, personality, and social norms. (2) Closely associated with background and lifestyle factors are use of alcohol and other distracters in driving situations. We have skill factors (3) that mainly concentrate on perceptual motor skills but also hazard perception skills. Motivational factors (4) are an integral part of driving and accidents. Background factors and motivational factors are again closely associated with each other and risk-enhancing circumstances. (5) Risk-enhancing factors are realized in such issues as why, when, with what, and where a driver drivers. The quality of chosen driving tasks and environments is important with regard to safety, not only kilometers or miles driven (Laapotti et al., 2009). From the previous discussion, it can be seen that there are different kinds of factors that increase novice drivers’ accident probability. We can find detailed analysis of them in this book: age (Chapter 23), impaired driving (Chapter 17), speed (Chapter 18), culture (Chapter 14), fatigue (Chapter 21), brain and decision making (Chapter 9), visual search patterns (Chapter 11), emotions and personality (Chapter 12), and use of safety restraints (Chapter 16). It is self-evident that the previously mentioned risk factors have to be somehow attended to in driver education curriculum.

3.1. What Do Motivational Theories of Driver Behavior Offer to Driver Education? There are three major motivation-based theories of driver behavior: Wilde’s (1982) risk compensation model (later termed target risk; Wilde, 1994), Fuller’s (1984) risk avoidance model (later termed the task-difficulty homeostasis model (Fuller, 2008; in this book, termed risk

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allostasis theory), and Na¨a¨ta¨nen and Summala’s (1974, 1976) risk threshold model (also called the zero-risk model). These theories have been evaluated and criticized in review articles such as those by Michon (1985) and Ranney (1994). In simplified terms, all risk theories consider that the key in driving safety is taking risks or how to learn not to take risks. Wilde (1994) and Fuller (2008) make recommendations concerning driver education, but their recommendations have not had any general effect, at least explicitly in curricula. This is understandable because trying to develop a driving school curriculum around one main idea is very difficult, considering the variety of risk factors in driving. However, one idea is quite central in motivational and risk theories; that is, the consideration of the nature of riskdobjective risks and subjective or observed risks and their interplay in accident causation. One of the reasons for the high number of accidents according to risk theorists (Na¨a¨ta¨nen & Summala, 1974) is that the subjectively estimated risk in a situation is lower than the real risk. If subjectively estimated risk were higher than the real risk, then it would prevent such behavior that would lead to risky situations. Another way of considering the same phenomena is to speak of driver’s subjectively estimated skills and skills that are needed to carry out certain skills in a driving situation (Kuiken & Twisk, 2001). The key in safe driving, according to the idea of “calibration,” is the driver’s ability to accurately balance between task demands and skills. Thus, it is not enough that skills for performing a certain action are trained if the driver cannot estimate in an accurate way the demands of the situation compared to real skills. As indicated from Norwegian (Glad, 1988) and Swedish (Gregersen, 1996a, 1996b) study results, increasing skills may increase subjective skills more than objective ones. The idea of subjective and objective risks and subjective and objective skills needed in driving is closely associated with the self-assessment or self-awareness theme in the psychology of expertise (Kolb, 1984; Mezirov, 1981). The central idea is that a driver as an expert should always be aware of what he or she is doing and why and should determine if the task for him or her is appropriate or is it too difficult. This concept is called metacognition in cognitive psychology.

3.2. Driving as a Complex Cognitive Skill All modern theories of driver behavior are based on the idea that human behavior is based on how humans process informationdon cognitive processes. That is why it is difficult to separate theories concerning complex skills and theories concerning risks and motives. In fact, Summala (1996) described driving as a hierarchical decision-making system in which motivational aspects and cognitive aspects of driver behavior are combined.

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Groeger’s (2000) theory of driving as a complex cognitive task is based on cognitive processes. Groeger focused on the basic cognitive processes that are responsible for human perception, action, decision making, etc. He described a four-faceted framework for understanding the driving task. The four modules in his framework, almost all of which interact with each other, are (1) implied goal interruption, (2) appraisal of future interruption, (3) action planning, and (4) implementation. Groeger described elements of each of the four modules. These elements are measurable cognitive processes. Groeger then used this model to explain “driving ability ratings assessed by an accompanying examiner” via multiple regression approach. His results were promising. However, although it is possible to describe and assess driving ability, Groeger’s results do not indicate much about the safety of the driver, only about skill. Gregersen and Bjurulf (1996) developed a model concerning significant factors associated with young novice drivers’ driving behavior and accident involvement. They wrote about their basis for their model that considerable benefit can be drawn here from the general theories about learning, information processing and decision making, and attitudes and values about social interaction and influence, but here it is necessary to further refine both their application in the particular context of novice driver behavior and their mutual significance and interaction in this context (Gregersen & Bjurulf, 1996). In their model, two main processes are described: the learning process and aspects of life (social influences and individual circumstances), which are age related. Gregersen and Bjurulf show that three main problems are built into this learning process: (1) Getting enough experience takes time, (2) overestimation of own skills is possible, and (3) there is a false (too high) sense of safety. Gregersen (2003) concludes that age-related factors account for 30e50% of the reduction in accidents during the first few years of driving. The model of Gregersen and Bjurulf (1996) provides a list of important factors that must be addressed in driver education. One of the prominent features is the difference between objective skills and subjective skills. This possible difference creates a need for using self-reflection or selfevaluation in driver education or, as Gregersen (1996a, 1996b) called it, “insight learning.” What can also be considered important in the model of Gregersen and Bjurulf is that the model contains motives and attitudes as part of the lifestyle and group norms. In this way, the model is more general than that of Groeger (2000).

3.3. Hierarchical Models in Psychology and Our Way of Defining Driving Although knowledge of how to use the controls of a car and how to maneuver it forms the basis of driving, an analysis

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of the driver’s task and accidents has shown that adequate psychomotor skills and physiological functions are not sufficient for safe driving. This conclusion concurs with the notion that driving is by and large a self-paced task (Na¨a¨ta¨nen & Summala, 1974). It is the driver’s own actions and decisions that determine how successful and safe his or her driving is. Modern research in traffic psychology shows not only the importance of performance factors (i.e., what the driver can do) but also the importance of motivational and attitudinal factors (i.e., what the driver is willing to do) (Rothengatter, 1997). This observation concurs with the distinction between the concepts “errors” and “violations” in driver behavior (Parker et al., 1995; Reason, Manstead, Stradling, Baxter, & Campbell, 1990). Errors are regarded as behavior (actions, maneuvers, etc.) with a non-intended outcome. Violations, on the other hand, refer to faulty actions (especially from a safety standpoint) made deliberately despite having knowledge of their possible implications. This distinction is fruitful, for example, when discussing age and gender differences in traffic (Rimmo¨, 1999). Since publication of the book by Miller, Galanter, and Pribram (1960), hierarchical approaches have been used when trying to conceptualize and explain human behavior. The importance of hierarchical approaches is realized also in the general debate in traffic psychology (Janssen, 1979; Michon, 1985, 1989; Ranney, 1994; Summala, 1985). Earlier hierarchical approaches focused on the performance aspects of driving behavior (Mikkonen & Keskinen, 1980; Rasmussen, 1980; van der Molen & Bo¨tticher, 1988), but such an approach can also be used to combine the motivational and attitudinal aspects of driving behavior with performance, or operations in certain traffic situations. Such a four-level combination was developed by Keskinen (1996), building on the earlier three-level hierarchy by Mikkonen and Keskinen (1980). A comparative overview of various hierarchical theories is provided by Keskinen et al. (2004).

3.3.1. Description of the Four Hierarchical Levels of Driver Behavior Although the four hierarchical levelsdmaneuvering, traffic situations, goals and context of driving, and goals for life and skills for living (Hatakka et al., 2002; Pera¨aho et al., 2003)dare qualitatively different from each other and separated in the model, no single level is independent from the others. They are all present in a driving situation, and together they encompass the different components that are present in a driving task. A basic assumption in the model is that higher levels control and guide behavior on lower ones. However, this control is not a simple top-down process because it is

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constantly checked against the feedback received from the action. The levels are to some degree interdependent so that change at one level by necessity brings about change at other levels too, downward as well as upward. However, interdependence does not imply equality. The cognitive structures that we call the “highest level” (level 4 in the model) provide the basis for a person’s way of life in general as well as in the specific traffic context. They are therefore more stable and fundamental compared to the other three levels, which in turn are more domain specific and subordinate. Skills that are used, and the inner models that are applied (choices that are made), at the lower levels are therefore under guidance of higher level preconditions (including higher level skills for coping in life) and demands (including goals and motives). Factors locating on the highest level are the ones that are most important from a safety standpoint. No matter what amount of safetyrelated knowledge a driver may have, the effect of this knowledge is ultimately dependent on if and how the driver uses it. 3.3.1.1. Level 4: Goals for Life and Skills for Living The personal motives, behavioral style and abilities, and the social relations of a driver in a broader sense are the main ingredients in the highest level in the hierarchy. These include personality factors such as self-control but also lifestyle, social background, attitudes, gender, age, group affiliation, importance of cars and driving as part of one’s self-image, and other preconditions that research has shown to have influence on choices and behavior as a driver. There is ample proof that such factors also have a direct influence on accident involvement (Berg, 1994; Gregersen & Berg, 1994; Hatakka, 1998; Jessor, 1987; Schulze, 1990). This level also includes factors such as a driver’s physical and mental abilities (e.g., handicap and cognitive level of functioning). It is easy to understand the importance of these factors in a scene-setting sense. They are factors that the individual driver, or driver education for that matter, can do very little about other than taking them into consideration as something that limits the choices available. However, awareness of such personal limitations serves to lessen their negative effect. The differences between males and females with regard to traffic risk can be traced back to lifestyle differences and the motivational aspects of driving. These differences are both qualitative and quantitative (Bru¨hning & Ku¨hnen, 1993; Farrow, 1987; Keskinen, Laapotti, Hatakka, & Katila, 1992; Laapotti & Keskinen, 1998; Twisk, 1994b). For example, Schade and Heinzmann (2009) found that novice female drivers had during their first driving with full license 22% fewer accidents and 50% fewer offenses than males had in Germany. Most studies of young drivers, however, either have focused on males or have failed to distinguish between the behavior of males and females

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(Jonah, 1990; Laapotti & Keskinen, 1998; Renge, 1983). This is why our image of a typical young driver’s accident usually resembles the features of a male driver’s accident rather than that of a female driver. Speeding and driving while impaired are typical for male drivers but rare for female drivers. Such differences are probably the result of differences in motives (Laapotti & Keskinen, 1998). Naturally, in addition to the effect of age, experience also has an independent effect on accident involvement (Maycock et al., 1991). 3.3.1.2. Level 3: Goals and Context of Driving The third level is a decision level, which in certain respects refers to the navigational and planning tasks of the driver that are described in earlier hierarchical conceptualizations of a driver’s tasks (Janssen, 1979; Michon, 1985; Mikkonen & Keskinen, 1980; van der Molen & Bo¨tticher, 1988). The level also contains trip-related goals and driving contextsdthat is, why a driver is driving on a certain occasion, where and when, and with whom. Included is planning of driving route and driving time (e.g., daytime or nighttime driving) as well as choice of driving state (e.g., sober or impaired, relaxed or stressed, and refreshed or tired) and driving company. The social context of driving is an especially important factor with regard to young persons. Social pressure has a considerable impact on driver behavior because a driver is never alone on the road but in constant interaction with other persons, groups, social institutions, and society as a whole. Allport (1985) describes how the thoughts, feelings, or behavior of individuals are influenced by the actual, imagined, or implied presence of others. This refers to an emotional experience of having to respond to someone’s wishes or to some external body of authority regardless of whether this person is physically present or not. Passengerrelated risks are emphasized in many traffic-related social psychological studies (Laapotti, 1994; Marthiens & Schulze, 1989). A social context in the form of a peer group represents the most important influence on the behavior of young male drivers (Lewis, 1985). Farrow’s (1987) and Laapotti’s (1994) results support this statement. So-called “disco accidents” are typical examples of accidents following peer-group pressure (Schulze, 1990). As Twisk (1994a) stated, young drivers especially are not isolated individuals but, rather, part of a closely knit social structure. 3.3.1.3. Level 2: Mastery of Traffic Situations The focus on the second level in the model is on competence that has to do with knowledge of how to drive in a certain traffic situation. A driver must be able to anticipate and adjust his or her driving in accordance with the constant changes in traffic (e.g., choose an appropriate speed). Knowledge of traffic rules, hazard perception concerning

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situations, and interaction with other road users are typical contents on this level. Choices that are made on this level follow from third-level choices and fourth-level preconditions. 3.3.1.4. Level 1: Vehicle Maneuvering A driver needs tools to fulfill his or her motives. Any motivation to show off through driving (fourth level) or knowledge of traffic rules (second level) makes no sense if a person does not know how to start a car engine in the first place. The role of the first, or lowest, level in the hierarchy (“vehicle maneuvering”) is in the model considered to be executive with respect to choices made on levels 2e4. Emphasis is on perceptual and motor skills that have to do with vehicle control and handling. This includes not only basic skills, such as knowledge of controls, driving off, braking, and gear changing, but also more complex knowledge, such as keeping the car under control, evasive maneuvering, understanding the concept of traction, understanding the impact of seat belts, and use of rearview mirrors. 3.3.1.5. All Levels Are Important for Safe Driving The levels are interdependent but not equal. Higher level goals and motives always override skills and considerations on lower levels. It is easy to understand why several attempts to improve safety by improving skills at the lower levels of the hierarchy have actually failed to decrease accidents. For example, negative safety effects have been obtained when improving skills in vehicle handling on slippery road (Christensen & Glad, 1996; Glad, 1988; Katila et al., 1996; Keskinen et al., 1992). If increased skills or, worse, imagined increase in skills (Gregersen, 1996a) are used to satisfy needs for maintaining as high a speed as possible, the results are very likely to be negative. If the motivational level fails to produce a safe strategy for driving, no level of skills in mastering traffic situations or vehicle handling is high enough to compensate for this lack of safety orientation and to produce a safe output. A positive influence (e.g., an exercise in driving school) will produce a positive result only if the higher level preconditions are equally positive. A negative influence (e.g., social pressure) will lose its power if the higher level preconditions provide a positive counterforce. The goals and motives of a driver may either increase or decrease the level of risk. With regard to driver training, the hierarchical perspective demands a wide range of methods in teaching/ instruction. Skills for vehicle maneuvering and mastery of traffic situations are the basis for successful operation in traffic, and these aspects should be learned well during driver training. However, as previously highlighted, the skills that are applied and the choices that are made at the lower levels are under guidance of goals and motives on

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the highest level. In addition to the training of basic skills, driver training should also deal with the higher levels in the hierarchy and take into consideration the driver’s goals associated with driving and, for example, skills for dealing with social pressure during a trip.

4. BASIC DRIVER LICENSING MODELS AND PRESUPPOSITIONS BEHIND DIFFERENT MODELS Driver licensing models usually concentrate only on the systems that guide the licensing process, and they do not say much of the learning content or learning methods. Driver licensing systems differ between countries but also between continents. In all countries in which a developed traffic and safety culture exists, there are regulations concerning driving tests or training or both. Christie (2001), Nyberg (2003), Senserrick and Haworth (2005), and the OECD (2006) have described in detail different kinds of driver licensing systems. Licensing systems differ with regard to licensing age, obligatory education, curriculum, single- or multiphase education, professional and nonprofessional education, the meaning of testing, etc. The main differences concern the balance between training and testing in licensing and the role of professional driver education. All European countries have a testing procedure, but not all have obligatory curriculum to be followed in training. The possibility for nonprofessional education in addition to professional driving school education is also different among countries. For example, Nordic countries (except Denmark), The Netherlands, and the United Kingdom allow nonprofessional education without special demands. In France, there is a combined system consisting of driving school education and accompanied driving. Traditional licensing systems contain only one phase and driving test. In the United Kingdom, only the driving test is obligatory (test driven model), and there is the possibility for a student who turns 17 years old to be allowed to start practicing to take the test on the same day. Currently, multiphase education systems are increasing in Europe. In the United States, Canada, Australia, and New Zealand, the most popular licensing system is GDL. GDL is a multiphase system comprised of a learner phase, practicing phase (with restrictions), and independent driving (full license). GDL was developed to minimize problems caused by the traditionally young age for permitting driver’s licenses in these countries. Even when a student has gone through the entire licensing procedure, he or she may in most states be only 16.5 years old. The OECD (2006) report gives a good overview of driver licensing systems, but missing is the more

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qualitative approach to the systems, not only structural ones. The focus here is on the qualitative (psycho-sociopedagogical) features of the systems. Jonsson, Sundstro¨m, and Henriksson (2003) also provide a detailed description of driver licensing models in some European countries.

4.1. Driving School Education A typical system in Europe is to require professional driver education in driving schools of all applicants for a license category B. The basic idea is that (only) professional instructors deliver effectively the knowledge and skills needed for driving a car and for getting a driver’s license. Professional teachers deliver both theory and practical training. Professional teachers are expected to master the task and to be able to transfer this mastery to learners, who are the receivers of the message. A problem may arise from the fact that there seems to be major variation in the pedagogical training of driving school instructors, but the expectations are still as presented previously. The use of professional training is often associated with a rather short learning period (varying from 1 week to several months). In countries in which driving school is the only option, education is typically arranged according to a curriculum and controlled by authorities. In addition to a short learning period, driving school education often has explicated goals and contents for training, organized and structured teaching, an emphasis on feedback, and at least a possibility that the relationship between practical and theoretical instruction is close. Training resources set limits to training, especially in driving school education, and the contents and methods used in training will be even more essential factors than in accompanied driving. The GDE model (Hatakka et al., 2002; Pera¨aho et al., 2003) is an example of explicating goals of driver education. The idea in the GDE framework is to cover the driver’s task as a whole, including relevant aspects varying from basic vehicle handling to general life skills (e.g., personal motives or impulse control). Hatakka et al. (2002) concluded that traditional driver training is mostly limited to basic skills and knowledge of vehicle maneuvering and mastery of traffic situations, and only some attention is given to risk-increasing factors. Fewer in number and less in use are training goals and training methods aimed at aspects associated with goals and contents of driving and general personal and motivational aspects related to driving. Goals and methods for enhancement of self-evaluative skills are also not typical. There is a large body of research on the effects of formal driver education in the United States (Mayhew & Simpson, 2002; Williams & Ferguson, 2004). The general result is that there is no evidence of the superiority of formal training compared to other forms of learning. This result

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has been repeatedly misinterpreted to show overall fallibility of formal training. However, it shows merely that formal training in the United States, which typically consists of 30 h of classroom teaching and 6 h of driving on the road, does not provide good results. Formal training has also been used in such a way (e.g., for shortening the learner stage) that novice drivers who are eager to drive solo might select the shortened learner stage of formal training. This self-selection may then have an influence on safety: Early soloists may drive more and in a more risky way than those who wait longer and are not so motivated to drive independently. The criticism of formal training in the United States is that specific crash-reducing skills are not taught. Insufficient attention is paid to the importance of motivation in applying new skills or the overconfidence that may result from skills acquisition. Furthermore, lifestyle factors related to risky driving and the development process are not addressed. Overall, young drivers are treated as a homogeneous group rather than as individuals (National Highway Traffic Safety Administration, 1997). The U.S. experience suggests the need for development of the contents of driver education. The problem of goals and contents of driver education is the question of the validity of activities. This validity question can be phrased as follows: Do educational models provide novice drivers with the skills, knowledge, and motives that they need in the real world to cope with problems?

4.2. Graduated Driver Licensing and Accompanied Driving The reduction of accident liability of novice drivers during the first years or even months after licensing is often interpreted to be a function of quantity of practice. The same basic idea, guarantee enough driving experience in a safe environment, is the main motivating factor behind GDL, accompanied driving, and layman instruction. The second idea behind these driver education methods is to make the learning period a long one, thus increasing the age of new drivers when fully licensed. Rapid reduction of accident liability of novice drivers after licensing has been found in several studies (Maycock et al., 1991; Mayhew, Simpson, & Pak, 2003; Sagberg, 1998). Maycock et al. emphasize the effect of experience in accident reduction, but age also has an independent and similar effect. Rapid accident reduction with mileage after licensing could be interpreted as lack of sufficient experience before licensing. If the mileage before licensing were greater, the accident reduction after licensing could start from a lower level. Another possible explanation is based on reducing (extra) motives at the beginning of solo driving

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when the driver has the chance to feel “independent” and “grown up.” He or she can satisfy his or her motives by speeding and testing his or her limits, for example. There has been little explicit analysis on the experience effect in driving. It seems that the main hypothesis is based on achieving automatism of necessary skills. Automatic performance is less error prone, is not disturbed by intervening factors (e.g., fatigue or pressure), and requires less attention and thus demands less information processing capacity than non-automatic performance. Cognitive workload is reduced when automatism takes place. Gregersen et al. (2000) reported a decreased workload for drivers who had started practicing at age 16 years compared to other drivers with less practice. Quantity of training refers explicitly to the amount of training but does not tell anything about contents or it does not referee what the “experiencing person” has learned from the experience. As a pedagogical element, quantity of training remains rather obscure if it is not connected to the quality or contents of training. Quality and quantity are not necessarily associated with each other. Nevertheless, an increase in the quantity of training is presented as one method for improving the skills of novice drivers. Duration of training can be associated with quantity, but it may simply mean that training occurs over a long time period. For example, training may be distributed over a long time but not intense (Sagberg, 2000). However, duration also has some independent properties. One relevant assumption is that better learning results can be achieved by distributing the available teaching resources over a longer time period. The principle of spaced training versus massed training has been studied widely in pedagogy. The results show generally better learning results with spaced practice (Dempster & Farris, 1990). Better results are also obtained in the area of learning motor skills (Shea, Lai, Black, & Park, 2000). Distributing exercises over a period of time enables better processing of experiences. Other mechanisms may be better targeting of attention and development of memory traces. Another advantage of spaced practice is the possibility to combine different pedagogical methods. For example, the learner may be given independent learning tasks between training sessions. There are two ways to increase the duration of training: start training early (16 years) and grant license when 18 years or start later and also continue later in the multiphase model. The underlying idea behind lay instruction is that driving is a skill that can be learned by practicing by oneself. The role of the lay instructor is not exactly that of a teacher, but he or she takes care of safety, tutoring, and perhaps providing feedback. The idea of “learning by doing” is prevalent, and the role of theoretical aspects in driving skill is secondary. This is reflected in the fact that theoretical education is usually not strictly controlled.

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In this chapter, the terms lay instruction, accompanied driving, and nonprofessional education are used interchangeably. There is no clear distinction between the concepts; however, lay instruction and nonprofessional education are referred here when the emphasis is on training the learner in the basics of driving skill. This is the case when the lay instructor is preparing the learner for the driving test and the responsibility for learning is on the lay instructor. The role of the lay instructor is more like an accompanying person, such as in the French AAC model. A professional instructor teaches the basics, and a lay instructor accompanies the student while practicing and ensures safety. This is the case also in the new German system (AD17), in which teaching is especially forbidden. In Sweden, Norway, and the United Kingdom, the role may vary during the process. In the beginning, the role may be more like an instructor’s role, and it changes later to the role of an accompanying person. Lay instruction is widely accepted as a part of training or as an alternative to training in driving school. Lay instruction is an essential part in models that aim to extend learning periods (L17 in Austria, AAC in France, starting practice at age 16 years in Norway and Sweden, and AD17 in Germany). The pedagogical idea underlying the extended learning period is that driving is a task that requires both technical and motivational skills. This is why the beginning of the novice driver’s career should take place as a prolonged protected learning period. This is the basic idea in GDL (Waller, 2003). In addition to control of the vehicle and mastery of traffic situations, the driver should be familiarized with risks caused by traffic and him- or herself and to be motivated to avoid these risks. Extended learning periods are being increasingly used in driver licensing in Europe, but not in the same way as GDL. Such countries as Sweden, Norway, France, and Austria have offered the possibility to start training as early as age 16 years. In addition to increased driving experience, the driver may be slowly growing to become a mature and safe driver. Driving after licensing may no longer be a new and exciting activity. The training environment also has effects on learning: It affects how effective learning is and what skills the learner learns. There is a conflicting situation in driver training. Learning to drive in an urban area makes driving later more comfortable and easier and may also save money in the form of minor accidents. Driving in an urban area also gives the feeling that learning is taking place and that it is important to practice to handle traffic situations. On the other hand, serious accidents, fatalities, and serious injuries often take place outside urban areas on rural roads and highways. Driving there may be experienced as less demanding, and in driving school training (at least in Finland) not much time is spent there. In lay instruction,

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more time is used outside urban areas on rural roads (at least in Finland, Sweden, and France) and during trips to far-away destinations. It is also important to be able to drive in one’s own living environment. However, the problem is that it may not always be challenging enough. Students in both a Swedish study of 16-year-old drivers (Gregersen et al., 2000) and the German evaluation study of the AD17 model more often lived in the country and in small towns (Schade & Heinzmann, 2009). At least in Sweden, novice (started driving at 16 years of age) drivers’ accident features (single-car accidents and animal accidents) indicated that drivers had been driving more around their own living environment (Gregersen et al., 2000). However, it is self-evident that in accompanied driving there are greater possibilities to select and practice in different kinds of environments. However, it may be that accompanied persons try to avoid the most difficult conditions (nighttime, slippery conditions, and rush hour) for their own safety, and doing so prevents students from gaining experience in difficult conditions. Here, the climate of training refers to the attitude of the instructor toward other participants in traffic or toward safety or even his or her motives concerning driving and driving style. It is possible to speak also about driving culture, which is taught to the candidate during the teaching and learning process. Driving school instructors have to concentrate more on content that is assessed in examination, whereas accompanying laypersons may also give other kinds of feedback. They may support cultural traits that belong to their own repertoire of driving. This can be an advantage or a disadvantage. Whether it is an advantage or a disadvantage depends on the accompanying person’s own values, attitudes, and motives. He or she may be safety promoting but may also promote a fast and aggressive driving style. In the German driver education system (AD17), up to two-thirds of accompanying persons were novice drivers’ mothers (Schade & Heinzmann, 2009). There are at least four interesting findings here. Children, of which 40% were boys, who practiced driving with their mothers have a good relationship with their mothers. It may also be possible that “female driving style” increases youngsters’ safety later in their driving careers. Also, children who select or accept their mothers to be their accompanying person may have the same kind of values with regard to driving already at the beginning. Finally, mothers may serve as accompanying persons for practical reasons: Perhaps they have more time to do so than do their husbands. In driving school, it is possible to create a positive and encouraging learning climate, but there is insufficient time to examine attitudes. However, in accompanying driving there is at least the possibility to create such a positive and much longer lasting learning climate, which may

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encourage the student to also learn attitudinal and other cultural matters. It is not as important for the lowest level of driving hierarchy, but at the level of traffic situations, these cultural and attitudinal contents are important, and they become even more important further up in the hierarchy.

4.3. Combined and Multiphase Models Some driver education models combine lay instruction with professional instruction. Explicit combined models exist in France, Austria, and Germany. Learning in these models often starts with professional instruction that follows a curriculum and aims at basic knowledge and skills for the learner to be practiced further with a lay instructor. Professional training is extended with practical experience gained with lay instruction. There may be lessons in driving school also for lay instructor. The role of professional instruction is to give formal information and to guide and structure training. In the German model (AD17), the candidate must pass full-scale driver training in driving school (at the age of 17 years), after which he or she is allowed to drive accompanied until the age of 18 years and final licensing. In practice, combined models exist also in other countries, in which the system is more liberal. For example, in the United Kingdom and Sweden, a vast majority of the candidates do not rely only on lay instruction even though it is allowed. However, the amount, timing, and content of professional training are left open. Multiphase models refer to models in which a compulsory further training after preliminary licensing is required to get a permanent license. Two-phase models are in use in Austria, Finland, Luxembourg, and Switzerland. Training systems that include two or more phases are under discussion in several countries, and studies are being performed in Europe (Sanders & Keskinen, 2004).

5. HOW EFFECTIVE ARE DRIVER LICENSING MODELS IN PRACTICE? At the beginning of car driving, the aim of driver training was to help the new car owner to maneuver the car. Then, teaching started also to concentrate on traffic rules and traffic situations. However, safety as one of the main goals of driver education emerged quite late. For a long time, the general aim of driver training was to prepare the student for the driver examination. Today, when the effects of driver education are evaluated and described, it is self-evident that safety is most important. One of the most difficult tasks in traffic psychology is the evaluation of the effectiveness of different driver licensing. The safety aim of driver education programs is usually to decrease the number or risk (compared to

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mileage exposure, population, or driver licenses) of fatalities or serious injuries among novice drivers at the beginning of their driving career. However, there are many problems in such evaluations. First, accidents, especially serious accidents, are rare. Berg (1994) reported that approximately 1% of novice drivers are involved in serious accidents during the first driving year. Second, accidents are multicausal phenomena: There are usually many factors affecting the causation of accidents. In addition to safety effects of driver education programs, there are other measures that can show how effective a program is in practice (Hatakka, 2003; Keskinen & Baughan, 2003). The most important criteria for success is of course safety, but other criteria include learner’s satisfaction with training, immediate learning effects (passing rates on examination and quality of mistakes on examination), novice drivers’ attitudes and their behavior in traffic when fully licensed (offenses and other measures of driving style and the amount of driving), and safety compromising results of driving behavior (number and severity of accidents) and the time of accidents (during learner stage and intermediate stage or when fully licensed). The evaluation studies in Europe are mainly focused on accidents that occur when drivers are fully licensed. When evaluation studies of GDL have been performed, the interest has been in accidents during training. Reliable evaluation is difficult because it is rarely known if the process of education has succeeded and especially how the process has been realized on an individual level. The process of education has several steps. First, there is possibly an official curriculum or at least goals for driver education. Second, teachers understand the goals in their own way and try to teach them to students. Third, students understand the goals in their own way and learn something according their understanding. Then, after having the opportunity to drive individually, new drivers behave in their own way (exposure, speed, car choice, driving tasks, etc.). Finally, novice drivers may get into an accident or not, and the accident may be serious or even fatal. Only in rare cases have researchers been interested in this process and how the message in official curriculum is changed during the education process. Katila et al. (1996) found in a Nordic comparison concerning slippery road training that regardless of similar official goals for training and regardless of what teachers described they were stressing in their teaching, students got quite a different view of important factors for safe driving in slippery road environments. In the HERMES project, in which the intention was to use a coaching method supporting learning to make students more responsible for their learning and later for their driving, an unexpected result was found. According to students, the new coaching method fit better to more mature female students and students with higher education and not so well to younger and less educated

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males. However, younger and less educated males have a higher accident frequency in traffic (HERMES, 2010). There are many reviews on the effects of driver education (Christie, 2001; Engstro¨m, Gregersen, Hernetkoski, Keskinen, & Nyberg, 2003; OECD, 2006; Senserrick & Haworth, 2005). GDL has inspired much research in the United States, Canada, and Australia, but its variants, such as accompanied driving, have also been evaluated frequently (France, Sweden, Norway, and Austria). The second group of programs that has been evaluated contains different kinds of multiphase or multielement programs (Finland, Austria, and Germany).

5.1. Effectiveness of Graduated Driver Licensing The January 2003 special issue of the Journal of Safety Research was devoted to GDL. The first 12 papers, which were written for and presented at the GDL symposium in 2002, provided a comprehensive review of research as of that date on teenage driver issues in general and GDL in particular. Hedlund, Shults, and Compton (2003) and Hedlund and Compton (2005) summarized the findings of the earlier results concerning the effects of GDL on safety. Later, Senserrick and Haworth (2005) published an excellent review of driver training and licensing systems. Hedlund et al. (2003) showed that GDL programs are effective regardless of their specific details; even GDL programs and their evaluation methods differ substantially across jurisdictions. According to the authors, GDL reduces teen driving and improves driving knowledge and behavior. Begg and Stephenson (2003) and Lin and Fearn (2003) showed that GDL programs that require a learner’s permit to be held for some minimum period of time delay the age at which a young driver obtains a license to drive without supervision. The same mechanism, reduction of driving, is, according to Hedlund et al., producing a substantial reduction in nighttime crashes. McKnight and Peck (2003) argued that extended learning can substantially reduce accidents if well structured and highly controlled. Long-term effects of GDL were still scarce in 2002 when the symposium was held (Hedlund et al., 2003). Hedlund and Compton (2005) studied several overviews of GDL (Engstro¨m et al., 2003; Senserrick & Haworth, 2004), and they also offered a description of what is going on in the area of evaluation research and implementation of GDL. The main results from seven jurisdictions and several studies can be summarized as follows: A reduction in accidents occurred in almost all studied jurisdictions when drivers were 16 or 17 years old. Larger reductions occurred when the number of accidents was compared to population, but there were smaller reductions when they were compared to licenses. There was evidence

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that the number of licenses decreased after GDL (Hyde, Cook, Knight, & Olson, 2005). Vanlaar et al. (2009) published a study that used a metaanalytic approach to GDL programs in North America. They found strong evidence in support of GDL. GDL had a significant positive impact on the relative fatality risk of 16-year-old drivers. However, no evidence was found to suggest GDL has had an overall impact on the relative fatality risks among 17-, 18-, and 19-year-old drivers when considering only the summary effects. This is in line with the findings of Mayhew, Simpson, Williams, and Desmond (2002), who found positive effects (accident reduction) for drivers aged 16 and 17 years and also for 18-year-olds. However, in the case of 18-year-old drivers, this effect was found only in the learner phase when they were driving supervised compared to driving unsupervised. The positive effect of 18-year-old drivers diminished rapidly after licensing, and crash rates increased during the following 2 years. There was also no long-term effect found for the program (Nova Scotia, Canada) after 16- and 17-year-olds graduated to full license. Much of the improvement for 16and 17-year-olds occurred during restricted night hours. Vanlaar et al. (2009) also reported other interesting findings, and some of the results that they did not get and report are also interesting. They found specific effects according age groups. For drivers aged 16 years, it was safer for them to not take passengers during the intermediate phase, no matter if they were family members or other persons. In other age groups, there was no such connection. In the 18-year-old age group, having mandatory driver education during the learner phase increased safety. Usually, the result is the opposite. Two factors may have influenced this result. First, the effect appeared only in the 18-year-old group and not in the whole population. It is possible that mandatory driver education as part of the learner phase improves drivers’ driving safety because they are older. Second, there is convincing evidence that if formal or school-based driver education is used to get unsupervised driving at an earlier age, then safety will be compromised (Mayhew et al., 2002). What the result does not indicate, however, is that school-based driver education as such would be harmful. It may only indicate that those who select it (if not obligatory) often have stronger motives to start driving unsupervised, and in this way they may differ from others. Vanlaar et al.’s results described the situation in which driver education was a mandatory part of the learner phase. However, as the authors noted, having a mandatory driver education in intermediate phase for 19year-old drivers did not influence safety in a positive way. Vanlaar et al. (2009) focused on the significant findings only. However, there are interesting independent variables that they used in their analysis but that did not give any significant results. Some of the results may be “missing” because the authors analyzed their material by age groups.

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At least some age-based questions remain unanswered, such as the influence of minimum entry age for learner and also intermediate stage. Also, the minimum and maximum length of a mandatory holding period in the learner stage made no difference, which may also be associated with the use of age groups. However, independent variables not associated with age and still important, but in this study without any influence, are minimum hours of supervisory driving required in the learner stage and the question of mandatory supervised hours at night. One conclusion could be that these variables are not important and that the effects of GDL are mainly based on restrictions and as a result perhaps reduced exposure (Shope & Molnar, 2003; Williams, 2007) and not so much on what is learned during the program. However, it is surprising that the Insurance Institute for Highway Safety’s ratings of the quality of the GDL programs (divided into three classes) did not have any effect. This raises the question of how important are the differences between the programs. Also, an evaluation of GDL (Karaca-Mandic & Ridgeway, 2010) argues that GDL policies reduce the number of accidents of 15- to 17-year-olds (police-reported fatal and nonfatal collisions) by limiting the amount of teenage driving rather than by improving teenage driving. This prevalence reduction primarily occurs at night, and stricter GDL policies, especially those with nighttime driving restrictions, are most effective. The authors also found that teen driving quality does not improve ex post GDL exposure: GDL policies do not make teenagers better drivers in later years. McCartt, Teoh, Fields, Braitman, and Hellinga (2010) reached a similar conclusion: Graduated licensing laws that include strong nighttime and passenger restrictions and laws that delay the learner’s permit age and licensing age are associated with lower teenage fatal crash rates (fatal crashes among 15- to 17-year-olds). However, contrary to the findings of Vanlaar et al. (2009), McCartt et al. (2010) found a difference between poor and good rated licensing lawsdas much as 30%. The differences between poor and good rated licensing laws are mainly in the amount of restrictions: The good ones have more restrictions than do the poor ones.

5.2. Effectiveness of Accompanied Driving First in France (1987) and then in Sweden (1993) and Norway (1994), a driver education program was begun that was partly related to the ideas of GDL and lowered the student age to 16 years. This licensing system received the general name accompanied driving, and the aim was the same as in GDLdto offer driver students a long protected practicing period. Also, because in most European countries the minimum age for full license is 18 years, it was necessary to start practicing early enough to guarantee

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sufficient experience for a novice driver before full license. In France (Page, 2000), in addition to driving with an accompanying person, the student and accompanying person had to receive instruction from a driving school. In Sweden and Norway, it was not necessary; obtaining experience with the accompanying person was sufficient, although many students also took lessons in driving school. In Finland, a layman can get permission from authorities to teach usually his or her son or daughter to drive. However, in Europe there seems to be a growing interest to favor accompanied driving as a teaching method (Twisk & Stacey, 2007), although the evaluation results of the effectiveness of this method are not convincing. After several evaluation studies, the only success story has been the Swedish accompanied system (Gregersen et al., 2000). In France (Page, 2000, Page, Ouimet, & Cuny, 2004) and Norway (Sagberg, 2000), no safety benefits were found after accompanied driving when a new driver started to drive independently with full license. The evaluation results in France were disappointing (Page et al., 2004), as were those in Norway (Sagberg, 2000). There was no reduction of accidents after novice drivers obtained their full license. In Norway, it was even found that accidents during the accompanied period increased (Sagberg, 2000). When Sweden lowered its age limit for practicing with an accompanying person to 16 years, the evaluation results were positive (Gregersen et al., 2000). Practicing increased and accident figures declined as much as 15%. Compared to the original French model, the accompanied training in Sweden was very liberal: There was no compulsory cooperation between accompanying person and professional driver teacher. Later, Sweden introduced a short compulsory briefing for all who start accompanied practicing. However, the favorable results may have been affected by self-selection bias. Although the choice of taking accompanied driving from 16 years is a free one, learner drivers from higher social groups were overrepresented. Such confounding factors as socioeconomic status and licensing age were controlled, but of course there was no possibility to control the reasons for the self-selection. Austria introduced a new program (L17) for driver education in 1999. Candidates for a category B driving license can start at the age of 16 years in a driving school with standard theoretical and practical requirements. Then, education continues with a parallel professional and layman practical training period (minimum of 3000 km of practice), concluding with perfection training in the driving school again. When 17 years old, drivers can take the test and drive solo at 17 years, instead of the usual 18 years. The evaluation results concerning Austrian L17 model are mixed. There are positive (Winkelbauer, Christ, & Smuc, 2004) and negative (Bartl & Hager, 2006) results. According to the Winkelbauer et al. study, L17 drivers

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committed less traffic offenses than traditionally educated drivers, but no significant difference in accident rates was found in the Austrian central license file between L17 drivers and traditionally educated drivers. Contrary to the data from the central license file, a significant difference between the groups was found with regard to self-reported accidents: L17 reduces the number of accidents, particularly in the second and third year after licensing. More indepth analysis shows that male drivers are responsible for this lower number of accidents (Winkelbauer et al., 2004). However, the data largely refer to minor accidents involving material damage. In the study by Bartl and Hager (2006), substantial samples of L17 drivers and traditionally educated drivers were interviewed as well, but this study did not find positive results.

5.3. Effectiveness of Multiphase Driver Education Programs Multiphase models are also popular in Europe. The simple idea is to mix learning in driving school with the learning in independent driving. The second or even third phase is further education after getting permission to drive independently. These kinds of models were first introduced in Norway (1979) and Finland (1989) and later in countries such as Luxembourg and Austria. Usually, the second phase of education is focused on driving in difficult conditions, but increasingly it is also focused on selfassessment and hazard perception. Research results on the influences of multiphase driver education programs vary between countries and between studies. In the Austrian multiphase model (since 2003), all learner drivers must complete a track-based safe driving course, a psychological group discussion, and two feedback drives with a driving instructor in the first year after gaining a license. According to Bartl and Esberger (2005), there was a clear reduction in the total number of accidents involving personal injuries and fatalities among 18- and 19year-olds. An 11% reduction in accidents among 18- and 19-year-olds was found when the first half of 2005 (after introduction of the multiphase system) was compared with the first half of 2003 (before the full introduction of the multiphase). In contrast, during the same period, accidents decreased by 2% in all other age categories. However, there were weaknesses in the study design. Also, an evaluation study of the multiphase system in Austria (Gatscha & Brandstaetter, 2008) suggested promising results regarding serious accidents among young, novice drivers. In Finland, novice drivers gain a preliminary license once they have passed the driving test, and within 6e24 months all drivers in Finland have to follow a second phase of driver training. A full license is granted thereafter. The second phase training takes place in an authorized driving school and is divided into three parts: analysis of personal

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driving skills and style (on-road feedback drive), track training units, and classroom training. There is no second test. Several evaluation studies have been conducted throughout the years (Katila et al., 1996; Katila, Keskinen, Hatakka, & Laapotti, 2001; Keskinen et al., 1992; Laapotti & Keskinen, 1998; Laapotti, Keskinen, & Rajalin, 2003; Pera¨aho, Keskinen, Hatakka, & Katila, 2000), but it is not possible to make a clear-cut conclusion regarding the effects of the second phase in the Finnish national curriculum. However, the results do not indicate any drastic decrease in accidents. One of the reasons may be that contrary to some other studies, such as Gregersen’s 16year-olds study (Gregersen et al., 2000), Willmes-Lenz et al.’s AD17 study (Willmes-Lenz, Pru¨cher, & Grossmann, 2010), and Winkelbauer’s L17 study (Winkelbauer, 2004), which seemed to produce large reductions in accidents, the students in Finnish studies were not self-selected. The national curriculum is compulsory for all driving students in Finland. This concerns even students who select the accompanied way of driver education. The study by Laapotti et al. (2003) (questionnaire study; N ¼ 9,305; rate of return, 48%) provides some interesting results concerning younger and older novice driver accidents at the beginning of the driving career (maximum 4 years of driving). Because all drivers in the sample were novice drivers but differed by the age when they received their full license, it is possible to see how even minor age differences (motivational differences?) result in differences in safety. The usual sharp decline in accidents (accidents/ 100 drivers) at the beginning of independent driving could be found only from the younger age groupdage 22 years or younger. There was no such decline for drivers 23 years or older. This difference is important in at least two ways. Those who selected to go to driver education approximately 4 years later than the lowest age limit of 18 years form a self-selected group. They differ by their age, but they may also differ by other important variables, such as motivation to drive, which again has effects on driving safety. The second finding is that it is possible to start a driving career without a period of high risk. The German model, AD17 (accompanied driving at the age of 17 years after driving school and driving test until the age of 18 years), has been the subject of several studies (Funk et al., 2009a, 2009b; Schade & Heinzmann, 2007, 2009; Stiensmeier-Pelster, 2008; Willmes-Lenz et al., 2010). Contrary to most GDL studies in North America, evaluation studies concerning AD17 have focused on time after full licensing (drivers age 18 years), and the followup periods have varied between 3 months (Schade & Heinzmann, 2007) and 18 months (Stiensmeier-Pelster, 2008). The outcome data (accidents and offenses) were collected from the Central Register of Traffic Offenders and with a questionnaire study that was repeated four times.

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The number of subjects varied from study to study (including self-reported accidents and offenses but also mileage), but there were always at least 1000 subjects in both comparison groupsdAD17 and conventionally trained novice drivers. The main results revealed that AD17, according to the researchers, was a success. Drivers who had participated in AD17 training had 15e17% fewer accidents and also fewer offenses than conventionally trained novice drivers (Willmes-Lenz et al., 2010). Based on this success, the model became the dominant form of driver education among 18-year-old novice drivers in 2007, and it was adopted by all federal states by 2008. According to the questionnaire study (Willmes-Lenz et al., 2010), subjects who selected the AD17 education more often came from better educational or economic backgrounds. The average duration of actual accompanied driving was between 7 and 8 months. Participants drove on average 2,400 km (1,491 miles). Duration of the accompanied phase was not associated with later accidents, but the amount of practicing was: Those who had driven less than 1,000 km (621 miles) had more accidents per million kilometers than did those who had driven more than 1,000 km. The authors should have also reported how many percentage of those who drove less or more than 1,000 km had accidents because comparison of accidents per kilometer is not a reliable measure if the kilometers driven differ significantly. It is not easy to summarize the findings of the multitude of evaluation research during the past few decades on both sides of the Atlantic Ocean. However, something can be said about the quality of research designs in evaluation studies, and something can be said about the general results. Research designs have mostly been beforeeafter types or intervention group/control group comparisons. No experimental studies concerning driver education have been published recently. Beforeeafter designs compare, for example, accidents before and after the renewal of the training system, and often the data have been collected from official statistics. Sometimes, this has been done even without knowing the number of novice drivers before and after the renewal. Use of absolute numbers gives us the important information of how many casualties have occurred on the roads during a certain time, but they do not give us any hint regarding why there are such (possible) reductions. Such results tell us nothing of the safety effects of training that could be based on learning effects. Reduction of accidents may be caused by reduced exposure. Use of only beforeeafter designs makes it almost impossible to determine which elements in training are effective if all new drivers have to practice according to the new system. Comparison between an intervention group and another, often normally or traditionally educated group has a danger

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that may ruin the whole studydself-selection. As in experimental studies, experimental and control groups are used to determine intervention effect, and the leading principle is that experimental and control groups have to originate from the same population. The only difference should be the intervention to which the experimental group is exposed. When a group of students selects a certain kind of driver training system instead of another one, there is already a difference between the groups. Also, the difference remains, even if there are no differences between the groups in age, sex, socioeconomic level, or earned grades in school. We can try to control these kinds of variables, but we cannot control the original difference in selection. When Glad (1988) in Norway made his evaluation concerning phase 2 in Norwegian driver education, he had real control and experimental groups. In some areas of Norway, the renewal was put into effect earlier than in others, and it was then possible to compare groups that were not volunteered in a different way. Regarding North American and European studies of affectivity of methods, on the basis of GDL studies, the system has been successful in preventing accidents for 16and 17-year-olds, but after full licensing GDL does not produce safer drivers. In Europe, the research has focused on drivers with full license, and the results of different types of training systems are mixed. One thing is clear: Simply increasing technical skills does not help new drivers to drive safer; often, the opposite occurs. Also, simply increasing driving experience does not help new students after licensing either. However, in the past few years, there has been increasing interest in using specific goals and methods to reach the major goals. The GDE model has been widely implemented as an ideal model for driver training (Twisk & Stacey, 2007).

6. GOALS AND CONTENTS OF DRIVER EDUCATION: GDE The GDE-framework was first published by Hatakka et al. (2002). The four-level hierarchy (Keskinen, 1996) was expanded within the scope of the EU-funded project GADGET (1999) to a framework by dividing the four hierarchical levels into three columns concerning (1) knowledge and skills, (2) risk increasing factors, and (3) self-evaluation (self-assessment) skills. Thereby, it became possible to create a structure for defining what should be focused on in driver training. The cells in the GDE framework are used for defining detailed competencies that are needed in order to be a safe driver. It is a description of driving in general, and it is not suitable for explaining the behavior of some particular driver and is not “a theory” that can be tested against empirical material; even the parts of the whole framework

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are based on empirical findings. Pera¨aho et al. (2003) described the GDE framework in the following way.

6.1. Central Contents of Driver Education 6.1.1. Knowledge and Skills The first column (“knowledge and skills”) in the framework describes what a good driver needs to know at each level in order to drive a vehicle and cope in normal traffic circumstances. This includes how to maneuver the car, how to drive in traffic, and what rules must be followed (lower level skills) and how trips should be planned and how personal preconditions influence behavior and safety (higher level skills). The term “knowledge” encompasses both practical and theoretical knowledge. Regarding the two highest levels, the aim in driver training should be to introduce the driver to the concept of driving as a skill that goes beyond the interaction between man and machine. Success or failure thus follows mainly from a person’s motivational characteristics and the driving strategy he or she chooses.

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vehicle control and maneuvering. Risk awareness exercises, on the other hand, are designed to increase knowledge, experience, and recognition of dangers on the road. The message and focus are entirely different in these two types of exercises, although they resemble each other.

6.1.3. Self-Evaluation, Self-Assessment, and Self-Awareness The third column (“self-evaluation”) is a central and essential element in modern pedagogical thinking. Selfevaluation might be defined as a process whereby an individual tries to get feedback on his or her personal actions from within the self. In the context of driving, it is a matter of becoming or wanting to become aware of personal preconditions and tendencies as well as skills and abilities regarding maneuvering, coping in traffic, planning of driving, and life in general. Self-evaluation is seen as an important tool not only in driver training but also in development of driving skill after training. Research on the development of expertise shows that meta-cognitive skills and reflective thinking are essential characteristics of an expert (Kolb, 1984; Mezirov, 1981).

6.1.2. Risk Increasing Factors The second column in the framework (“risk increasing factors”) is closely related to the first column, but it emphasizes particular knowledge and skills related to factors that increase or decrease risk. The content in the second column stands in its own right because of the importance of these factors for safety. In practical driving school education, they must be integrated into teaching of general skills and knowledge. Typical risk factors are emphasized and described in more detail. The risks are different on different levels of the hierarchy. The frequently used concept “hazard perception” is a good example to be analyzed. By using the GDE framework, it is easy to see that the traditional idea of hazard perception as “road-craft” appears rather limited. Several studies have shown that on a general level, deliberate risk taking, violation of rules, underestimation of risks, and overestimation of personal abilities are common features of young drivers, especially young males (Jonah, 1986, 1990; Keskinen, 1996), and that such behavior lessens with age. However, as Jessor (1987) and Twisk (1994a) noted, this type of behavior also has a functional dimension as a part of the maturation process toward adulthood. Driver education should be able to address both these dimensions, a task in which it has not succeeded fully in any country, as evidenced by the speeding and drinkdriving accidents of young male drivers. Regarding driver education, a distinction should be made between training of skills and training of risk awareness. Skill-based training is primarily about learning

7. FUTURE OF DRIVER EDUCATION AND TRAINING The main message throughout this chapter is that (1) driving skill may be conceptualized as a broad set of skills that are used according to drivers’ goals and motives, and (2) from this arises a need for versatile use of pedagogic methods. No single theory or method can be expected to cover all levels of the hierarchy of driving behavior. The goals of education and training and the level that is being focused on should determine the optimal learning method. The key to the higher levels in the hierarchy and to an increase in self-evaluative skills lies in the activity of the learner him- or herself. The recent trends in pedagogic theories emphasize a constructivistic approach to learning, problem-based learning, and experiential learning; that is, learning evolves through the learner’s own activity, making active use of personal experiences. Fairly simple training methods, traditional lecturing, repetition, and memorizing (e.g., traffic rules or traffic signs) probably produce good results for the lower levels of the framework. However, these methods can be improved by good feedback, such as connecting a certain traffic rule to the wider context by discussing its role for safety. In summary, the following aspects should, in light of the hierarchical approach and the GDE framework, be borne in mind in driver education (Pera¨aho et al., 2003): l

Skill training should be balanced with risk awareness exercises.

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l

l

l

l

l

l

l

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Teaching skills, acknowledging the risks involved in these skills, and self-evaluation of the personal aspects of these skills and risks should alternate and complement each other. The curriculum as well as the training should cover all four levels in the hierarchy. Driver training should promote the view of driver behavior as a self-paced, multilevel task. Although vehicle maneuvering skills and skills for mastery of traffic situations are the basis for success in traffic, these skills should be connected to the higher levels and trained in such a way as to avoid any negative effects. The driver’s task is not only a complex psychomotor challenge requiring lower level psychomotor skills and abilities but also an operation (safe or unsafe) that is related to the driver’s goals, motivation, and strategic planning, as well as skills in self-control. The highest levels in the hierarchy are not accessible through teacher-centered methods such as lecturing or simply by increasing the amount of training. Active learning methods are needed that make use of the learner’s own experiences. Training of self-evaluative and metacognitive skills should be included. This provides an opportunity for developing expertise after training and for attaining and modifying motives and goals on the highest levels of the hierarchy.

The main message of the GDE framework is that in addition to the training of basic skills, driver training should also address a driver’s motives and goals related to different aspects of driving, such as skills for dealing with social pressures during a trip. Skills for vehicle maneuvering and mastery of traffic situations are basic requirements for successful operation in traffic. However, if the connection between these skills and the motivation to use them is not made, the effect of education may be opposite to that desired. If the motivational level fails to produce a safe strategy for driving, no level of skill in mastering traffic situations or vehicle handling is high enough to compensate for this lack of safety orientation.

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Chapter 30

Persuasion and Motivational Messaging David S. Anderson George Mason University, Fairfax, VA, USA

1. OVERVIEW Traffic safety is commonly promoted through the standard three-pronged approach using engineering, enforcement, and education. Each of these elements has an important role with the desired outcome of safer communities vis-a`-vis traffic safety. The emphasis of this chapter is primarily on the “E” of “education”, with specific focus on how to ultimately maximize safe behavior by individuals. The chapter is designed primarily for program planners, researchers, and others orchestrating meaningful strategies that result in safer behavior. Key to this understanding is identifying ways in which those communicating traffic safety messages can do so in ways that are persuasive and motivational, toward the desired outcome of safety-oriented behavior. This chapter addresses what we know about “what works,” what would be appropriate for maximizing impact, how we can best allocate our limited resources, how we build upon prior learning, and what we should do to plan reasonable strategies. This chapter is designed to assist program planners and other intermediaries with a range of communication strategies so that what they want to achieve through these efforts has a greater likelihood of, in fact, being achieved. Whether the aim is increased use of safety belts, less impaired driving, proper use of child safety seats, or heightened awareness of the importance of driving in a courteous manner, persuasive communication can aid in achieving these ends. When thinking of communication strategies, it is vital to consider a wide range of approaches, including, but not limited to, brochures, posters, public service announcements, interviews, billboards, media campaigns, and workshops. Thus, this chapter focuses on similar foundations appropriate for the various approaches and is not limited to traffic safety campaigns such as “Click It or Ticket” or “Friends Don’t Let Friends Drive Drunk.” An important first step in understanding how to engage in persuasive strategies is reviewing the professional literature. This is followed by an outline of suggested strategies and processes to aid in individual efforts or a series of strategies to enhance traffic safety from the educational or human standpoint (i.e., not the “engineering” or “enforcement” Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10030-X Copyright Ó 2011 Elsevier Inc. All rights reserved.

segments). An important caveat at the outset of this review is the lack of any guarantee that the desired results will actually be achieved; nonetheless, attention to the processes outlined is viewed as increasing the likelihood of obtaining positive outcomes.

2. REVIEWING CURRENT RESEARCH A review of the professional literature on the evaluation of traffic safety communication initiatives reveals limited conclusive evidence regarding communications initiatives on traffic safety. Some varied findings are found in the literature, although no overwhelming evidence is found that suggests that the campaigns on traffic safety are necessarily effective. From the reviews and evaluations conducted and reported in the literature, it is apparent that evaluation of traffic safety communication efforts is limited. Although specific studies are cited in this section as well as throughout this chapter, there are several overall highlights. First, traffic safety communication efforts are often well-intended but not designed with an aim of conducting evaluation, whether rigorous or simple in nature. The literature states that conclusive studies about the effectiveness of these efforts are lacking. For example, Wundersitz, Hutchinson, and Woolley (2010, p. iv) report that “due to the lack of scientific evaluations, generally poor methodological designs, confounding factors, and lack of documentation of campaign activities, it was difficult to determine what elements of the road safety mass media campaigns were effective.” Whittam, Dwyer, Simpson, and Leeming (2006, p. 616) cite six criteria as important for a successful traffic safety campaign and further report finding “no published evaluations of a driving-safety campaign including all six criteria.” A second issue found in the literature highlights the importance of traffic safety campaigns targeting a specific group of individuals. Several studies (Berg, 2006; Goldenbeld, Twisk, & Houwing, 2008) highlight how males and females react differently to some campaigns. Others cite ways in which youth respond to messages, such 423

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as how fines, speed cameras, and alcohol breath testing can affect risky driving (Ramos et al., 2008). Youth, however, are not all the same; with motorcycle drivers, different strategies are necessary, based on the psychological factors of impatience, sensation seeking, and amiability (Wong, Chung, & Huang, 2010). Tay and Ozanne (2002) showed that fear-based public health campaigns, although used extensively, did not reduce the fatal crash rate of the primary audience of the campaign (young male drivers). The important factor is to target groups based on their needs and how they perceive the world around them, as well as what might work best for the specific group on the identified issue. In other words, a global, universal campaign should not be expected to be successful due to the different needs and response patterns of various constituencies. A third theme found in the literature is that many program planners are involved in a variety of efforts seeking to accomplish a desired end of improved traffic safety. They seek to achieve their results through multiple strategies, including communications efforts but also other elements, such as policy change, enforcement, or environmental modifications. The result is that it becomes difficult to specify the relative contribution of the communications initiatives. The resulting change in individual behavior may be the result of a policy change, which was in turn affected by the communication strategy (Yanovitzky & Bennett, 1999). Thus, the communication strategy may have been successful in reaching a policy maker, who then achieved a policy change that had the ultimate impact on the public’s behavior (rather than the communication strategy having a direct effect on behavior). Related to this is the tendency to look at the ultimate or long-term outcome of behavior change. What may be more appropriate, according to the literature, is to examine the proximate results that can be directly attributed to the communication effort. This is in contrast to the primary focus on the ultimate behavior change (of no red light running, no speeding, no impaired driving, etc.). This leads to the last element from the literatured complexity. Human behavior change is extremely complex, and trying to specify the elements that make a difference in individuals’ behavior is a challenge, even when focusing on a specific subgroup. Furthermore, making change that is lasting requires maintaining a perspective of the broader cultural context; McNeely and Gifford (2007, p. 3) state, “Rather than simply addressing characteristics of traffic problems per se, we can consider the modes of cultural reproduction and how they might be affected in order to improve outcomes for traffic safety.” The need to maintain cost-effective and reasonable strategies is important; also important is to have strategies that make a difference with behavior. The complexity of behavior change is not unique to traffic safety, and current research highlights

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the opportunities that exist to learn from other fields of study.

3. LAYING THE FOUNDATIONS This chapter on persuasion and motivational communication emphasizes the human interaction central to the education segment of traffic safety. The three “E’s” often go hand-inhand, as education can serve a role of informing about enforcement as well as engineering. This chapter emphasizes various ways of educating specific audiences regarding a range of traffic safety issues. The contents of this chapter can be likened to a recipe book, with a variety of kitchen implements providing assistance. In the kitchen, the recipe book provides instructions about how to put together various ingredients so that the desired edible product is suitably prepared. With the various raw materials, assistance is provided with the use of tools and equipment (e.g., the engineering component). Similarly, this chapter offers resources designed to be helpful in achieving a quality productda targeted outcome in traffic safety. Persuasive communication works the same way; to achieve results, the program planner needs to be organized and use a plan to move ahead. This chapter seeks to provide that plan, as well as some of the implements and helpful tools. With traffic safety issues, as with other areas of public health, the most helpful starting point is the end point. When designing messages about any of the various traffic safety topics, and for any of the various traffic safety audiences, the key question is “What do you want the audience to know, feel, or do?” That is, as a result of the communicative efforts, what knowledge do you want them to have, what attitudes or feelings do you want them to hold, and/or in what behavior do you want them to engage? For example, with knowledge, what is important for the audience being reached? Is it having them know the legal consequences of speeding? Is it knowledge about the increased crash risks associated with speeding? Alternatively, is it for them to have an understanding of the physics links to a crash caused by speeding, or is it to have them understand how vehicle conditions (e.g., the condition of tires) can affect speed and the driver’s safe control of the vehicle? Is the aim to have the audience appreciate how various situational circumstances, such as road conditions, weather, visibility, or traffic, may affect the appropriate safe speed? Similarly, traffic safety communications can focus on attitudesdhelping an audience to appreciate the importance of having the child safety seat installed properly or getting a person to believe that following the speed limit law is important. This emphasis on attitudes may address a person wanting to avoid hassles associated with a fine or court appearance; similarly, it may emphasize an overall

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societal ethic of exhibiting courteous driving skills. The important consideration is on what it takes to reach the audience: Is it something practical (avoiding hassles) or something more broad-based (embracing an overall ethic of courtesy)? Two concerns emerge from this initial listing of knowledge and attitudinal considerations. First, some program planners will want all the identified knowledge or attitude items achieved. Although desirable, it may not be feasible to achieve everything. With persuasive communication, it is critical that priorities be made; communications initiatives benefit from a clearly defined focus. Second, although knowledge or attitudes alone are helpful, the ultimate aim is a change in behavior. Program planners benefit from clarifying their beliefs and assumptions regarding how the proposed strategies will affect behavior. This helps them think through what is to be communicated so that it has the greatest likelihood of achieving the desired behavioral outcome. For the previous examples, increasing knowledge about crash risks is helpful only if it ultimately helps influence behavior surrounding speeding, aggressive driving, or other driving behaviors. Similarly, modifications in attitudes are helpful as long as the change contributes to the desired behavioral outcome. Although most public health and traffic safety program planners ultimately want a change in behavior, specific communication strategies may have a more limited and immediate outcome. Having a focused, limited, proximate outcome may be central to achieving the ultimate desired outcome. For example, knowledge, if it ends there, is not sufficient as an ultimate goal; however, knowledge would be reasonable as a foundation for making progress in achieving behavioral change. What is important is that program planners define clearly each aspect of the persuasive communication effort and its designed aims. Complementing this focus on knowledge and attitudes is the theoretical construct. Communications initiatives benefit from a grounding that makes clear the assumptions about why the strategies chosen will result in the desired result or outcome. The rationale for this is that often the messenger is not clear with the desired result and has not thought through the assumptions associated with this end point and what it takes to achieve it. Although traffic safety and public health strategies are generally prepared with good intentions, they often have a limited focus. By carefully planning and implementing effective messages related to the myriad of issues associated with traffic safety, it is more likely that the messages will be more appropriate and effective, with positive results achieved. To achieve results, a seven-step planning process is presented. These steps begin with an understanding of the audience, attending to their needs and issues, and trying to understand their “inner worlds” as much as possible. The plans developed, along with the specific content and the

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methods for communication, comprise the overall communication strategy. This seven-step planning process is valid for developing print materials (e.g., brochures, posters, fliers, and billboards), audiovisual materials (e.g., radio and television public service announcements), and interpersonal communication strategies (e.g., interviews and workshops). The conceptual process and planning of initiatives, regardless of the type of approach, are consistent. Whether designing a brochure, preparing a workshop, doing a radio public service announcement, or engaging in another communication initiative, planners focus on what they want the reader to know, feel, or do. The process is the same; the specific strategies vary based on factors such as the audience, the message, and the strategy (Figure 30.1). This chapter defines each of these seven steps, with specific considerations and applications appropriate to a range of traffic safety issues. Program planners will adapt specific elements to their unique needs and circumstances and will modify them based on available resources. Simply stated, a “cookie-cutter” approach is not appropriate or reasonable. What is appropriate for one location or audience may or may not be appropriate for another; however, others’ effort can be reviewed and considered, and it can be helpful in aiding program planners to design what is appropriate for their local needs and issues. A final consideration is that even with careful planning and implementation, achieving the desired aims is not quick and easy. On the other hand, the process need not be unnecessarily difficult. Although the process is challenging, time-consuming, and requires thought, it can also be fun and engaging. Most important, this process provides the foundations for meaningful and successful results.

4. STEP 1: UNDERSTAND THE NEED AND AUDIENCE The first step in preparing the necessary messages for the audience is to define the issue. What are the issues that need attention? What problems exist in the community that would benefit from modification, such as high-risk intersections where pedestrians are at greater risk or incidents associated with specific times of the day? Does a recent incident, or a trend in problems in some area, provide the basis for attempting to persuade a group of people to change their practices? Clarifying the issue is the critical first step. Associated with the issue clarification is understanding the audience to be reached. Who comprises this group of people, and what are their characteristics? Factors such as age, gender, literacy, interests, and what might attract their attention are critical as the foundation for effective planning. In this process, more than one audience may be targeted; if this is the case, then different criteria may be used, resulting in potentially different messages for the same issue.

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FIGURE 30.1 Persuasive communication planning model

Table 30.1 illustrates a situation involving Hispanic males who, in certain locations, were crossing a busy road and getting hit by automobiles. This occurred in both a suburban location and a rural location. In the suburban location, these pedestrians were alcohol impaired and crossed the highway at non-crosswalk locations; in the rural setting, individuals walked along a two-lane highway and occasionally wandered into the road and got hit by automobiles. In each situation, the problem occurred due to alcohol impairment, and it typically occurred after the individuals received a paycheck. The specific area of concern, as well as the target audience, helps program planners target messages and

strategies. These communication efforts can complement other traffic safety strategies, such as those associated with engineering or enforcement activities. For example, signage can direct individuals to crosswalks, signal intersections, or pedestrian ramps over the highway that are designed to reduce pedestrian injuries. After identifying the audience and its needs, these identified characteristics serve as the basis for preparing the persuasive efforts so they are more likely to resonate with the audience. For example, with the Hispanic male audience noted previously, focus groups informed program planners that a visual approach, with limited words, would work best.

TABLE 30.1 Understanding the Audience

Audience example

Issue of Concern

Characteristics

Motivators

Pedestrians crossing high-volume traffic area

Hispanic males

Enjoy soccer

Behavior occurs while individuals have been drinking

Ages 19e30 years

Celebrate on paydays

Behavior often occurs on paydays

Working

Like colorful imagery

English is second language

Family-oriented

Wear dark clothing at night

Religious Do not mind taking risks

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The result was a campaign with limited focus on English words and inclusion of images that made sense to the audience. Strategies should be incorporated that appeal to the target audience, initially to attract their attention and ultimately to appeal to their understanding and frame of reference. Two additional considerations are important. First, it is important that a message be clear and understandable. A helpful strategy for this is the use of the “SMOG” rating reported by the National Cancer Institute (Clear & Simple, 1994). This is designed to keep the language simple and understandable, with an aim of minimizing the number of words with three or more syllables in most documents. By keeping language simple, messages are much clearer and understandable by the various intended audiences. Complementing this, one must ensure that the materials are culturally appropriate; this means that the messages and materials must be designed to reach the target audience, with the various considerations relevant to a specific community. Thus, even with racial or cultural similarities, specific nuances that link to factors such as country of origin, region of the country, and urban/rural/suburban factors must be taken into consideration. This is best understood by those at the local level, who can help clarify locally appropriate factors so that the planned messages have the greatest possibility of reaching the audience in the desired way. To further assist with ensuring that the materials and their language are appropriate and conveying the desired message, pilot testing is appropriate. Having a sound understanding of the audience associated with the area of need provides the essential foundation for effective communication. This helps program planners target the message and tailor it to the audience in question. Should the issue be more generic, a more generalized approach may be appropriate. As such, the level of detailed planning may be much less. For example, if the aim is to have all drivers in a community pay increased attention to driving speeds while traveling in school zones, the messaging may be the same for all audiences. However, if the individuals of concern with this issue are young, novice drivers, the messaging may appeal directly to them but not be as relevant for older drivers. Similarly, the target audience may be the diversity of residents in an area for whom English is a second language; a set of bookmarks may be appropriate, with multiple versions in different languages so that each major group of residents has a physical reminder of the traffic safety message in its own language. This carefully developed foundation is most helpful for subsequent steps.

5. STEP 2: CLARIFY THE ASSUMPTIONS The next step emphasizes clarifying the assumptions surrounding what is being targeted. This is the step with

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more formalized theory building. In this step, attention is given to the assumptions undergirding the audience’s behavior and what might be appropriate for modifying this behavior. Although there are many theories regarding behavior change, three frameworks are most helpful for traffic safety communication: the stages of change model; the health belief model; and Aristotle’s concepts of logos, pathos, and ethos. In addition, step 3, which follows, offers considerations about whom to reach, through a review of the Institute of Medicine’s universal, selective, and indicated approaches. Each of these foundations can be considered because each one addresses communication planning in complementary ways. What is important in using any of these or other frameworks is making conscious decisions about what approaches are most relevant and what is most likely to have the desired influence on the target audience. That is, a sound understanding of the audience’s needs and characteristics (step 1) helps inform this step’s clarification of the theoretical assumptions about the target audience’s views of the issue and possible “triggers” or motivators for change. The first primary theory is the stages of change model, developed by Prochaska and DiClemente (1983). This is based on an assessment of “where the person is.” Emerging strategies can be viewed as heightening an individual’s awareness about the desired action and then making efforts to move the person to that specific behavior. For example, if an individual is not aware of a specific safety action (e.g., the risk associated with driving at regular speeds during adverse conditions, or the distraction caused by texting or by talking on a hands-free mobile phone), then he or she is not likely to see any need to change behavior; this individual would be targeted at the precontemplation stage. However, if an individual is aware of the risks with such unsafe behavior but still engages in the risky behavior, then he or she is in the contemplation stage and can be engaged to move to the preparation stage. Furthermore, if a person is practicing the safe behavior (in the maintenance stage), appropriate strategies would be designed to help maintain this behavior. One example is rewarding individuals for wearing safety belts, with campaigns rewarding drivers who use these obtaining good results, particularly when safety belts were relatively new in cars. Program planners identify the status of the target audience regarding the desired behavior and then craft the most appropriate messages and materials. Table 30.2 illustrates the stages of change model, as presented by the National Cancer Institute (Glanz, Rimer, & Su, 2005). Another model helpful for clarifying the message is the health belief model. This helps program planners think about what strategies may be helpful in motivating the target audience to engage in the desired behavior. By understanding some of the audience’s issues, it may become clear that certain aspects included in this model are

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TABLE 30.2 Stages of Change Model Stage

Definition

Potential Change Strategies

Precontemplation

Has no intention of taking action within the next 6 months

Increase awareness of need for change: personalize information about risks and benefits

Contemplation

Intends to take action in the next 6 months

Motivate; encourage making specific plans

Preparation

Intends to take action within the next 30 days and has taken some behavioral steps in this direction

Assist with developing and implementing concrete action plans; help set gradual goals

Action

Has changed behavior for less than 6 months

Assist with feedback, problem solving, social support, and reinforcement

Maintenance

Has changed behavior for more than 6 months

Assist with coping, reminders, finding alternatives, and avoiding slips/relapses (as applicable)

Source: Glanz et al. (2005).

more appropriate than others for targeting. This model focuses attention on attitudes, beliefs, or perceptions that may get in the way of an individual’s safe behavior. In using this model, think about the range of reasons why individuals often do not engage in safe behavior. The following statements are not uncommon among various groups and individuals: l

l l

l

l

l

l

l

l

l

l

l

I don’t worry about speeding, since everyone else is doing it. The police are never enforcing the law. Having just a few drinks is OK, as long as you’re not above the legal limit. Driving with a hands-free mobile phone is safe, since both hands are free. I’ve been driving for 65 years, since I was 17; I know what I’m doing. I bicycle with my headphones on, since music helps me enjoy the experience. I like the free feeling of the wind blowing through my hair when I take my bike out on the open road. My child is big enough, and (s)he doesn’t need that constraining seat. It’s such as hassle to always wear a safety belt; after all, I’m only driving around the neighborhood. I’m not sure what the laws are about passing a school bus, since school is not in session right now. The weather is making the road dangerous, but I need to keep up with the rest of the traffic. I can’t believe that guy; he just cut me off, so I’ll show him!

What is behind the lack of these individuals being as safe as possible? One factor is the decision framework within which an individual operates; each person has perceptions of the world surrounding him or her and a range of influences on his or her decisions. It is within this overall

decision-making context that individuals make a type of risk assessment regarding situational issues. How they perceive risk, how severe the risks are, the likelihood of being involved with the harmful behavior, and the factors associated with avoiding risk are all associated with their decisions. As Paul Slovic (2000, p. xxxvi) states in the introduction to his book, “Human beings have invented the concept risk to help them cope with the dangers and uncertainties of life.” Within the context of traffic safety, the specialists and program planners know quite readily what the unsafe or unhealthy behaviors are. Use of a framework such as the health belief model can be helpful in thinking through perceptions of risk and for further refining these potential areas of focus that can be used in motivational campaigns and messaging. For example, the program planner can refine the messaging by identifying whether the concern with the audience is based on a perception of susceptibility or a perception of severity (or both); this clarity helps the efforts be more focused and the message’s recipient (the target audience) to be clearer with regard to the desired actions. When the audience hears the message more clearly, it is more likely to engage in the desired behavior. Table 30.3 illustrates the health belief model, as prepared by the National Cancer Institute (Glanz et al., 2005). Applying traffic safety considerations to the health belief model illustrates how this theoretical grounding can be useful. Table 30.4 illustrates how each concept can be imbedded within the context of this framework. Further elucidation of these issues is helpful for program planners because they gain greater clarity about the specific issue of concern and the specific factors that may be beneficial to address with safety communication strategies. Similarly, the health belief model can be used for a single issue, such as alcohol-impaired driving. Ultimately, having a focus on a single issue is more likely to be of

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TABLE 30.3 Health Belief Model Concept

Definition

Potential Change Strategies

Perceived susceptibility

Beliefs about the chances of getting a condition

Define what population(s) are at risk and their level of risk Tailor risk information based on an individual’s characteristics or behaviors Help the individual develop an accurate perception of his or her own risk

Perceived severity

Beliefs about the seriousness of a condition and its consequences

Specify the consequences of a condition and recommended action

Perceived benefits

Beliefs about the effectiveness of taking action to reduce risk of seriousness

Explain how, where, and when to take action and what the potential positive results will be

Perceived barriers

Beliefs about the material and psychological costs of taking action

Offer reassurance, incentives, and assistance; correct misinformation

Cues to action

Factors that activate readiness to change

Provide “how to” information, promote awareness, and employ reminder systems

Self-efficacy

Confidence in one’s ability to take action

Provide training and guidance in performing action Use progressive goal setting Give verbal reinforcement Demonstrate desired behaviors

Source: Glanz et al. (2005).

greatest relevance to traffic safety personnel with a dedicated interest in a focused topic, such as speeding, red light running, pedestrians, school buses, or work zones. The framework can be helpful by specifying ways in which each concept may be relevant or not. Table 30.5 provides an example for one such traffic safety issue. A third theoretical approach can be useful for developing a framework for persuasive communication. This approach has been around for centuries, dating to Aristotle’s era. He said that to persuade others in a specific

direction, it is important to consider each of three distinct areas of emphasis: reason or logic (logos), individualized passion or interest (pathos), and a sense of what is right or morally desirable (ethos). Table 30.6 highlights these three areas with an illustration based in traffic safety. These three frameworks or models are helpful in the “toolkit” of resources for traffic safety professionals. Although these three do not represent all the potential approaches, they do represent the dominant ones used in

TABLE 30.4 Health Belief Model with Traffic Safety Applications Concept

Traffic Safety Issue

Illustration

Perceived susceptibility

Speeding

Perceptions about the likelihood of getting involved in a traffic crash, being injured or killed, or being caught by police

Perceived severity

Alcohol-impaired driving

How much of a negative consequence results from enforcement, such as jail time, financial cost, inconvenience from loss of license, and public embarrassment

Perceived benefits

Motorcycle helmet

Reduced injury if involved in a crash

Perceived barriers

Cell phone

Perceived loss of time and convenience of the driver wanting to talk on the phone

Cues to action

Safety belt

Sign, light, or buzzer to remind driver/passenger to buckle up (in passenger vehicle or public transportation)

Self-efficacy

Child safety seat

Learning how to get seat installed properly, with minimal loss of time

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TABLE 30.5 Health Belief Model with a Focused Traffic Safety Issue: Alcohol-Impaired Driving Concept

Illustration

Perceived susceptibility

Perceived likelihood of having limitations on driving judgment or behavior, or being in an alcohol-related crash, of being injured, of killing oneself or others, of being caught by police

Perceived severity

How much of a negative consequence results from enforcement, such as jail time, financial cost, inconvenience from loss of license, and public embarrassment

Perceived benefits

Peace of mind regarding safety of self and others with being a sober driver, taking public transportation, having a designated driver; reduced costs associated with safe transport

Perceived barriers

Making arrangements for safe transportation, perceived short-term costs (financial, time) to engage in this sober driving process

Cues to action

Engaging in deliberate planning to designate sober driver, speaking up when faced with potential alcohol use to say “I’m driving” or “Who is driving?”

Self-efficacy

Feeling confident to speak up and engage in planning safe transportation activities

addressing health behavior. Furthermore, noteworthy is the fact that no one framework is sufficient in preparing appropriate messaging. What is most important is to highlight the specific areas of concern, learn as much as possible about the audience to be targeted with the messaging, and then determine which of these theoretical constructs, and which parts within them, can be helpful in designing the most appropriate messages and messaging strategy. For effective messaging, it is important to clarify these factors as concretely as possible so that the planned effort is well grounded. In summary, the stages of change model can be used to determine where the target audience is when viewed along a continuum (from precontemplation to maintenance), the health belief model can help ground the efforts in the types of approaches that might motivate

them to action, and Aristotle’s framework can assess tactics that might be used to influence their thinking. Blending these three foundations is the key to appropriate message preparation.

6. STEP 3: PREPARE THE PLAN The third step is an organizational one. This step builds upon the first two steps by examining the types of strategies that may be used in preparing motivational messaging. The important first effort within this step addresses the audiencedwhether the effort is going to be “one message for all” or more focused. This will be based on the need defined in step 1. Some messages may be most appropriate for the entire community, and some benefit from more specific targeting. Helpful in thinking about this is the Institute of

TABLE 30.6 A Balanced Approach Concept

Explanation

Illustration

Logos

Emphasizing a rational and logical approach, building on scientific foundations. Often used to highlight assumptions on which decisions are made, including challenging faulty assumptions

Behavior based on faulty assumption: A person decides to not wear a safety belt because, in the event of a crash , he or she wants to be thrown clear of the vehicle and not get trapped in the vehicle

Pathos

Addressing the emotions, aims, feelings, and social desires of individuals. Can tie into insecurities. Often linked to tragic events, without attention to rational arguments

Action stimulated by tragedy: A community wants a strict crackdown on speeding after the tragic death of a child in a school zone

Ethos

Promoting a quality character among the audience, through engaging in trustworthy sources. It is helpful to evoke good sense, good moral character, knowledge, and authority to gain the confidence of the audience

Confidence promoted by authority: Promoting a traffic safety campaign with testimonials by law enforcement, medical and research personnel

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Medicine’s three-part framework, cited in Gordon (1987) and Drug Abuse Prevention: What Works (1997). Although initially designed for a mental health setting, and adapted for prevention, this framework can also work easily for traffic safety. Its three componentsduniversal, selected, and indicateddcan help guide the messaging. As with the theories, some overlap among these components may be appropriate. The first component, universal approaches, is designed for all within the community. These address an entire population, whether at the national, state, or local level. With traffic safety, this would include the entire population, including subgroups of parents and youth, older and younger drivers, and pedestrians and automobile drivers. Universal approaches are typically general in nature and are not time-consuming or intrusive for the audience. At the same time, their global nature may be seen as irrelevant to some who receive the message. Selected approaches are directed to a subgroup within the population; this grouping is based on a set of unique characteristics for the audience that makes the audience more vulnerable to risk or more appropriate for specific messages. Groupings may be based on factors such as age, gender, experience, role, or situation. For example, selected approaches for age would result in targeting messages in different ways for older drivers and for younger drivers. With gender, males and females may be reached by different messages because of different driving patterns or perceptions. Experience is a helpful classification, with targeted messages for novice drivers differing from messages for those with more experience. The consideration of role would result in different messages based on driving an automobile, motorcycle, truck, or bicycle; it may also be a different message for those who are involved with public transportation or long-distance driving. Different messages are appropriate based on specific situations, such as night driving, inclement weather conditions, work zones, congested areas, and school zones. Based on these or other identified factors, the messages to the identified subgroup or subpopulation can be prepared in a focused manner. Selected messaging is appropriate because some identified factor is common among these individuals that differentiates them from others. For example, it is presumed that based on the subgroup membership, the messages appropriate for mature, older drivers would necessarily be different from those targeted to younger drivers. The third classification within this framework includes indicated strategies. This grouping is based on an identified need or issue specific to individuals. This could include individuals who have a speeding violation, a conviction for driving while intoxicated, or multiple parking citations. There may be other groups, such as those who do not pass the driver licensing exam or

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individuals or family members who have special equipment for driving an automobile. This classification is helpful for preparing messages and communications outreach that are designed specifically for these situations. For example, a person convicted of drunk driving may receive certain messages that are different from those received by the general public (with universal approaches) or young drivers (with selected approaches). Similarly, it is appropriate to have different messages for those convicted of a second offense and for those convicted of a first offense. Incorporated in these indicated messages may be additional reasons for not engaging in the specified behavior, alternative strategies to help offset the behavior, testimonials from others who have previously engaged in the behavior, and information providing referral to local resources. Often, an aim of indicated strategies is personalized effort to keep the individual from repeating the problematic behavior. Indicated strategies are an important component of the entire repertoire of strategies because they focus on needs and issues more directly relevant to the audience. Table 30.7 summarizes the three foci with illustrations. This foundation helps program planners carefully consider what they want the audience to “know, feel, or do.” Based on an understanding of the audience’s needs and unique features (from step 1), and a consideration of what might be appropriate for reaching the audience (through the theories found in step 2), program planners can build messages that are more specific and appropriate. Table 30.8 illustrates how to think through issues relevant for an audience at a particular time. Examples are shown for each of three types of audiences, including universal, selected, and indicated situations. Program planners may find it helpful to adapt this planning resource for specific communication efforts in their communities. A final consideration in preparing the overall plan for effective communication and messaging has to do with timing. Communication planners have often found it helpful to link their initiative to some specific external occurrence. Some examples are obvious, including a campaign about school bus safety scheduled for the fall return to school, awareness efforts about impaired driving during a holiday season or special festive events, and materials provided to youth and parents upon receipt of the initial driver’s license. For other situations, based on local or specifically identified needs or issues, it can be helpful to identify something with which to link the initiative. For example, if a community has a problem with red light running, the timing for highlighting important legal and safety issues could be linked to items such as the release of new data, a tragic local situation, a time of year when greater congestion occurs, or the opening of a new store. This type of local linkage can help by providing a “hook” so that media channels and

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TABLE 30.7 Strategies Based on Focus Concept

Explanation

Illustrations

Universal

Designed for the population as a whole, without attention to subgroups or subpopulations

Messages about changes in DUI laws Awareness about safe driving in winter Campaign on aggressive driving Pedestrian safety initiative in congested area

Selected

Targeted to a specific group or subgroup, based on identified need, specific issue, or higher risk

Safe driving messages for novice drivers Campaign on night driving for elderly drivers Information for parents of schoolchildren Special materials for those purchasing a moped

Indicated

Focused efforts for individuals having a specific issue based on problematic behavior

Materials for drunk drivers after first conviction Brochure for parents of young driver offenders Information for multiple offense speeders Resources for families of drivers with disabilities

the public can envision personal relevance for attending to the issue. One of the challenges with traffic safety issues is that what program planners believe is obvious may not be as clearly understood by the target audience. To help the general public or targeted audience have this understanding, and ultimately the desired safe behavior, it is helpful to find ways to link the specific issue with other factors. The following are examples: l

Link the release of information about the law on a specific issue with an anniversary of the passage of that law (e.g., the 10th anniversary of the passage of new laws regarding DUI).

l

Organize a campaign about an issue, such as school bus safety or school zone speeds, based on the enrollment of a certain number of students (e.g., the 10,000th student to enroll).

l

Prepare a message linked to a date, such as the 4th of July (e.g., “Four tips for the Fourth”).

The planning of the traffic safety campaign or messaging initiative is an important task because it helps focus the goals to be accomplished in ways that best address the needs of the identified audience. The foundations and design with planning overall or focused messages is an important preparation strategy for the development of the content.

TABLE 30.8 Traffic Safety Messages Planning Resources State the Issue or Concern

What is Desired (Know, Feel, Do)

Theoretical Elements That May Help Address This

Example: Change in DUI laws

Know: Awareness of new law and consequences

Perceived susceptibility

Audience: General (universal)

Know: Importance of obeying the law

Perceived severity Logos

Example: Not wearing safety belt

Know: Heightened bodily harm in an unbelted crash

Perceived susceptibility

Audience: Youth (selected)

Feel: Personal vulnerability to injury Feel: Personal responsibility to prevent unnecessary harm to self

Perceived severity Pathos

Do: Wear safety belt regularly Know: Local laws and consequences

Cues to action Precontemplation

Feel: Importance of preparing to stop significantly before entering intersection Do: Anticipate a signal change and slow speed in preparation for stopping

Ethos

Example: Running a red light Audience: Violators (indicated)

Cues to action

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7. STEP 4: BUILD THE CONTENT

l

The specific approaches organized within traffic safety messaging benefit from multiple approaches implemented in a variety of ways. That is, program planners cannot expect that a single message using a single approach will have a broad impact. Thus, this step focuses on thinking beyond a single messaging strategy because a single messaging strategy can risk having the target audience not comprehend the message. Using multiple approaches has the advantage of several strategies complementing one another, thus gaining greater access to the audience. Each of these can complement other traffic safety initiatives within the “engineering, enforcement, and education” framework. Here are examples: l

l

l

l

l

l

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A community with concerns about speeding has signs and posters prepared. To help individuals install child safety seats more safely, an informational brochure is prepared. Youth texting on mobile phones while driving are targeted with graphic public service announcements social networking approaches. Safe prom seasons are promoted with periodic text messaging to high school students. An increase in aggressive driving prompts television and radio public service announcements. Concerns about pedestrian safety at identified intersections are addressed by a radio talk show with medical and law enforcement professionals taking call-ins by listeners.

A targeted campaign to deal with impaired pedestrians includes signage in stores and businesses, table tent placards in local restaurants, and posters at local bus stops.

When doing the messaging, consider the types of sources of information that may be accessed and frequently used by the target audience. Furthermore, the nature of the message sought, and the theories undergirding it, may help guide this effort. Some audiences will respond to written articles in the newspaper or a special television program, whereas others will benefit from posters at bus stops or ads on social networking sites. Table 30.9 illustrates a range of approaches that can be considered as part of the messaging process. All of these are not appropriate for all issues, and some are more appropriate because of the ways in which the target group accesses information. Program planners can review what approach best fits the audience and the issue of interest. Other approaches can be identified to add to this listing; what is important is to find strategies that work best for the audience(s) identified. In addition to having a variety of strategies, it is important that the approach be prepared in ways that complement the message and are appropriate for the audience (Bensley & Brookins-Fisher, 2009). Thinking about what the audience should know, feel, or do helps select strategies with the desired reach and impact. For an issue that is particularly serious, a somber tone may be preferred; thus, the message should have images and colors that convey this. Similarly, if the aim is to excite an audience, the style should communicate this theme.

TABLE 30.9 A Menu of Strategies Brochure

Booklet

Flier

Poster

Fact sheet

Banner

Billboard

Radio PSA

Television PSA

Web advertisement

Newspaper ad

Magazine ad

Talk show

Television interview

Press release

Tips sheet

Facts and figures

Notepads

Calendar

Survey results

Testimonials

Checklist

Refrigerator magnet

Planner

Self-assessment form

Outline for local talks

DVDs

Radio script

Computer mouse pads

Screensaver

Program planner

Workshop outline

Personal reflections

Pencils/pens with message

Shower hanger informational card

Flier for envelope stuffing

Workshop

Public speech

Website

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Furthermore, with a target audience of a certain age (e.g., older or younger drivers), the imagery used should be representative of this group. Although many of these suggestions seem obvious, often the planning fails to reflect this judgment. Specific suggestions include the following: l l

l

l

l

l l

l

l l l l l l l l l

Incorporate bright colors to attract attention. Include background settings relevant to the topic and/or audience. Blend animals or objects (e.g., soccer balls and school buildings) to draw attention to a specific audience. Repeat messages throughout the various approaches, such as a slogan, tagline, or catchy phrase. Include data or statistics to ground the need for the desired outcome. Incorporate the sponsoring agency and its logo. Provide a quote from an authority figure (e.g., chief of police, researcher). Include a testimonial to illustrate the value of the result sought. Put the person(s) in a relevant, practical situation. Appeal to intellect and emotions. Be innovative and creative. Be culturally relevant; demonstrate diversity. Show “before and after” scenes. Appeal to emotion, patriotism, and personal relevance. Localize the data. Include referral resources. Include contact information, such as phone number, website, and address.

Regardless of the topic or issue, and regardless of the strategy used, the preparation of the content involves many of the same processes. To maximize the opportunities for messages to be heard, respected, and ultimately adopted by the audience sought requires careful attention to appropriate strategies. For example, when using a brochure or a billboard, the message must be clear, point to the next step(s), contain credentialing information (e.g., a sponsor), and cite where to turn for more information (e.g., a phone number or website). Similarly, if the initiative involves a media interview or workshop, the speaker’s message must be clear and concise, point to a next step, and incorporate resources. Although the specific details and processes may vary, the overall strategy remains the same.

8. STEP 5: PLAN, PILOT TEST, AND REFINE The details come to fruition in this phase. Rather than taking materials that have been drafted and implementing them directly, it is important to conduct a “dry run” to test the approaches. Based on what outcomes are desired (what the audience will know, feel, or do as a result of engaging

Major Countermeasures to Reduce Risk

with the strategies or resources), attention is paid to the ways in which the audience obtains this message. This planning and review step focuses on how the messages and approaches are going to be received. One main way of testing the message and approach is to hold a focus group or discussion with individuals who represent those who will be reached. For example, if the aim is to reach young Latino males, the initial testing of the messages and approaches should be done with representatives of this audience to get their feedback and suggestions. If the aim is to reach them with a specific message (what they would know, feel, or do), then initial testing should be performed and representatives should be asked what it “says” to them and how they react to it. Key questions include the following: Is it credible? Does it represent your group well? Do you feel included or invited? Does it resonate for you? What does it make you want to do? How do you feel about it? What does it say to you? This type of review process can assess the extent to which the planned communications effort seems to “resonate” with the target population. By first testing it with a small, convenient group that has a similar background and attitudes as those of the target population, modifications can be made. It is important to undertake this pilot testing at several points throughout the development process. Program planners should engage sample groups with ideas, messaging, wording, imagery, and other strategies being considered to obtain their reactions as early as possible so that modifications can be made at various points in the developmental process. Once some mock-ups are prepared (e.g., draft materials, storyboards, and scripts), more structured reactions can be obtained. Another important consideration in the pilot preparation is the development of workshops as well as other public appearances, such as a speech. To maximize the impact of these opportunities, special attention is paid to their circumstances. One suggestion, helpful for any of these settings, is to follow the mantra, “Tell them what you’re going to tell them, then tell them, then tell them what you told them.” Although this may sound simplistic, it is helpful for providing a clear framework for the workshop, speech, or even remarks on a talk show. When preparing a workshop or other remarks, regardless of the length, the content found in Table 30.10 can be helpful for developing an organized approach to maximize the impact. Similarly, public remarks benefit from having some variety so the audience stays engaged and is clear about the desired outcomes (know, feel, and do). This can be done

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TABLE 30.10 Workshop Development and Implementation 1. Know your audience

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technical considerations. In planning, it is helpful to consider the logical sequence of activities, as well as to have contingency plans, for smoothly implementing a program.

2. Arrange the setting 3. Clearly define learning objectives 4. Have appropriate materials 5. Have “bookends” for remarks and activities 6. Vary styles Lecture Activity Discussion Visuals Examples Video Role play 7. Engage the audience

with testimonials, quotes, examples, and visual illustrations. Even with a brief television or radio interview, it can be useful to include elements such as a slogan, website, and phone number. The final consideration focuses on obstacles or challenges. Program planning takes into account the seven “P’s”: “proper prior planning prevents pathetically poor performance.” One way of maximizing this is to have time lines, alternative strategies, and contingency plans. For example, when preparing for an event, have materials ready ahead of time (allowing for delayed delivery), prepare alternative delivery mechanisms (in case the equipment does not work), and make backup files often (in the case of corrupted files or computer crashes). It is also helpful to identify alterative locations (that can accommodate the event if weather conditions are problematic) and have a list of backup speakers (if the chosen speaker is unavailable or cannot attend at the last minute). Use of a Gantt chart or a PERT (Program Evaluation and Review Technique) chart can be helpful in preparing a schedule; these illustrate how the various aspects of a project fit together in the planning and preparation phase. They help illustrate what needs to be done, and when, in order to meet deadlines. Often, various tasks can overlap with one another and not be done sequentially. For example, if a CD-ROM is going to be distributed at an event, start with the delivery date and work backwards for the conceptualization, design, preparation, testing, revision, and production; a parallel process is preparing the appropriate approvals for a vendor or specific

9. STEP 6: IMPLEMENT It is during the implementation phase that all the effort expended on the needs assessments, theoretical grounding, creative work, planning, and organizing pays off. This is where things come together, and the activity, resource, or initiative connects with the audience. A communications campaign or release of a publication (e.g., a billboard, poster, brochure, or flier) involves connecting the audience with the work. This is where the target population sees the message, starts to think about it, and, ideally, starts to modify behavior. The entire aim of the persuasive communication is to have an effect on behavior; however, as noted previously, behavior change is not quite so simple. Although program implementation is designed to be as effective (as persuasive) as possible, the ultimate impact is benefited by the target audience having multiple exposures from various sources. Thus, the value of a communication campaign is that it engages the audience from multiple approaches with a consistent, clear message. As highlighted previously, a communication campaign blends multiple elements that could also be used independently. For example, having a theme week or day, or an awareness month, would be appropriate for any of the range of traffic safety issues. A week with a “buckle up” focus may include fliers in local storefronts, ads in the newspaper, public service announcements on the radio or television, banners on busses and at bus stops, workshops for parents and young drivers, checkpoints monitoring safety belt use and distributing a bookmark, presence on local talk shows, and media coverage. With the implementation of a campaign, or with the initiation of a specific activity (e.g., a poster), media coverage can be very helpful in furthering the awareness of the issue. This could begin with a kickoff event such as a press event. Media can be invited to cover statements from local leaders, research and data from academics, testimonials from local personnel or celebrities, and highlights of the campaign. Media coverage can also include the local media (whether print, radio, television, or web based) providing an additional venue for the target audience to receive the message. Media representatives may cover the release of an initiative, and it may be an opportunity for communicating controversies and different perspectives associated with the activity (i.e., different views about whether the traffic safety issue is of high importance). What is exciting about media coverage is that the local press can be most helpful with informing a much broader

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audience than can typically be done directly with the programmatic resources. Furthermore, this media coverage is done without direct costs to the project. For example, if a campaign is being implemented to promote a new law about use of mobile phones while driving, the direct costs include materials preparation, printing, distribution, postage, and advertising. If the media gets involved, many more individuals will hear the message; furthermore, any imagery used with the media coverage can complement the materials distributed, thus enhancing the message and impact. This strategy can also be used with controversy surrounding many traffic safety initiatives; for example, if something new is being considered (e.g., passing a law, enforcing sanctions, and doing educational activities), different viewpoints may be heard and create greater public awareness. Whether sharing information in a proactive manner, addressing a controversy, or responding in a crisis-type situation, a balanced approach is helpful. U.S. Department of Health and Human Services (Communicating in a Crisis, 2002) officials dealing with crisis activities have highlighted three “communications fundamentals” for these situations; however, these are applicable to all types of situations. First, it is important to develop goals and key messages. Those handling media relations should strive to ease concerns as well as provide suggestions about how to respond. Second, it is critical to stay on message; speakers should emphasize the major points without being overly repetitive. Third, information provided should be accurate and timely, including statistics, information sources, interpretations, and any conclusions. Strategies implemented with traffic safety education and communication efforts can be maximized through careful planning and implementation using this step. The final issue is whether this effort has had any impact; this is addressed with approaches in step 7.

10. STEP 7: REVIEW, REFINE, AND REGENERATE The final step for the preparation and delivery of messages is evaluative. This assesses how well the efforts were accomplished, and it identifies improvements that can be made to maximize attainment of the desired aims. This step includes information gathering, followed by reviewing information and making decisions about the future. Essentially, this process reviews what parts of the process should be maintained and what could be modified for improvement. This is based on the results achieved so that a better understanding is gained to determine what program elements seemed to result in the desired effect. The overall aim is to continuously improve the program with message distribution, receipt, and impact.

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In planning the evaluation, program planners can think in terms of two parts. First, consider the process evaluationdhow well things went. Second, consider the outcome evaluationdthe results achieved. Often, program planners focus primarily on the process evaluation, primarily because it is much easier to do. They ask whether the audience liked the workshop, whether the resource was seen, how many items (e.g., handouts, fliers, brochures, and posters) were distributed, and similar measures. Although this is valuable and important to document, the ultimate aim is whether the efforts had any impact. Certainly, if something is not distributed, it will not be seen; if there are problems with the weather or equipment so that an undertaking does not occur as planned, then the results will necessarily be limited. These are important to know so that modifications can be made in planning and implementing the program and activities and also in building contingencies. The more difficult evaluation focuses on the results or outcomes. Lonero (2007, p. 11) summarizes this succinctly: “The needed development of an expanding pool of knowledge for continued refinement of behavioral technology will only become available through objective, empirical evaluation.” If the outcome specified is a change in knowledge about laws in a community, then it is important to document whether knowledge did, in fact, change. This is challenging, and program planners will benefit from working with an evaluator or someone who knows how to gather the necessary information to demonstrate whether the results were achieved. Even more challenging to document, if changes were achieved, is whether these changes can be attributed to the program or initiative that was implemented. The basic theme of the evaluation is that the more precise or clear the program planner is with regard to what is desired (what the audience should know, feel, or do), the easier it is to document whether this, in fact, occurred. With evaluation, multiple approaches can be undertaken (Anderson, 2008). A simple way of viewing these is to think in terms of “quantitative” approaches and “qualitative” approaches. Typically, quantitative approaches are those that involve numbers, and qualitative approaches are those that involve interactions with others. Although overlap exists (i.e., an evaluator can codify observations or qualitative responses), it can be helpful to think about gathering both types of information with regard to the communications efforts. One way of thinking about what and how to evaluate is to definitely have some numbers because policy makers and decision makers like to have “data” they can grasp. It is also helpful to have illustrations or examples, such as stories, testimonials, and quotes provided by individuals; these help provide the flavor for what the data indicate. Using both types of data together can be very powerful. Multiple approaches exist for gathering the information. What is important is to design evaluation approaches that

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TABLE 30.11 Evaluation Strategies Advantages

Disadvantages

Survey

Easy to complete Can be completed quickly by respondent Simple to score Provide quantitative results Easy to keep anonymous Can compare with existing data sources Automated scoring (optical scoring, online) Low-cost approach

May not capture respondent’s feelings Some responses may not reflect accurately Questions may have different interpretations For subanalyses, requires professional skills May be avoided by respondent if intrusive Qualitative data need data coding and analysis

Online survey

Easily include extra questions with existing survey Easily incorporate questions with locally developed survey Questions can be brief and limited in number Random list of individuals easily generated Data review is quick and easy to accomplish Easy to keep anonymous Low cost

Respondents less likely to complete if lengthy May need incentive to encourage participation May not capture respondent’s feelings Respondent may avoid if intrusive or irrelevant Qualitative data require work with data entry Requires coding and analysis

Interview

Provides rich insight Respondent can interpret insights Respondent may speak honestly Respondent may feel honored to be interviewed Provides an opportunity to probe thoughts

Requires skilled interviewer Respondent may not speak honestly Approach can take time Respondent may have limited time Challenges with coding and analysis

not only help document the program results but also can be done within existing resource, time, and logistical constraints. Because no single approach is best, and levels of experience and budgets vary, it is helpful to consider a variety of evaluative strategies, each of which has its own strengths and weaknesses. Table 30.11 documents advantages and disadvantages for six popular evaluation approaches; this helps planners make appropriate choices based on how the evaluation will be used, what is needed, and available resources and time lines. Once the evaluation results are obtained, program planners can assess what went well and what could be improved. After examining the process evaluations, assessments can be made about potential changes in factors such as approach, efficiency, cost, effort, and distribution. The outcome evaluations can provide results linked to what is wanted (the “know, feel, and do” factors); this includes documentation regarding the knowledge, attitudes, behavior, intentions, perceptions, and any other results being monitored by the program planners. A specific example of an assessment tool is shown in Figure 30.2. This is prepared to gather information about the reach and impact of a communication campaign. It can be included as part of another methodology (e.g., added to an existing paper or online survey), or it can be used as a stand-alone approach to gather information about a campaign. The results of this tool suggest whether

individuals in the community even saw the campaign on the identified topic and, if so, what impact they believe it made. Although this does not capture the actual impact, it does assess respondents’ perceptions of the impact. This type of information can help program planners as they review and refine the strategies used. Overall, the review of all results obtained from various sources is helpful because program planners can thus determine whether and where changes may be needed. Thus, if a traffic safety awareness campaign was implemented and the evaluation showed that community members were not aware of it, it will be helpful to understand why this was the case. Similarly, if a workshop was conducted on the proper and safe installation of child safety seats and participants were no better prepared at the end of the workshop than at the beginning, then modifications to the workshop’s approach are necessary. The aim is to get increasingly closer to obtaining the desired outcome (vis-a`vis “know, feel, and do”) that is linked to the awareness effort being implemented.

11. CONCLUSION Preparing effective communication concerning traffic safety sometimes seems like a daunting challenge. Although progress has been achieved for some traffic safety issues (e.g., deaths due to drunk driving have been

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Which of the following approaches about [traffic safety topic] have you seen this year?

Major Countermeasures to Reduce Risk

If you saw the [traffic safety topic], what impact did it have on you? Made me

Saw in the

Did not

community

see

No impact

think about

Made me

my

change my

behavior

behavior

Posters Television advertisements Newspaper advertisements or articles Information display Presentations/workshops about impaired driving Website or email Other: (please specify) FIGURE 30.2 Assessing a communication campaign

reduced), new areas of concern appear (e.g., texting while driving). With all the efforts implemented to achieve results with any specific issue, changes often are limited and take a long time to achieve. One reason for this is that persuading or convincing people to change their behavior is, in fact, challenging. Another reason is that many of the strategies used, although well-intended, have not been grounded thoroughly or implemented with sound planning. The persuasive communication planning model introduced in this chapter is designed to organize the thinking of program planners. The model’s first step, focusing on the need and the target audience, is a basic foundation. Audience strategies vary based on a range of demographic factors, as well as a clear understanding of their needs and interests. Step 2 highlights the importance of gaining a solid theoretical foundation regarding what might affect the target audience, including understanding basic assumptions regarding these individuals. Third, preparing a plan for the most appropriate strategies and messages is vital. The fourth step involves developing the content, including a range of approaches useful within the context of an overall campaign. Step 5 addresses overall planning activities, helping to organize how and when specific strategies will be implemented. This also involves testing how well the

messages and approaches resonate with the target audience. Steps 6 and 7 involve the implementation of the activities and monitoring how they proceed as well as how they are received. These results are important, particularly because they can be helpful in identifying what is worthy of continuation and what needs refinement. Thus, the review process leads directly to some regeneration of the product and process and some rethinking about the initial steps of understanding the audience and what might “work” with them. Certainly, no magic answers or silver bullets exist for persuading others about the desired outcome of increased traffic safety. The role of risk perception and decisionmaking processes are central to this process. The responsibility of traffic safety leaders and program planners is central to enhancing the awareness, skills, and ultimately the behavior of the various publics served. What is needed is greater clarity in defining and planning what is necessary for the public to “know, feel, and do.”

REFERENCES Anderson, D. S. (2008). IMPACT evaluation resource. Fairfax, VA: George Mason University, Center for the Advancement of Public Health. Bensley, R., & Brookins-Fisher, J. (2009). Community health education methods: A practical guide (3rd ed.). Boston: Jones & Bartlett.

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Berg, H.-Y. (2006). Reducing crashes and injuries among young drivers: What kind of prevention should we be focusing on? Injury Prevention, 12, i15ei18. Clear & simple Developing effective print materials for low-literate readers. (1994). Washington. DC: National Cancer Institute, National Institutes of Health. Communicating in a crisis: Risk communication guidelines for public officials. (2002). Washington. DC: U.S. Department of Health and Human Services. Drug abuse prevention: What works. (1997) (1997). pp. 10e15. Rockville. MD: National Institute of Drug Abuse. Glanz, K., Rimer, B. K., & Su, S. M. (2005). Theory at a glance: A guide for health promotion practice (2nd ed.). Washington, DC: National Cancer Institute, National Institutes of Health. Goldenbeld, C., Twisk, D., & Houwing, S. (2008). Effects of persuasive communication and group discussions on acceptability of antispeeding policies for male and female drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 11(3), 207e220. Gordon, R. (1987). An operational classification of disease prevention. In J. A. Steinberg, & M. M. Silverman (Eds.), Preventing mental disorders (pp. 20e26). Rockville, MD: U.S. Department of Health and Human Services. Lonero, L. P. (2007). Finding the next cultural paradigm for road safety. In Improving traffic safety culture in the United States: The journey forward (pp. 1e20). Washington, DC: AAA Foundation for Traffic Safety. McNeely, C. L., & Gifford, J. L. (2007). Effecting a traffic safety culture: Lessons from cultural change initiatives. In Improving traffic safety

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culture in the United States: The journey forward. p. 23. Washington, DC: AAA Foundation for Traffic Safety. Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51(3), 390e395. Ramos, P., Diez, E., Perez, K., Rodriguez-Martos, A., Brugal, M. T., & Villalbi, J. (2008). Young people’s perceptions of traffic injury risks, prevention and enforcement measures: A qualitative study. Accident Analysis and Prevention, 40(4), 1313e1319. Slovic, P. (2000). The perception of risk. London: Earthscan. Tay, R. S., & Ozanne, L. (2002, Summer). Who are we scaring with high fear road safety advertising campaigns. Asia Pacific Journal of Transport 1e12. Whittam, K. P., Dwyer, W. O., Simpson, P. W., & Leeming, F. C. (2006). Effectiveness of a media campaign to reduce traffic crashes involving young drivers. Journal of Applied Social Psychology, 36(3), 614e628. Wong, J. T., Chung, Y.-S., & Huang, S.-H. (2010). Determinants behind young motorcyclists’ risky riding behavior. Accident Analysis and Prevention, 42(1), 275e281. Wundersitz, L. N., Hutchinson, T. P., & Woolley, J. E. (2010). Best practice in road safety mass media campaigns: A literature review. Adelaide, South Australia: Centre for Automotive Safety Research. Yanovitzky, I., & Bennett, C. (1999). Media attention, institutional response, and health behavior change: The case of drunk driving, 1978e1996. Communication Research, 26(4), 429e453.

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Chapter 31

Enforcement Bryan E. Porter Old Dominion University, Norfolk, VA, USA

1. INTRODUCTION Ken Burns is one of my favorite documentarians. Recently, I discovered a lesser known Burns’ documentary profiling Horatio Nelson Jackson. Jackson and his driver and mechanic, Sewell Crocker, were the first to drive “the horseless carriage” across the United States (Burns, 2003). The year was 1903, and on a $50 wager, Jackson promised he could drive from San Francisco, California, to New York City, New York, in less than 3 months. Jackson and Crocker made the drive in 63 days. They accomplished this feat at times without roads to drive on. They survived numerous and costly breakdowns that required parts to be shipped to wherever they happened to be before the automobile, named the Vermonter, would function again. They persevered and changed history. The United States’ transportation system in 1903 was dominated by horse-drawn carriages and train locomotives. Due in many ways to Jackson, the automobile would capture the country’s attention and solidify the future’s reliance on automobile transportation. In addition to being fascinated with Jackson’s tale, I was surprised (and amused, truthfully) about his arrest for speeding. Sometime after returning to his home town of Burlington, Vermont, Jackson was arrested and required to pay a $5 fine and court costs for driving 6 mph (approximately 10 kph). This was 6 mph itself, not 6 mph over the limit. The speeding law was written to prevent horses and earlier forms of transportation from moving faster than this in an urban environment. It made me wonder how strange it must have been for traffic police to give tickets for horseand-carriage violators one minute and then see Jackson speeding by at 6 mph in an automobile the next. Then, as now, enforcement is a cornerstone, if not the cornerstone, countermeasure to control driver behavior. Enforcing traffic laws is an important consequence intervention targeting driving behaviors deemed unsafe and risky in terms of how they increase the chances for the driver or others to be harmed. Yet, just as in Horatio Jackson’s time, enforcement’s effectiveness varies by the strength of the law enforcement activities. I offer this chapter to readers interested in reviewing the role enforcement plays in society, Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10031-1 Copyright Ó 2011 Elsevier Inc. All rights reserved.

both its effectiveness and its challenges. I suggest future directions for enforcement studies. First, however, I open with a brief discussion of the components of enforcement. Enforcement begins with legislation, and it is certainly true that the strength of the law makes a difference. Second, enforcement continues with police officers using the law to cite violators with tickets and arrests. Third, courts have opportunities to review cases, with judicial outcomes carrying out the sentences, dismissing the charges, or providing some decision in between (reduced sentencing). These three components make up the enforcement system, but I focus on the officer enforcement role, which has received the most study and is probably the most controlled by traffic psychologists, who can partner with officers in roadway interventions. I continue the chapter with a discussion of the two theoriesdclosely relateddthat are the backbone of the enforcement process. I spend more time and space here than perhaps one would think, but I do so to show how enforcement fits within a larger system of behavior change. Taking time now actually helps me when I suggest an alternative enforcement technique near the end of the chapter. After the theoretical introduction, I proceed with a brief discussion of two types of law enforcement: traditional (officer pulling a violator over to issue a ticket) versus automated. Perhaps the most studieddand sometimes contesteddarea of enforcement throughout the world is the use of automation to identify violators, notify police, and issue citations after police review. “Photo radar” for speed and “photo red” for red light running have received more international attention in recent years and, at least in the United States, are often the focus of significant political debate. I continue with brief reviews of enforcement’s impact on key traffic violations. I combine traditional and automated research here. Very good reviews have preceded this chapter, and I refer readers to these while providing only an overview. The impact of enforcement on crashes, injuries, and fatalities is considered in a separate section, too. In my experience, having some information about reductions in 441

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crashes is important for convincing lawmakers to consider making lawsdor strengthening existing onesdto enhance enforcement options. Persuading them that enforcement reduces a risk behavior is often insufficient if one cannot show them that the risk behavior to be legislated against leads to actual, physical tragedy. Finally, I conclude the chapter with challenges to enforcement and future considerations for alternatives and research. To be truthful, I do not have much optimism that, in the immediate future, traffic psychologists will have the will or the support to consider empirically these challenges, alternatives, or future directions. At least in the United States, the dominant support for enforcement activities comes from the states, which in return receive mandates from the National Highway Traffic Safety Administration (NHTSA), part of the federal government’s Department of Transportation. NHTSA has every reason to push the status quo in enforcement, which is heavily dependent on highly visible police giving tickets supported by mass media programming and community partnerships. After all, such programs, with the latest edition known as Click It or Ticket, have shown success (Reinfurt, 2004; Tison & Williams, 2010). These federal programs have significant room for improvement, but for now ticket-giving enforcement is a key component of large-scale, traffic safety programs.

2. THE ENFORCEMENT SYSTEM The enforcement system begins with legislation. The law dictates conditions under which a behavior is in violation and the fines that are allowed to be assessed. In traffic settings, the strength of the law has been particularly important. In the United States, for example, there is significant interest in primary versus secondary laws. Primary laws are those allowing police officers to stop a motorist for a given violation and issue a citation (standard enforcement; Eby, Vivoda, & Fordyce, 2002). Secondary laws, on the other hand, are those that require an officer to stop a motorist for another, primary violation before he or she is allowed to issue a citation for a violation. States vary on what behaviors are primary or secondary enforced. The one receiving the most attention is safety belt use. Currently, 31 states and the District of Columbia have a primary law for enforcing adult safety belt use. All other states have secondary laws for adults, except New Hampshire, which has no adult use law (New Hampshire requires restraint use through age 5 only; after this, there is no legal requirement) (Insurance Institute for Highway Safety (IIHS), 2011a, 2011b). Note that these laws are separate from child restraint laws, which involve all states and the District of Columbia. There is good evidence that enforcement under primary law systems is associated with higher safety belt use rates. States with primary laws have an average rate

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approximately 10 percent points higher than those with secondary (Beck & Shults, 2009; Houston & Richardson, 2006). Eby et al. (2002) documented that, for Michigan, belt use increased more than 13 percent points immediately after the primary law took effect. One year later, the use rate remained 10 percent points higher than when Michigan had the secondary law. Laws are also critical for the enforcement of impaired driving, as a second example. Blood alcohol content (BAC) limits are significantly related to fatal crashes. Specifically, Wagenaar, Maldonado-Molina, Ma, Tobler, and Komro (2007) estimated that 360 fewer fatalities per year occurred in 28 U.S. states as a result of the BAC limit being lowered from 0.10 to 0.08. They estimated that 538 more people per year could be saved if the BAC limit was lowered further to 0.05. The second component of the system, the enforcement of the law on the roadways, is the main focus of this chapter. I set this aside momentarily. Then there is the third component, the judicial system. Laws are enforced by officers writing tickets or making arrests, which then are upheld or not by courts. Of the three components, the judicial system is the least covered or focused on by traffic psychology. I have thus far ignored this component in my own research not because it is unimportant but, rather, because it is extraordinarily difficult to influence. Psychologists can influence legislation by offering scientific evidence about the effectiveness of different policies. They can influence enforcement by officers by assisting with identification of high-risk populations, persuasive media messages, and, occasionally, enforcement techniques. However, in my experience, judges are, mostly for good reason, staunchly independent actors. They listen to the science of the system that brings a case to their courtroom, but they maintain their discretion within the limits of the law. Whether psychologists believe or not that reckless driving via high speeds should receive significant punishment, a judge will decide based on many aspects of the person’s life what punishment seems fair, even if that punishment theoretically may be ineffective in preventing future violations (e.g., reducing such a speeding charge to faulty equipment if a driver agrees to have the speedometer calibrated; this is hardly a disincentive to stop speeding). Laws and courts are important components of the enforcement system, and law enforcement officers’ effective work depends on the strength of the laws (e.g., primary vs. secondary and the level of fines) and court actions (will tickets be upheld, dismissed, or reduced to a lesser offense?) However, I now focus almost solely on the officer and the issuance of citations for violations, leaving legislation and judicial considerations for other authors. The work of the officer is where most of our literature resides. This is where the theories of enforcement mostly apply.

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3. THEORETICAL BASES FOR ENFORCEMENT 3.1. Deterrence Theory Enforcement as an activity for social control can be explained by two main theoretical structures, one from sociology/criminology and the other from psychology. Deterrence theory, from the former, explains the importance of punishment and punishment avoidance to deter the occurrence of illegal activity (Stafford & Warr, 1993). Deterrence theory can then be subsumed within the larger learning theory that has been a focus of experimental and quasi-experimental applications in psychology since the early twentieth century. Specifically, defining deterrence in terms of the consequences of punishment and punishment avoidance places “deterrence” into the larger learning theory system, which discusses punishment and reinforcement. Social learning can also contribute (Akers, 1990). For parsimony, deterrence theory gives way to learning theory in this chapter.

3.2. Learning Theory From psychology, enforcement’s effectiveness is best explained by learning theory and even more precisely by operant conditioning theory. Behavior is shaped by antecedents (conditions that occur before the behavior of interest) and consequences (conditions that occur after the behavior that increase, or decrease, the probability of the behavior occurring again; Kazdin, 2001). One of the more descriptive examples of operant conditioning comes from Mattaini (1996), who offered the interlocking contingency model. This model assimilates the

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five common antecedents studied in the behavior analytic literature. Table 31.1 lists these antecedents and their general descriptions. I provide examples of how such antecedents may apply to traffic settings. Three of these antecedents have particular importance for the effectiveness of enforcement. The first is “rules.” Rules are societal and culturally driven, and they contain regulations proscribed by customs, social norms, and laws. Laws, as reviewed previously, are the first step to effective enforcement. More important, many people will alter their behavior to come into compliance with the law without necessarily having to receive a ticket from an officer (Dinh-Zarr et al., 2001). Furthermore, there is a significant interest in behavioral psychology on “rule-governed behavior” as an explanatory factor for why people will change their behavior without being reinforced or punished for it directly; rules can imply consequences that influence action. Interested readers should refer to Baum (2005) for an excellent overview of how rules work in behavior change. The second antecedent of importance to enforcement is modeling. Witnessing what happens to others enhances our learning. This “vicarious conditioning” from social learning theory compliments operant conditioning in that it acknowledges we can be reinforced or punished by watching the consequences of others’ behavior (Akers & Jensen, 2003). And, social learning is important for deterrence (Akers, 1990). The third key antecedent involves law enforcement officers being “occasions,” or discriminant stimuli. Officers in cars along the road are signals to us that we should slow down or likewise drive legally or more safely. They signal to us that tickets could be written, and we quickly remove our foot from the accelerator or apply brakes as behaviors we have learned (that have been conditioned) so that we avoid the ticket.

TABLE 31.1 Antecedents Affecting Behavior with Specific Traffic Examples* Antecedent

Description

Traffic Example

Occasion

Signals to individual that reinforceable behavior is appropriate, or signals that a behavior if performed will be punished (an inhibitor)

A red stop light: Signals the driver to stop, or possible punishment will occur

Rule

A law that is passed; a social norm that has evolved in the culture

Primary safety belt law

Models

Individuals witness others receiving consequences for behavior

Witnessing an officer pull another driver over for speeding; having a friend who received a ticket from automated enforcement

Structural antecedent

Relatively permanent stimuli, from the person (personality characteristics) or environment (infrastructure)

A driver’s mental health or affect affects driving (e.g., see Chapter 13 of this handbook); narrower roads slow speeds (e.g., traffic calming)

Establishing operations/ motivating condition

Situation that affects consequence sensitivity; affects motivation

Fatigue creating likelihood of running red light

*Antecedents from Mattaini (1996).

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Our behavior to avoid a ticket, as well as the actual behaviors we continue to do or not do in response to receiving tickets, can best be explained with consequences, or the outcomes of those behaviors. The two consequences affecting behavior are reinforcement and punishment. First, defining these terms is necessary because they are easily and often misapplieddeven by psychologists. Reinforcement is any stimulus that strengthens a behavior. Usually, this means it increases the likelihood a behavior will occur again (Skinner, 1953). There are two types of reinforcement: positive and negative. Positive reinforcement increases behavior through the application of a pleasant or desirable stimulus following desired behavior. Daniels and Daniels (2004) described it as getting something you want for your behavior. Negative reinforcement also increases behavior, but it does so through the removal of a painful or noxious stimulus. An example is buckling your safety belt when you see a police officer (an “occasion”) in order to avoid a ticket. Deterrence theory relies, in part, on negative reinforcement. You are deterred from acting a certain way (you avoid it) because your loss (funds or freedom) was unpleasant when you did not escape previous tickets. Punishment is any stimulus that decreases the likelihood its previous contingent behavior will occur again. Positive punishment decreases behavior by presenting something undesirable following the occurrence of the target behavior (Daniels & Daniels, 2004). An example is receiving a ticket for speeding. However, note that receiving tickets is punishing if and only if speeding behavior is less likely to occur again. Negative punishment also decreases behavior, but it does so by removing something desirable. For traffic safety, negative punishment may be suspending one’s driver’s license or impounding the vehicle as a consequence for drinking and driving. Daniels and Daniels provided a shorthand to denote the four different types of consequences, which is used henceforth in this chapter when relevant (Table 31.2). Much has been written about which consequence technique is most effective in producing a target behavior. The

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behavioral literature supports the more direct use of Rþ practices to shape and create desired behavior. People work for things they want (freedom to speed on the highway and get places more quickly), and such a motivation may be more powerful than the motivation to avoid getting caught (R-). Working to get what one wants may be more motivating than concern about punishment (fines or jail; Pþ). Daniels and Daniels (2004) argued that performance improves when Rþ is manipulated and applied to strengthen desired behaviors. People are being positively reinforced for their preferred behaviors whether we manipulate Rþ or not, and this contingency control may be stronger than control via Pþ and R-. Skinner (1953) warned that punishment only temporarily suppresses undesirable behavior. Therefore, if someone is punished for not wearing a safety belt, he or she will not necessarily increase belt use as a habit in the future. Rather, some other behavior (e.g., buckling up when an officer is seen but unbuckling otherwise) may be strengthened. Punishment should be used as a last resort, with “other procedures [being] employed in advance of punishment” (Kazdin, 2001, p. 238). If society wishes to increase appropriate behaviordand safer driving practicesdit should at a minimum deploy Rþ techniques to supplement more traditional Pþ enforcement approaches. However, for several reasonsdsome of which are discussed laterdenforcement of laws usually relies on Pþ, with tickets being given to reduce the likely occurrence of the behavior in the future. The loss of funds or sometimes jail time for egregious offenses (P-) is also important. Thus, punishment is the norm, not the exception, for controlling the roadwaysdand most other social conventions monitored by governmental agencies. Drivers as a result learn to avoid punishment (R-). In the end, drivers’ changes resulting from enforcement may be only temporary (Skinner, 1953) and reliant on continuous enforcement for those changes to be maintained. A significant challenge to enforcement is to maintain the suppression of undesirable behaviors; a more

TABLE 31.2 Daniels and Daniels’ (2004) Codes for the Four Consequences with Specific Traffic Examples Code

Description

Traffic Example

Rþ

Positive reinforcement (e.g., giving something desirable; increases behavior)

Feeling pleasure when speeding and speeding more in the future

R-

Negative reinforcement (e.g., taking away something undesirable; increases behavior)

Slowing down to avoid a speeding ticket when witnessing police on the road and being more likely to act similarly in the future

Pþ

Positive punishment (e.g., giving something undesirable; decreases behavior)

Receiving a ticket for red light running and being less likely to run a red light in the future

P-

Negative punishment (e.g., taking away something desirable; decreases behavior)

Losing a vehicle when it is impounded after an impaired driving arrest and being less likely to drink and drive in the future

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significant challenge is for enforcement to create lasting change and new safe habits. Even so, an important side effect of Pþ strategies deployed extensively throughout communities is the possibility of increasing perceptions of getting caught (Siegrist, 2004). Fuller and Farrell (2004) further supported this notion. They found that awareness of high enforcement was linked to a greater perception of getting caught for speeding and not wearing seat belts compared to control locations. Perceptions of getting caught for drunk driving were higher for controls, although Fuller and Farrell argued this may have been due to the controls not knowing much about the program but being influenced by other national efforts to reduce drink driving. Either way, drivers perceive Pþ strategies to which they are vulnerable and as a result may behave in ways controlled by R- to avoid getting caught. In other words, if drivers perceive significant enforcement to reduce speeding, they may drive more slowly as a reaction. They may do this even though the chances of getting caught themselves are actually quite small.

4. THE TECHNOLOGY OF ENFORCEMENT: LIVE OFFICERS VERSUS AUTOMATION One of the most common debates among traffic interventionists, policy makers, and the public community involves the role that automated enforcement playsdor should playdin traffic enforcement. Automation via cameras taking pictures of violators is often compared to live officers doing traditional work of patrolling streets and issuing tickets. Although it is a misnomer to think automation works without live officers (in Virginia and in many other jurisdictions, an officer must review the tickets and judge their validity before issuance to violators), the debate usually ignores this and focuses on technology’s perceived intrusion into privacy (for an early discussion on this concern, see Harper, 1991), unfairness (Wells, 2008), and relationship to increasing certain traffic crashes (i.e., rear-end crashes; Erke, 2009) versus data suggesting reductions in violations and the more serious crashes associated with injury (i.e., reducing injuries from red light running; Hu, McCartt, & Teoh, 2011; Retting, Ferguson, & Hakkert, 2003). There is much to support automated enforcement, from theoretical to practical reasons. Theoretically, automation allows drivers to learn more quicklydand perceive with greater assurancedthat an illegal behavior will be caught each time it occurs. Learning proceeds most efficiently with consistent and contingent associations of stimuli, behavior, and consequences (Chance, 2009; Daniels & Daniels, 2004). Practically, too, automation is beneficial to officers’ safety. Some road segments and intersections are too busy, too large, and too dangerous for officers to effectively deter some violations, such as red light running. Attempts to do

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so place officers at risk as well as risk the safety of motorists pulled over and others attempting to avoid stopped vehicles along the roadside. Mobility is affected by live officer patrolling; depending on the road segment, motorists pulled over can create traffic slowdowns. Regarding the detractors to automated enforcement, although the complaints must be evaluated, they typically have little merit. Citizens in London (including its boroughs) should expect to be watched by more than 7,400 cameras (“The Statistics of CCTV,” 2009), whether they are violating the law or not. Citizens in the United States are watched by an unknown number of security cameras in stores, fuel stations, public and private buildings, and banks. We become accustomed to these cameras without public outrage. Automated enforcement activates when a threshold is surpassed, whether that threshold is a speed or intersection loop triggered by a vehicle entering on a red signal. Violations are the focus, with legal behavior typically not saved to film or network. Furthermore, the crashes related to installation of automated enforcement may result from the novelty of the technology and drivers learning about the enforcement technique and applying brakes to stop, causing less aware drivers who are following these drivers to hit them. Rear-end crashes often increase after installation of traffic lights as well (Short, Woelfl, & Chang, 1982), but there are fewer arguments against traffic lights and less public outcry over unintended crashes caused by traffic lights. Unfortunately, there is not enough space for a full review and debate on automated enforcement in this chapter. For now, I share automated enforcement programs equally with traditional, live officer approaches as a tool in the enforcement arsenal. Also, as you will read, automated enforcement is an effective tool indeed. This is fortunate because currently there are more than 500 communities in the United States using red light cameras and more than 80 using speed cameras (IIHS, 2011a). In addition, at least 12 countries throughout the world have automated enforcement for speeding or red light running that can be evaluated (Wilson, Willis, Hendrikz, Le Brocque, & Bellamy, 2010).

5. ENFORCEMENT EFFECTIVENESS IN REDUCING RISK BEHAVIORS Enforcement as a tool has been extensively studied and has been the subject of very good reviews and book sections devoted to its effectiveness. I highlight only major findings to give readers some information about how enforcement affects three key problem behaviors: safety belt non-use, impaired driving, and speeding.

5.1. Safety Belt Use It is worth noting that one behavior we often wish to increase is safety belt and child protection use. Enforcement,

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technically, does not increase restraint use. It, as a punisher, reduces non-use. This nuance, although accurate technically, in practice is cumbersome to explain. We are much more accustomed to reading about increases in belt use, not decreases in non-use. Therefore, I discuss how belt use rates increase with enforcement programs but only to comfort the reader, not in ignorance of learning theory’s sophistication. Safety belt use, actually, is the first behavior for review. Safety belt use reduces the risk of injury and death by 45e50% (Elvik & Vaa, 2004). Vivoda and Eby (see Chapter 16) also demonstrate the significant saving in lives resulting from safety belt use. As a result of safety belt use effectiveness, this behavior is a major focus of enforcement programs throughout the world. There are several exceptional reviews of safety belt enforcement programs. Dinh-Zarr et al. (2001) reviewed studies of enhanced enforcement, or programs that put additional officers on patrol (e.g., selective traffic enforcement programs). The 15 studies that measured safety belt use found an 8e24% increase after enforcement activities. The two studies that examined fatalities found a 7e15% decrease. Elvik and Vaa’s (2004) review, from their comprehensive Handbook of Road Safety Measures, also found belt use increases ranging from 13 to 20%. In addition to increasing belt use for the general driving and passenger public, enforcement has increased belt use for nighttime occupants. Chaudhary, Alonge, and Preusser (2005) documented a six-point increase (from 50 to 56%) in front-seat belt use after a nighttime enforcement program. Nighttime belt use tends to be lower than daytime use (Chaudhary et al. reported the daytime rate in their study in Reading, Pennsylvania, to be 56%). In addition, Vivoda, Eby, St. Louis, and Kostyniuk (2007) found that daytime-only enforcement programs do not affect nighttime use rates, giving more importance to the need for separate and effective nighttime programs as evaluated by Chaudhary et al. Furthermore, enforcement can be effective even in states with secondary laws for belt use rather than primary (Vasudevan, Nambisan, Singh, & Pearl, 2009). Recall that in secondary states, officers must use another primary violation first as a reason to stop a motorist. Enforcement strategies, coupled with mass media applications are the backbone of most society-level programs to increase belt use. What seems clear from the literature is that these high-visibility enforcement efforts, if they are regularly used and have sufficient legal support with fines, have been successful in increasing belt use rates. The punishment created by non-use can lead drivers to buckle up to avoid the tickets and fines. Although there are problems with punishment-based approaches, as mentioned previously and discussed again later, it is clear that enforcement is a key component for increasing safety belt use rates for much of the population. In fact, Williams

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and Wells (2004) argue strongly that enforcementd highly publicized, primary, and with “enhanced penalties” (p. 179)dis what will be needed to reach the non-users who thus far have not responded to belt use messages.

5.2. Impaired Driving Shults et al. (2001) reviewed 11 studies of selective breath analysis at checkpoints and found evidence for a 13e27% decrease in all crashes likely to involve alcohol. Elvik and Vaa’s (2004) meta-analysis of 26 studies published from the late 1970s to the mid-1990s found that traffic enforcement reduced drunk driving fatal crashes by 9% and injury crashes by 7%. They also found that suspending drivers’ licenses can reduce such crashes by 18%. In addition to fines and even jail time for severe impaired driving, one other enforcement outcome can include the removal of driver’s licenses and even vehicle impoundment. Society’s decision to remove a driver’s license or vehicle is the outcome of judicial proceedings at some level, not necessarily the work of the officer. The role of the officer and the perception of the officer’s ability to catch impaired driving are influenced by what are known as ALR (administrative license removal) laws. One concern with ALR programs has been economic: Will drivers who are told they cannot drive suffer economically by the punishment? That is, will they suffer undue harm to their job security? Ironically, Knoebel and Ross (1997) suggested that there is little reason for such drivers’ jobs to be of concern, mainly because drivers continue to drive despite loss of license. They perceive the risk of getting caught without the license to be low. Daniels and Daniels (2004) highlighted the importance of “certainty” in a stimulus’ strength in controlling behavior. Enforcement certainty or at least perceived certainty, particularly for impaired driving reductions, seems critical for punishment to be effective. Voas and DeYoung (2002) reported that 75% of such drivers with license revocations continue to drive. They reviewed a slightly different administrative outcome in vehicle impoundment or related decision (e.g., immobilization and forfeiture). Many states apparently have such laws, but few were found to enforce them in ways that could be evaluated. Voas and DeYoung, however, were able to evaluate different programs in Canada and the United States. For example, impounding vehicles in California reduced repeat offenses by first-time offenders 24%. Repeat offenders were also impacted. There were 34% fewer convictions 1 year after impoundment. Vehicle seizure was related to 50% fewer re-arrests than nonseizures in Portland, Oregon. Although these and similar findings are positive, Voas and DeYoung noted that many more communities and states have laws on the books to impact vehicle use but have not applied them well enough

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for evaluation. This area of enforcement is well worth additional attention in future research.

5.3. Speeding Elvik and Vaa’s (2004) review of speed enforcement demonstrates that, as for the other behaviors, the countermeasure is effective. Specifically, in the 16 reviewed studies, fatal crashes were reduced by 14% and injury crashes by 6%. More interesting, a good deal of the research on speed enforcement is now focusing on automated techniques. For example, Stradling, Martin, and Campbell (2005) reviewed the effects of speed cameras on attitudes and behavior in Glasgow, Scotland. In one example, the percentage of speeding was reduced from 64% at baseline to 31% a year after cameras were introduced. Retting, Farmer, and McCartt (2008) likewise found support for speed camera enforcement. They documented a 70% reduction in speeding 10 mph over the limit with the use of cameras and signs compared with 39% with signs only and 16% on streets without either. Thomas, Srinivasan, Decina, and Staplin (2008) found that speed cameras reduced injuries by 20e25%. Finally, Chen (2005) documented the cost-effectiveness of a photo radar program in British Columbia. He estimated that approximately $32 million (in 2005 conversion) was saved in insurance claims.

6. ENFORCEMENT EFFECTIVENESS IN REDUCING CRASHES AND CASUALTIES In addition to enforcement reducing unsafe behaviors, which is important in its own right, applied researchers are often asked by policy makers and government agencies whether enforcement leads directly to reductions in injuries and fatalities. Answering this question usually requires large data sets and longitudinal designsdboth relatively expensive to obtaindincreasing the value of the efforts required to complete the work. Fortunately, the literature is showing the results of the extensive efforts in this area, and indeed, in addition to reducing risk behaviors, enforcement has an impact on casualties. A couple of examples were given above, but others include Wells, Preusser, and Williams (1992), who found checkpoints reduced crashes by 6% and injury crashes by 16%. Williams, Reinfurt, and Wells (1996) found that had a safety belt enforcement program (Click It or Ticket) not occurred as evaluated in North Carolina, 45 more people would have died and 320 would have been injured in the 6month period post-program. Natural experiment opportunities have presented themselves to test enforcement’s impact on traffic casualties. For example, in 2010, the Fairfax Police Department in

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Virginia found that its officers were writing fewer tickets as a result of a new computer procedure to process each ticket written; the process was slowing officers’ ability to write their normal ticket numbers while they learned the procedure (Jackman, 2010). That interruption has not yet been fully evaluated; however, a situation arose in Quebec that was evaluated. Specifically, Blais and Gagne´ (2010) evaluated the impact of 61% fewer citations written by Quebec traffic officers. The reduction occurred during a 21-month period when police officers slowed citation production during union negotiations for a contract. Their analysis concluded that an average of 8 more injury crashes occurred each month during the period of lower citation rates, with a total of 239 more traffic injuries overall than would have been expected at normal citation rates. Uninterrupted enforcement likewise reduces fatalities. Redelmeier, Tibshirani, and Evans (2003) found, from a large (N ¼ 8975) Canadian study, a 35% lower relative risk of fatality up to 1 month after receiving a traffic citation. These gains recidivated during the next 3 to 4 months. The authors suggested more frequent enforcement to maintain the effect. However, their data suggest that a life is spared every 80,000 convictions, an emergency room visit prevented every 13,000 convictions, and $1,000 saved every 13 convictions. Not all research is positive concerning the effects of enforcement on reducing crashes. Dula, Dwyer, and LeVenre (2007) noted that there has been little change in drunk driving numbers since the late 1990s. Furthermore, they reviewed efforts and data sets in Tennessee and, while acknowledging limitations, found a striking lack of relationship between driving under the influence (DUI) enforcement arrests and DUI crashes. Among many suggestions, they argue that enforcement and the arrest rate must increase in order to increase the perception of getting caught for the behavior. They further suggest that social marketing and mass media are critical to increase these perceptions. Despite this latter study, the preponderance of evidence suggests enforcement does result in crash, injury, and fatality reductions. What seems key, however, is sufficient enforcement that leads to perceptions of risk in getting caught. Increasing the perceptions in the public that enforcement is prevalent is one of the challenges facing enforcement programs and theories.

7. CHALLENGES FOR ENFORCEMENT EFFECTIVENESS 7.1. Perception of Vulnerability A key component for effective enforcement is the perception, or belief, that one will get caught for performing an illegal behavior. This was mentioned previously. However,

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what happens when a driver violates a law but escapes punishment from an officer present? For example, many drivers speed on the roadways, but only a few are actually stopped by traditional law enforcement. There are too many violators for an officer to stop. It becomes a game of chance, and the odds are in favor of the driver. Evans (1991) reviewed the chances of an individual receiving a police violation. On average, this occurs once every 6 years. This may be quite insufficient to alter most drivers’ perceptions of risk. Beck and Moser (2006) found that personal exposure to a sobriety checkpoint actually reduced a person’s feelings of vulnerability, more so than had that person heard about the checkpoint experience from another person. This may have been the result of how checkpoints are run. The authors argue that the quickness in moving drivers may lead to the impression that the checkpoints are not effective. More challenging was the finding that few (12%) were exposed at all to checkpoints, and only 21% had even heard of the area program targeting impaired driving (Checkpoint Strikeforce). Enforcement-based programs that cannot reach the community to alter perceptions are unlikely to succeed in changing behavior. This challenge is one reason for the effectiveness of automated enforcement. Well-designed photo enforcement systems are better able, compared to traditional enforcement strategies, to catch a violation each time it occurs and create the means for officers to ticket each violation. Theoretically, such a process that is consistent in associating a consequence with a behavior will create more efficient behavior change. However, currently, automated enforcement for impaired driving is unavailable (automated systems via ignition interlocks are available to prevent the start-up of a vehicle if the driver’s BAC is higher than a set limit; this is a type of punishment that does not result in further legal consequences via tickets, fines, points on license, or judicial decisions but it is inconvenient to the driver, potentially creating lasting changes in behavior).

7.2. Avoidance (R-) One particular challenge of enforcement is that it may not actually “teach” the behavior being targeted (Daniels & Daniels, 2004). For example, speed enforcement may not change speeding; rather, it changes how one speeds near enforcement activities. A driver does not learn to drive more slowly; rather, he or she learns to avoid punishment by driving slower when there is a perceived likelihood of getting caught. An officer on the side of the road signals a driver to slow down. A sign warning drivers that an intersection is photo enforced warns them to stop for red lights. Each of these signals is an “occasion” as described by Mattaini (1996), warning drivers to avoid behaviors leading to punishment.

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There is empirical evidence supporting the fact that drivers do not learn the new behavior for the benefits of that behavior to safety but, rather, learn the behavior as an avoidance of punishment. For example, Stradling et al. (2005) reported that 54e56% of drivers surveyed selfreported slowing down in general as a result of speedenforcement cameras. However, an additional 30e32% reported slowing down only when cameras were present but not elsewhere. Motivating avoidance behavior may seem sufficient. After all, if enforcement activities shape driver behavior toward less risk in the face of that enforcement, what is the harm? However, enforcement activities fluctuate in intensity throughout the year as different political priorities take precedence. They fluctuate as city budgets rise and fall (e.g., Camden, New Jersey, is cutting more than 40% of its police force because of budget deficits; Luhby, 2011). Enforcement as a punisher is necessary to control road safety and will result in avoidance behaviors more than new safe habits. However, this should not mean that traffic psychologists and others should be satisfied that this is the only hope for controlling behavior. Other interventions to create new, lasting behavior must be pursued and studieddif anything to supplement enforcement activities that are inconsistent. Nevertheless, “threat avoidance” and R- techniques are likely to remain important and can be structured to have more impact; interested readers should explore the model presented by Fuller (1984) and his enhancements to avoidance and other concepts in Chapter 2 in this handbook with regard to the risk allostasis theory.

7.3. Unequal Outcomes of Punishment Enforcement has trade-offs. Chang, Woo, and Tseng (2006) found that in Taiwan, administratively removing licenses had mixed results. Taiwan, interestingly, has ALR laws allowing the removal of the license for life. The ban for life in Taiwan is applicable to drivers in hit-and-run crashes with injury or death, or in impaired driving crashes causing injury or death. First, this punishment seemed to affect the less wealthy, in that those with funds could pay the fine and drive, avoiding the ban. Those who could not pay had to abide by the removal. However, nearly one in four banned drivers continued to drive the same or nearly the same amount, and only 60% drove less. The ban was effective with only 17% who quit driving. Chang et al. (2006) suggested that drivers continued to drive because there was little risk of getting caught. The economic impact on drivers with such punishment is also a concern. However, there is evidence that employment is not affected by ALR laws (Knoebel & Ross, 1997), which should encourage their consideration and potential use in additional jurisdictions assuming drivers perceive the

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chance of getting caught is significant enough to change their behavior.

7.4. Punishment Versus Rehabilitation Should drivers receive rehabilitation instead of punishment? Typically, such a choice has been most relevant for impaired driving and not as relevant for more common violations such as restraint non-use, speeding, or running red lights. Taxman and Piquero (1998) discuss this choice for impaired driving, noting that rehabilitation attempts to treat the alcohol problem behind the impaired driving act. Using data from Maryland, Taxman and Piquero found recidivism to be 22% less when offenders received education and 17% less when offenders received alcohol treatment. First-time offenders, in particular, may respond the best when not given a “guilty” sentence but, rather, given “probation by judgment,” where the person completes the sentence given by a judge but the conviction is “stayed” if the offender completes the terms (p. 133). Should some type of rehabilitation be offered for other violations besides drunk driving? For example, Virginia offers violators a chance to attend driver-improvement clinics to remove demerit points from licenses that resulted from risky behaviors. Will these drivers be reformed? The majority of evidence suggests not, and in fact it suggests driver education and training has had little or no safety gains and benefits to participating drivers. Wa˚hlberg (2010) provides a review and an interesting discussion of one study that tentatively suggests that driver training for violators may at least make them more honest about their behaviors when surveyed.

8. FUTURE CONSIDERATIONS FOR RESEARCH Enforcement challenges are certainly worth future research study. Each of those areas has a wealth of opportunity for the field. However, there are other important research areas that, thus far, have not received much attention and were not part of the general discussion in this chapter. They are revenue and perception issues surrounding automated enforcement, the behavior of the enforcers, and alternatives to traditional punishment-based enforcement.

8.1. Revenue and Perceptions of Automated Enforcement One public criticism of automated enforcement programs is the perception that the technology is deployed to make money, not to enhance public safety. Evans (2004) believes this perception may hinder automated enforcement that he believes is among the more effective enforcement

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approaches. Blais and Dupont (2005) agree that perceptions are important and argue that if the cameras are perceived to be used for revenue or perceived to be deployed unfairly, they will not be socially accepted. Social acceptance, they found from their reviewed literature, is important for deterrence. Fairness, in general, is an important factor in enforcement effectiveness and public perceptions (Sherman, 1993).

8.2. Behavior of the Enforcers In this discussion, there is one aspect of enforcement that I have not touched upondthe officer and his or her behavior. This person, like others referred to in this handbook, has attitudes, emotions, behaviors, and perceptions that affect his or her driving behaviors. However, in addition, this person is also responsible for deciding what violation is to be enforced. Therefore, it is as important for researchers to care about the safe driving behaviors of officers as it is for them to care about the driving public. This is so for two main reasons. First, many officers come from the communities in which they work. Particularly in smaller jurisdictionsdand I know this from personal experience working with partners in this areadthe officers know people in their community and the community knows them. The officers’ behaviors are salient. Even in larger cities, drivers notice officers’ behavior, and it is not uncommon to see officers violate laws we must follow (e.g., speeding without their emergency lights on and running red lights). One particular behavior of interest that is often incongruent between the officer and the public is safety belt use. In my conversations with police officers, I have learned about their own behaviors regarding belt use, and a significant number of them ride unrestrained, often because there is a belief that belts are unsafe to officers, who need to depart their vehicles quickly to avoid harm and injury from suspects. What message does this send to the public? How fair is it for one group to be exempt from the law while simultaneously enforcing it? One unexplored research question is the role officers play as models, both good and bad models, for safe behaviors. Recall Mattaini’s (1996) interlocking contingency system that placed role models as an important antecedent intervention for promoting behavior. Police officers’ behavior is an important example to the community for what is accepted and expected. However, police officers may also be effective change agents, or those that researchers and practitioners can recruit to promote the program in their communities. Rogers (1995) suggests that such indigenous community members can be very effective in creating change in community behaviors by leading the way with their own actions. Just as important an area for research is a focus on protecting the officers from becoming casualties in traffic crashes. Noh (2011) reported that as of the mid-1990s, law

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enforcement officers are more likely to die in motor vehicle crashes than by other types of events. For the years 1980e2008, he found that in only 39.4% of the cases were officers driving in an emergency when they were killed; more than 60% of fatalities occurred when emergency signals were not in use. Furthermore, 42% of the officers killed in motor vehicle crashes were unrestrained. This is a lower non-use rate than that for non-officer fatalities, but it remains sufficiently high to consider programs to increase officer restraint use for their own well-being. Additional research would also be valuable to determine how many of the 42% unrestrained fatalities could have been saved had the officers been restrained. Given that crashes are a leading cause of officer fatalities today, it seems time to give more effort to understanding officer driver behaviors, occupant protection, and conflicting norms and allowances that may simultaneously increase officer odds of significant injury.

8.3. Alternatives to Punishment-Based Enforcement Recalling previous discussions about punishment not creating lasting safe behavior but, rather, teaching avoidance (i.e., doing the safe behavior to avoid the ticket but not because the safe behavior is desirable to the driver), is there a way for enforcement to use reinforcement instead of punishment? Is there a way for police officers to shape behavior by reinforcing desirable actions, as opposed to noting and punishing illegal ones? Rþ interventions are ideal for creating lasting behavior change (Daniels & Daniels, 2004). There is a line of research from traffic psychology’s past that is relevant to these questions. Although the line has weakened since the 1990s, I believedtheoreticallydthat researchers should consider how alternatives to punishment could be deployed by enforcement officers because they are already monitoring driver behaviors and are still best suited to interact with the public to shape these behaviors. Although not all scholars are concerned with the problems of aversive conditioning for behavior change (Malott, 2001), for now, and to inspire new research, I review the historical attempts in traffic psychology to be more positive with enforcement activities. Elman and Killebrew (1978), in one of the earliest studies, found that opportunities to win prizes effectively increased safety belt use. Geller, Johnson, and Pelton (1982) made similar conclusions using a program giving drivers opportunities to win a game if they were buckled up. Three studies demonstrated that rewards could be as effective as traditional punishment (Hagenzieker, 1991; Johnston, Hendricks, & Fike, 1994; Kalsher, Geller, & Lehman, 1989). A meta-analysis suggested that incentive programs increase safety belt use rates 12.0 percentage

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points in the short term, with a long-term effect of 9.6 percentage points (weighted effect sizes; Hagenzieker, Bijleveld, & Davidse, 1997). Four key studies are important as examples. Rudd and Geller (1985) reported positive effects of university campus police recording license plates of drivers who were buckled up, from which were drawn several winners of gift certificates. The authors found some maintenance of effects above baseline levels, cost-effectiveness support, and transference of program control from researchers to the police and other student groups. Police also responded positively to the program’s demands, including an increase in officer belt use. Kalsher et al. (1989) went further by comparing a 4-week incentive program (opportunities to win a prize) with a 4-week disincentive one (Pþ delivered in the form of warning tickets) to increase safety belt use of drivers on two navy bases. On the base receiving incentives, officers wrote down license plates of drivers who were buckled up so those numbers could be entered into a prize drawing. The base safety staff also delivered the rewards to the winners. On the other base, officers delivered the warning tickets to the drivers. Kalsher et al. found both incentives and disincentives to be equally effective in increasing belt use. A third study (Hagenzieker, 1991) compared a 2-month Pþ enforcement program targeting safety belt non-use on several Netherlands’ military bases with a 2-month Rþ enforcement program on others. The first month of each was for publicity only. Police on the Rþ bases helped identify buckled up drivers to receive opportunities to win prizes. Hagenzieker reported that both Rþ and Pþ enforcement approaches produced similar increases in safety belt use rates. Hagenzieker (1992) followed with a public attitudes survey to assess views on police giving rewards. Interestingly, 60% of respondents on Rþ bases agreed completely that giving rewards was an original way to increase belt use. Forty-two percent completely agreed that the police “show [ed] their right side by rewarding belt use” (p. 202). However, there were some less positive findings. First, Rþ respondents were less likely to completely agree with rewarding belt use than were respondents in the Pþ condition. Second, both groups had high rates of respondents completely agreeing (more than 50%) with the belief that rewarding the behavior may be “exaggerated” when the behavior is “compulsory” (p. 202). Finally, both groups had high rates of completely agreeing with the belief that “police have more important things to do than checking on belt use” (p. 202). The mixed public reactions are important to acknowledge and are worth exploring by additional research. Finally, Ludwig and Geller (1999) used law enforcement as a small component of a larger intervention agent effort. Specifically, during a 6-week period, they used pizza deliverers to promote buckling up by their patrons with

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coupons, radio promotions, and chances to win free pizzas if people displayed reminder cards from pizza boxes in their vehicles. Pizza deliverers and local law enforcement wrote down license plate numbers if they witnessed the cards, and the radio partner would broadcast the numbers for vehicle owners to come by and pick up free pizza vouchers. The authors focused on the deliverers’ belt use, which increased during the intervention and was maintained above baseline levels at a follow-up assessment. A majority of telephone interviewees from the community (58%) also self-reported a greater likelihood of buckling up for participation in the program. In only one of the previous examples did law enforcement officers provide immediate reinforcers to people who were buckled up as part of the program. Hagenzieker (1991) had officers give lottery tickets to buckled up drivers on two Rþ bases. The other studies had officers write down license plate numbers without directly interacting with individuals at the point of their behavior. Immediate consequences, be they reinforcers or punishers, are more effective than delayed consequences (Daniels & Daniels, 2004). The lack of studies with an immediate officer-driver interaction, and the need to address issues from Hagenzieker’s (1992) survey, warrant more in-depth effectiveness trials of this type of enforcement. The traffic safety literature seems to have lost interest in testing reinforcement-based approaches, instead favoring punishment models. Most of the literature evaluating law enforcement and traffic safety focuses on the need for stronger laws and more frequent and consistent distribution of tickets and fines (Pþ). Williams and Wells (2004) argued that stricter laws and higher penalties are important to change the “hard-core non-user group” (p. 179). Furthermore, Shults, Elder, Sleet, Thompson, and Nichols (2004) suggested that safety belt laws allowing primary enforcement (i.e., officers ticketing non-users without any other driving violation necessarily present) can increase belt use above rates already higher due to secondary enforcement laws (i.e., officers needing another present violation before ticketing for non-use). The review by Dinh-Zarr et al. (2001) further solidified Pþ leanings. There has been little optimism or effort to change the traditional use of Pþ in traffic law enforcement. It seems many researchers and enforcement practitioners have not resolved the difficulties and issues raised in the early 1980s about how to use reinforcement effectively, despite the successes reported previously. One of the earliest and still important debates occurred between Hurst (1980) and Warren (1982). Hurst, while acknowledging learning theory’s predictions, concedes that reinforcing safe driving would be difficult if not impossible to implement on a large scale. He notes that risky drivers are probably receiving some peer approval for their risk taking. Police officers, who are not admired, will not likely be able to offer meaningful

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reinforcers to counteract those given by peers. Warren is more optimistic, noting that law enforcement agencies use punishment over reinforcement because of how the target behaviors are defined. If the focus is illegal behavior, then punishment is highly likely as the countermeasure. It may be different if the target behavior is the legal alternative. Furthermore, Warren notes that traffic officers may become more trusted if they first convince the public that their work has real safety benefits. The greater trust may allow distribution of reinforcers for safe driving to compete more effectively against reinforcers for risk taking. Evidence suggests that Warren may be correct; surveys reported by Rudd and Geller (1985) and Hagenzieker (1992) indicate that police officers may get some positive boost by participating in reinforcement activities. In addition, the focus on “deterrence” in the social control literature tends to focus attention on illegal activities and not on the creation and maintenance of the alternate, legal behaviors.

9. CONCLUSIONS Enforcement, in general, has been effective at reducing risk behaviors, injuries, and fatalities on the roadways. It has theoretical support from criminology (deterrence) and psychology (learning) theories. This chapter reviewed the theoretical underpinnings of behavior change and how enforcement fits within larger theories; how enforcement has effectively targeted safety belt non-use, impaired driving, and speeding; and challenges to enforcement’s effectiveness. The chapter also reviewed the debate regarding automated enforcement. Automated enforcement is effective in reducing risk behaviors, but this alone does not convince the public of its fairness or safety potential. In fact, the perception of enforcement in general is important. Enforcement must be consistent enough to encourage people’s perceptions that they will be caught if driving illegally, but it also must be fair enough for those same people to understand and agree they are in violation. The future of enforcement, in my opinion, resides in these areas. In addition, the future resides in work with the enforcers as role models and in enhancing traditional, punishment-based enforcement with alternatives, such as reinforcement-based techniques. Officers are more likely to be harmed in traffic crashes than in other activities, and their own driving behaviors (e.g., safety belt non-use) do not help (Noh, 2011). They enforce laws that at times they violate, and once again, fairness to the public becomes a concern. Also, reinforcement-based alternativesdtheoreticallydmay be better suited to creating a lasting behavior change that punishment-based approaches, which create avoidance behaviors, do not. Progress in reducing risky driving, crashes, injuries, and fatalities will depend on many factors, many of which are reviewed in this handbook. For the conceivable future,

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communities will rely on enforcement activities to control the roadway commons, a shared space that needs structured protection (Porter & Berry, 2004). The goal for us is to do what we can via theory and evaluation results to improve how we design and deploy enforcement programs in our communities.

ACKNOWLEDGMENTS I thank Dr. Kelli England Will of Eastern Virginia Medical School and Dr. Randy Gainey of Old Dominion University for comments on portions of the manuscript.

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Eby, D. W., Vivoda, J. M., & Fordyce, T. A. (2002). The effects of standard enforcement on Michigan safety belt use. Accident Analysis and Prevention, 34, 815e823. Elman, D., & Killebrew, T. J. (1978). Incentives and seat belts: Changing a resistant behavior through extrinsic motivation. Journal of Applied Social Psychology, 8, 72e83. Elvik, R., & Vaa, T. (2004). The handbook of road safety measures. Amsterdam: Elsevier. Erke, A. (2009). Red light for red-light cameras? A meta-analysis of the effects of red-light cameras on crashes. Accident Analysis and Prevention, 41, 897e905. Evans, L. (2004). Traffic safety. Bloomfield Hills, MI: Science Serving Society. Evans, L. (1991). Traffic safety and the driver. New York: Van Nostrand. Fuller, R. (1984). A conceptualization of driving behaviour as threat avoidance. Ergonomics, 27, 1139e1155. Fuller, R., & Farrell, E. (2004). Evidence for the effectiveness of a high enforcement strategy: A case study from the Republic of Ireland. In T. Rothengatter, & R. D. Huguenin (Eds.), Traffic and transport psychology: Theory and application. Amsterdam: Elsevier. Geller, E. S., Johnson, R. P., & Pelton, S. L. (1982). Community-based interventions for encouraging safety belt use. American Journal of Community Psychology, 10, 183e195. Hagenzieker, M. P. (1991). Enforcement or incentives? Promoting safety belt use among military personnel in The Netherlands. Journal of Applied Behavior Analysis, 24, 23e30. Hagenzieker, M. P. (1992). Drivers’ opinions of enforcement and incentive strategies to promote safety belt use. Journal of Safety Research, 23, 199e206. Hagenzieker, M. P., Bijleveld, F. D., & Davidse, R. J. (1997). Effects of incentive programs to stimulate safety belt use: A meta-analysis. Accident Analysis and Prevention, 29, 759e777. Harper, J. G. (1991). Traffic violation detection and deterrence: Implications for automatic policing. Applied Ergonomics, 22, 189e197. Houston, D. J., & Richardson, L. E., Jr. (2006). Safety belt use and the switch to primary enforcement, 1991e2003. Government, Politics, and Law, 96, 1949e1954. Hu, W., McCartt, A. T., & Teoh, E. R. (2011). Effects of red light camera enforcement on fatal crashes in large U.S. cities. Arlington, VA: Insurance Institute for Highway Safety. http://www.iihs.org/research/ topics/pdf/r1151.pdf. Hurst, P. M. (1980). Can anyone reward safe driving? Accident Analysis and Prevention, 12, 217e220. Insurance Institute for Highway Safety. (2011a). Communities using red light and/or speed cameras: February 2011. http://www.iihs.org/ laws/auto_enforce_cities.aspx. Insurance Institute for Highway Safety. (2011b). Safety belt use laws: February 2011. http://www.iihs.org/laws/safetybeltuse.aspx. Jackman, T. (2010, June 7). Fairfax police writing fewer tickets because of problematic computer system. The Washington Post. http://www. washingtonpost.com/wp-dyn/content/article/2010/06/06/ AR2010060603219.html. Johnston, J. J., Hendricks, S. A., & Fike, J. M. (1994). Effectiveness of behavioral safety belt interventions. Accident Analysis and Prevention, 26, 315e323. Kalsher, M. J., Geller, E. S., & Lehman, G. R. (1989). Safety belt promotion on a naval base: A comparison of incentives vs. disincentives. Journal of Safety Research, 20, 103e113.

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Chapter 32

The Intersection of Road Traffic Safety and Public Health* David A. Sleet, Ann M. Dellinger and Rebecca B. Naumann Centers for Disease Control and Prevention, Atlanta, GA, USA

“Today we can prevent, treat, or cure most of the deadliest diseases known to humankinddand yet more than a million people around the world still die every year from traffic injuries.” dSleet, Dinh-Zarr, and Dellinger (2007, p. 41)

1. INTRODUCTION The health of Americans changed significantly during the twentieth century. In 1900, the leading causes of death were respiratory infections and diarrhea (Ward & Warren, 2006). Other infectious diseases, such as smallpox and poliomyelitis, were a constant source of dread. Public health and medical advances during the first half of the twentieth century led to a dramatic decline in the death rate due to infectious diseases. Today, widespread immunization programs have virtually eliminated the threat of diseases such as polio, diphtheria, and measles. As public health and medicine began to control infectious diseases, chronic diseases and injuries became the leading causes of death in the U.S. population. Among the most important of these injuries were those related to motor vehicle travel. This chapter identifies traffic injuries as a public health problem, discusses the history of this problem, describes common traffic psychology and public health perspectives in traffic safety, discusses the application of the public health approach to the traffic safety problem, highlights important successes, and discusses future research needs as public health and traffic psychology fields move toward collaborative efforts to improve road safety. * Portions of this chapter derive from a plenary address at the 4th International Conference on Traffic and Transport Psychology, Washington, DC, September 3, 2008 and are adapted from Sleet, D. A., Dinh-Zarr, T. B., & Dellinger, A. M. “Traffic safety in the context of public health and medicine,” which appeared in Improving Traffic Safety Culture in the United States: The Journey Forward. AAA Foundation for Traffic Safety, Washington, DC, April 2007: pp. 41-58. Used by permission.

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10032-3

2. HISTORY AND BURDEN OF THE TRAFFIC INJURY PROBLEM In contrast to other health problems of the early twentieth century, motor vehicle-related (referred to as “traffic” for the remainder of this chapter) injuries and deaths resulted from the development and rapid adoption of a new technologydthe motor vehicle. In 1900, motor vehicle travel was a novelty, and the risks to health and safety were largely overlooked. The motor vehicle represented a major improvement over other modes of personal travel at that time (e.g., the horse and buggy), and subsequent improvements in manufacturing made cars more affordable and availabledbenefiting commerce, communications, and people’s ability to move from one place to another more easily. In 1900, there were an estimated 8,000 registered automobiles in the United States (Ritter, 1994). By 1950, the number of automobiles had grown to 50 million (Federal Highway Administration (FHWA), 2000). By 2008, there were more than 255 million registered vehicles, more than 208 million licensed drivers, and an untold number of cyclists, pedestrians, and vehicle occupants exposed to traffic (FHWA, 2010). This rapid “motorization” of America brought with it increased exposure to potential risks for crashes and injuries to drivers, passengers, pedestrians, and cyclists (Global Traffic Safety Trust, 1998). As the number of vehicles and drivers increased, so did deaths and injuries on the roaddfrom 1.0 traffic deaths per 100,000 population in 1900 to a peak of 31.0 in 1937 (National Safety Council (NSC), 2002). Increased mobility on the road brought with it an increase in risk and a decline in safety: This is the paradox of motorization in America. Traffic injuries remain an enormous societal problem (Institute of Medicine (IOM), 1999). In the past 100 years, more than 2.8 million people have died and nearly 100 million people have been injured on U.S. roads and highways (U.S. Department of Health and Human Services 457

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(DHHS), 1992). Currently, traffic-related injuries are the leading cause of death for children, adolescents, and young adults, and they are a major cause of death for all other ages. In 2009, motor vehicle crashes led to 33,808 deaths and 2,217,000 nonfatal injuries associated with more than 5,505,000 police-reported crashes (National Highway Traffic Safety Administration (NHTSA), 2010). Medical costs and productivity losses associated with traffic injuries cost the United States more than $99 billion in 2005 alone (Naumann, Dellinger, Zaloshnja, Lawrence, & Miller, 2010), and it is estimated that these costs are equivalent to approximately $500 for every U.S. licensed driver per year. Research has also found that costs associated with crashes are equivalent to as much as 2.3% of the U.S. gross domestic product (Blincoe et al., 2002; Naumann et al., 2010). Traffic crash injuries on and off the job cost employers in the United States almost $60 billion (NHTSA, 2006).

3. A PUBLIC HEALTH PERSPECTIVE Public health is the science and practice of protecting and improving the health of populations. Public health uses education, public policy, environmental protection, product safety, and regulation to achieve population health goals (Association of Schools of Public Health, 2011). From the standpoint of preventable morbidity and mortality, public health has much to offer traffic safety. The field of public health has resources, skilled workers, and close connections to the community. Most people recognize public health as an essential government function, and it carries with it certain responsibilities to protect the public from unreasonable threats to health (Shaw & Ogolla, 2006). These features can be marshaled to justify efforts to reduce traffic injury, but only if there is a recognition that, like diseases, traffic injuries are predictable and therefore preventable. Although traffic crashes clearly have a public health impact on both the individual and society, they are often viewed as a transportation issue rather than a public health issue. Approximately 40 years ago, Sue Baker (1972) recognized this when she wrote, Injuries are a major and urgent public health problem, closely related to other health problems, but long ignored by most public health professionals. The basic etiologic approaches that have been successful in controlling disease are closely analogous to approaches for controlling injuries. (p. 1002)

As the World Health Organization (WHO) attests, traffic safety should be viewed as a shared responsibility and not the exclusive purview of any single sector, discipline, or agency (Peden et al., 2004). Traffic crashes affect not only transportation systems but also economic systems, health systems, jobs, families, and civil society.

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In the poorest of countries, a family breadwinner killed as a pedestrian, cyclist, or vehicle occupant can often mean the loss of family income, result in children who are instantly orphaned, and can materially affect the economy of low- and middle-income countries (Peden et al., 2004). The public health response to traffic injuries has come from a number of quartersdthe medical profession, public health organizations, consumer advocates, road authorities, communications specialists, traffic psychologists, and the federal health sector. The Centers for Disease Control and Prevention (CDC), part of the U.S. Public Health Service (PHS) within the DHHS, has taken the lead in this area within the federal health sector. Because of the enormous demands that traffic injuries place on the health care system, and the significant impact prevention programs can have on the problem, PHS became involved in this area early in the twentieth century and has played a critical role in organizing the public health response ever since. Through the use of descriptive epidemiology, risk factor identification, intervention development and evaluation, and the widespread implementation and dissemination of effective countermeasures, public health has advanced our approaches to prevention. Public health has also contributed to state-based prevention programs, public education and training, improved trauma care systems, and advances in rehabilitation medicine (von Holst, Nygren, & Andersson, 1997).

3.1. Addressing the Traffic Safety Problem The adverse consequences of increased motorization in the first few decades of the twentieth century led President Herbert Hoover to convene the first National Conference on Street and Highway Safety in 1924. This was the first in a series of presidential initiatives to create a uniform set of traffic laws designed to prevent collisions and protect the public from unnecessary death and injury (American Public Health Association (APHA), 1961). It was also during this period that the National Academies of Sciences, National Research Council, Division of Anthropology and Psychology established a Committee on Psychology of Highways to study the psychological principles of automobile driving (1930) and establish tests for drivers (1934) (National Academy of Sciences, 2011). Between 1924 and 1934, physicians and health workers were called in to participate in a national program, and formal committees were developed in all areas of traffic safety to work on the problem. However, traffic deaths continued to climb as the number of drivers and vehicles exposed to risk increased faster than the countermeasures designed to keep them safe. In 1934,

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a total of 36,101 traffic deaths were reported, a rate of 28.6 per 100,000 population (NSC, 2002). These numbers prompted President Franklin D. Roosevelt to enlist the cooperation of the governors in each of the 48 states to reduce the traffic injury problem. In a letter to each governor in January 1935, he began by saying (Roosevelt, 1938), I am gravely concerned with the increasing number of deaths and injuries occurring in automobile accidents. Preliminary figures indicate that the total of these losses during the year 1934 greatly exceeded that of any previous year. We should, as a people, be able to solve this problem which so vitally affects the lives and happiness of our citizens.. The responsibility for action rests with the States. There is need for legislation and for the organization of proper agencies of administration and enforcement. There is need also for leadership in education of the public in the safe use of the motor vehicle, which has become an indispensable agency of transportation. (pp. 62e63)

This pronouncement and plea to become involved in traffic safety and subsequent action by state governors was the genesis of the present-day Governor’s Highway Safety Offices, which exist in every state to assist efforts to improve traffic safety. On April 13, 1954, President Dwight D. Eisenhower established a Committee for Traffic Safety on an informal basis. On January 13, 1960, he provided a formal status to the Committee (under the leadership of William Randolph Hearst, Jr.) by signing Executive Order 10858 “to advance the cause of street and highway safety” (Weingroff, 2003, p. 120). The executive order described its purpose: The Committee, on behalf of the President, shall promote State and community application of the Action Program of traffic safety measures established by the President’s Highway Safety Conference in 1946, and revised in 1949, and shall further revise and perfect that Action Program in accordance with the findings of further research and experience. It shall also develop effective citizen organization in the States and communities in support of public officials with Action Program responsibilities.

The creation of the NHTSA was spawned in response to rising traffic death rates in the early 1960s and the climate of social reform. President Lyndon B. Johnson signed both the National Traffic and Motor Vehicle Safety Act and the Highway Safety Act in 1966. These acts paved the way for an intensified effort by the government to set standards and regulate vehicles and highways to improve safety for drivers, passengers, pedestrians, and cyclists (Transportation Research Board (TRB), 1990). This legislation led to the creation of the National Highway Safety Bureau (NHSB), which in 1970 became the NHTSA. Beginning with 1968 models, these two acts gave the NHSB/NHTSA the authority to set safety standards for highways and new cars.

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3.2. Applying the Public Health Model to Prevention The public health model for prevention has been applied to a wide variety of infectious and chronic diseases with remarkable success. Although many scientific disciplines, such as engineering, environmental health, and emergency medicine, have advanced our understanding of traffic injuries and their causes and consequences, public health brought new tools, methods, applications, and systems that had been missing from the traffic safety field. By definition, public health is not about individual patientsdit is about populations. Public health focuses on the continuous monitoring of health, on identifying, preventing, and managing diseases and conditions affecting health, with the aim of maximizing benefits for the entire population. This is what makes public health’s contribution to society unique. To do this, public health (by necessity) must draw from many disciplines, including epidemiology, health services, health promotion, psychology, human factors, health education, economics, and medical sociology. One of the unique strengths of public health is its connectedness to the community, its ability to approach health problems through a coordinated system of care. The population focus in public health lends experience in the development of tools and methods to identify, prevent, and treat illness, disease, and injury. These characteristics are embedded in the public health culture and can be successfully applied (or adapted) to the “disease” of traffic injury and to the promotion of safety. Public health can effectively use these tools and its national infrastructure to identify, track, and monitor traffic injuries and deaths and to design short- and long-term solutions to help counter the rising exposure to traffic injury. A systematic public health approach to traffic injury prevention began with NHSB’s first director, William Haddon (IOM, 1985). Haddon, a public health physician and epidemiologist, applied a scientific approach to the prevention of traffic injuries rooted in public health methods (Haddon, 1968). Haddon’s conception built upon the work of Dr. John E. Gordon, who suggested that injuries behaved like classic infectious diseases and were characterized by epidemic episodes, seasonal variation, and longterm trends (Gordon, 1949). Haddon further described the factors contributing to traffic injury as occurring during three phases: the precrash phase, crash phase, and postcrash phase (Haddon, 1968). NHTSA’s activities today continue to be influenced by Haddon’s work, with an emphasis on a better understanding of the driver, the vehicle, and the roadway environment. In 1959, James Gibson, an experimental psychologist, applied traditional epidemiology methods to the study of injuries. He concluded that injuries to a living organism can be produced only by some form of energy exchange

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FIGURE 32.1 Applying the epidemiologic triangle to reducing smoking and traffic-related morbidity and mortality. Source: Reproduced from Sleet and Gielen (1998).

(Gibson, 1961). To illustrate this concept, injuries, like smoking-related diseases, are the product of forces from at least three sources: the host (e.g., the smoker and the drinking driver), the environment (e.g., environments that promote smoking and hazardous roads or weather), and the agent (e.g., carcinogenic agents in tobacco and kinetic energy transferred to the host when a speeding car crashes) (Figure 32.1). Intervening on the host (changing behaviors to reduce risk), the environment (reducing exposure to hazards), and the agent (reducing noxious chemicals in tobacco or reducing energy transfer in a crash) can singly, or in combination, reduce the likelihood of smoking-related and traffic-related morbidity and mortality. To address host factors in reducing traffic injuries, speed limits were introduced, drunk driving laws were passed, and drivers and pedestrians were educated and trained in safer behaviors. To improve the environment, multiple strategies were used to improve roads, including better delineation of curves, the addition of edge and center-line stripes and reflectors, breakaway signs and utility poles, and highway illumination; the use of barriers to separate traffic lanes, guardrails, and grooved pavement to increase tire friction in bad weather; the practice of channeling left-turn traffic into separate lanes; the addition of rumble strips; and the availability of crash cushions on exit ramps (DHHS, 1992; Rice et al., 1989; Waller, 2001). To control the agent (kinetic energy released in a crash), manufacturers began building vehicles with improved safety features, including headrests, energy-absorbing steering wheels, rollover protection, dual brakes, shatter-resistant windshields, and safety belts (Rice et al., 1989; TRB, 1990). Vehicle safety regulations were introduced and promulgated by the government and adopted by manufacturers to meet vehicle performance and human injury tolerance standards in the event of a crash (Evans, 1991; Shinar, 1978). Enactment and enforcement of regulations controlling vehicles, roads, and human behavior, reinforced by public education, led to stronger policies that

saved thousands of lives on the road (Dellinger, Sleet, & Jones, 2007).

3.3. Leaders in Prevention 3.3.1. The Role of the U.S. Department of Transportation The recognition that traffic injuries could be prevented stimulated research and programs in federal and state governments, academic institutions, community-based organizations, and industry. From the transportation side, the NHTSA and the FHWA within the U.S. Department of Transportation have provided national leadership for traffic and highway safety efforts related to vehicles, driver behavior, and road environments since the 1960sdactivities that continue to benefit safety today (IOM, 1999). Among roadway improvements, the FHWA was charged with developing national standards for all traffic-control devices on any street, highway, or bicycle trail open to public travel (FHWA, 2003). Had it not been for these efforts at the federal level to design and implement actions supporting safer motor vehicle travel, the U.S. traffic injury and death rates would surely be higher than they are today.

3.3.2. The Role of the Centers for Disease Control and Prevention The growth of traffic safety at the federal level received a boost when, in 1986, as a result of the National Academy of Sciences report titled Injury in America (IOM, 1985), Congress authorized funding to establish a national injury prevention research program at the CDC. The CDC brought a public health framework and epidemiologic perspective to traffic injury prevention that included surveillance, risk factor research, intervention development, and dissemination. In this four-step model, the first step is to document

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FIGURE 32.2 CDC and the public health approach to prevention. Source: Reproduced from Centers for Disease Control and Prevention (2001).

the magnitude of the problem using surveillance (the systematic collection, analysis, and interpretation of traffic injury data), then to identify risk and protective factors for crashes and injuries, followed by developing and testing interventions to reduce the risk factors, and finally, implementing and disseminating programs found to be effective (Figure 32.2). This public health model was quickly applied to traffic safety programs, with an emphasis on moving from the identification of the problem using surveillance to the dissemination of programs and policies known to be effective in prevention. The CDC also funded state and local health departments to conduct traffic injury prevention programs (Sleet, Bonzo, & Branche, 1998). In addition, the CDC funded “Centers of Excellence” to conduct injury control research, with the initial requirement that half of the money be spent on research related to traffic injury prevention and control. Today, many of these centers continue to conduct important traffic-related research.

3.3.3. State and Local Public Health Departments State health departments, partly due to their role in carrying out disease prevention and health promotion activities, have played an important role in traffic safety. Their role, however, has trailed that of state highway safety offices, which are funded through NHTSA. Because of the role of health in protecting and promoting the health of state and local populations, health departments are key components in any effort to reduce traffic injuries. Health departments have the statutory responsibility for public health, provide community health services, deliver programs to underserved populations, and are typically experienced in working with a broad range of community groups and agencies (Sleet, 1990). Preventing injuries related to traffic crashes (e.g., alcohol-impaired driving, safety belt use, and

pedestrian and bicycle safety) is seen as an increasing responsibility of the health sector.

3.3.4. Collaborators in Public Health and Medicine In many respects, the collaboration between traffic safety and public health on traffic injury prevention has resulted from the joint recognition that they both share a common goal. Although the language and systems for addressing the problem may differ, they both bring important and unique perspectives. Local coalitions, the private sector, voluntary organizations, and nonprofit groups have also been critical in building momentum and action for traffic safety. State chapters of Safe Kids and the American Academy of Pediatrics have been strong supporters of traffic safety at the state and local level. Advocacy groups such as Mothers Against Drunk Driving, Physicians for Automotive Safety, Advocates for Auto and Highway Safety, and the Insurance Institute for Highway Safety have taken on the issues of safer vehicles, roads, and road user behaviors from a nongovernmental perspective. Collaborations in local public health settings have played a critical role in stimulating public debate, encouraging legislation and public policy, supporting victim rights, and supporting research outside the government. These efforts, along with those of federal and public health agencies and medical groups, have created a sea change in public interest and political action toward traffic safety. Collaboration within the medical professionsd particularly among large membership associationsdhas contributed to the development of traffic injury prevention in part because their collective views represent thousands of their members. As early as 1950, both the American Medical Association and the American College of Surgeons were recommending that automobile manufacturers design their cars for passenger safety and equip them with safety belts. The APHA in collaboration with the U.S. Public Health

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Service Division of Accident Prevention, published Accident Prevention: The Role of Physicians and Public Health Workers (APHA, 1961). This was during a time when the National Safety Council, the U.S. President’s Committee for Traffic Safety, and the PHS were all collaborating to reduce the unacceptable rise in traffic injury. Dr. Paul V. Joliet, Chief of the Accident Prevention Program of the PHS, stressed with his colleagues that “there are no simple easy solutions [to the traffic injury problem]” (Weingroff, 2003, p. 67). In 1971, H. J. Roberts, Director of the Mannow Research Laboratory in West Palm Beach, Florida, published a 1016-page avant-garde medical text, The Causes, Ecology and Prevention of Traffic Accidents, with collaborators from the American Association for Automotive Medicine, Physicians for Automotive Safety, and the International Association for Accident and Traffic Medicine (Roberts, 1971). Professional associations such as the American College of Preventive Medicine, the American Trauma Society, the International Union for Health Promotion and Education, the Society for Public Health Education, and the American Public Health Association have all adopted resolutions dedicating their leadership and professional members to promote highway and vehicle safety as a health issue and to integrate traffic safety into their prevention efforts.

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These collaborative efforts have, over time, fostered efforts to build a public health constituency for traffic safety, reinforcing the perception that traffic safety and traffic injury prevention are (and should be) priority health goals in a civil society. Changes in traffic safety laws, public perceptions of vehicle safety, and enhanced enforcement have led to a cultural intolerance of reckless driving, drinking and driving, and non-use of child safety seats. This intolerance has contributed to new social norms favoring safety. Similar changes have been observed overseas, for example, with public acceptance of efforts to reduce drink driving through the introduction of random breath testing (Job, Prabhakar, & Lee, 1997).

4. DOCUMENTING PROGRESS Public health’s contribution to injury prevention has been multidisciplinary and directed toward collective action (Fisher, 1988). Public health functions that have served the goals of traffic injury prevention include assessment (monitoring health behaviors and identifying community health hazards), assurance (enforcing laws and regulations that protect people from injuries and linking people to needed prevention and trauma care), and healthy policy (developing policies and plans that support healthy

FIGURE 32.3 Traffic death rates per 100,000 population and per 100 million vehicle miles traveled, 1966e2008, United States. Source: National Safety Council (2010).

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environments and behaviors conducive to traffic injury prevention) (IOM, 1988). Although much of what public health has accomplished is in prevention, important advances have also been made in improving emergency medical services and developing, implementing, and evaluating comprehensive trauma care systems. These components of “tertiary” prevention are also characteristics of traffic injury preventiondthat is, when the primary or secondary lines of prevention fail, tertiary prevention can minimize the consequences of the injury. Since 1966, these joint efforts of government (in transportation and health) and private agencies and organizations to reduce traffic deaths have resulted in a 43% decrease in the rate of deaths per 100,000 population and a 72% decrease in deaths per vehicle mile traveled (VMT) (Figure 32.3) (NSC, 2010). These reductions translate into more than 328,000 lives saved and countless injuries averted during the past 30 years (Kahane, 2004). These gains have resulted from a variety of causes, including changes in driver behavior, vehicle design, and road design that have improved both individual mobility and population safety. The reduction in traffic death rates in the United States was accomplished during a period of increasing vehicle ownership and VMT and is strong evidence that prevention efforts have overshadowed the increased risks that come with increasing VMT. Had this not been the case,

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we likely would have seen many more thousands of deaths as VMT increased. Reinforcing the success of traffic injury prevention efforts was CDC’s inclusion of motor vehicle safety as one of the 10 significant public health achievements of the twentieth century (CDC, 2001; Dellinger, Sleet, Shults, & Rinehart, 2006). Nonetheless, much more remains to be done. For example, amid decreases in death rates among other road user types (e.g., occupants and pedalcyclists) between 1999 and 2007, motorcyclist death rates increased 100% (Figure 32.4). Future prevention work will require collaboration across traffic safety, public health, and traffic psychology.

5. USING PUBLIC HEALTH AND TRAFFIC PSYCHOLOGY TO IMPROVE TRAFFIC SAFETY Human behavior remains an important factor in traffic injury prevention (Evans, 2004; Lonero, Clinton, & Sleet, 2006). A long line of human factors research and engineering has demonstrated an inextricable link between human behavior, the environment, and technology to enhance human health and safety (Fuller, 2002; Summala, 2005). However, the uses of this information in public health are under-recognized, underappreciated, and

FIGURE 32.4 Percentage change in motor vehicle-related death rates per 100,000 populationdUnited States, 1999e2007

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underfunded. Whereas traffic psychologists have been major contributors to our understanding of the behavioral and social causes of traffic crashes and injuries (Evans, 2004; Frank, 1997), health psychologists have not. Leading journals in health psychology, such as Health Psychology, the Journal of Health Psychology, the British Journal of Health Psychology, and Applied Psychology: Health and Well-Being, rarely publish articles related to traffic injury prevention and behavior. One of the leading textbooks in health psychology for more than 20 years (Brannon & Feist, 2009) allots 26 pages to discussing the behavioral dimensions of exercise, but not even a page appears on the behavioral dimensions of traffic injurydthe leading cause of death for Americans in the first four decades of life. The cover of a popular British health psychology text (Morrison & Bennett, 2009) shows a rock climber wedged between two boulders hundreds of feet above the ground without a harness, yet the book does not even include “unintentional injury” or “accidents” in its index of topics covered. Road safety needs transportation and traffic psychology, health psychology, environmental psychology, and public health. Improving road safety will require a shift in how these professions think about traffic hazards, personal and public health behavior, risk control, and the value of prevention. Both behavioral and environmental changes are necessary to reduce traffic injuries, but this will take time, professional collaborations, and resources from many fronts. The history of public health reveals that successful changes in behavior to improve health are possible, and that health risks can be reduced through the actions of individuals and communities. For example, smoking was once considered a harmless habit and part of a healthy, active lifestyle. Tobacco was heavily advertised and frequently endorsed by physicians and athletes. With mounting scientific evidence on the hazards of tobacco use, the public began viewing smoking negatively, tobacco control became a major health goal, and smoking declined dramatically. Similar shifts are required to affect a change in traffic injuries. Public health, together with traffic psychology, can contribute to this shift by l

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incorporating traffic safety culture into health education activities for the young so that children associate safety with all aspects of life; using behavioral data to identify the most significant risks to traffic injury; conducting research on behavioral determinants of traffic crashes and their associated psychological sequelae; using public health tools to assist the transportation sector in conducting safety audits to identify hazardous and unsafe road environments; adding road safety to health promotion and disease prevention activities;

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reducing health disparities by ensuring equal access to community preventive services such as child safety seats, bicycle helmets, and neighborhood sidewalks among poor and underserved populations; incorporating safety and mobility into healthy aging by focusing on the mobility needs of older adults, especially as they relinquish their driving activities; applying modern evaluation techniques to measure the impact of road safety programs and interventions; measuring health care costs and public health consequences of traffic injuries; identifying cost savings by applying known and effective interventions; assisting states and communities with local injury data collection and traffic injury surveillance systems; strengthening prehospital and hospital care for trauma victims by supporting comprehensive trauma care systems nationwide; applying behavioral theory to design interventions that influence policy makers to protect road users from traffic injury; and disseminating critical behavioral research in traffic safety to public health practitioners and in key public health journals and books.

Success will require participation from other sectors in society, such as education, transportation, business, economics, justice, human factors, and social services. Using a multidisciplinary perspective, traffic safety and health can move into urban planning, the built environment, social ecology, road administration, injury surveillance, and social marketing as necessary extensions of their work to preserve health and safety.

6. CHALLENGES AND OPPORTUNITIES FOR THE FUTURE Adding to the significant milestones contributing to reductions in traffic injuries are the directions set by the DHHS in its policy framework Healthy People Objectives for the Nation (DHHS, 2010). Healthy People 2020 is a set of national goals developed by the DHHS. These goals aim to improve the country’s health by reducing preventable health threats, including traffic injuries. Public health professionals at local, state, and national levels work to meet and exceed these goals through public health interventions and policy changes. Healthy People provides science-based, 10-year national objectives for promoting health and preventing disease. Since 1979, Healthy People has set and monitored national health objectives to meet a broad range of health needs, encouraged collaborations across sectors, guided individuals toward making informed health decisions, and measured the impact of the nation’s prevention activity (U.S. Department of Health,

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Education and Welfare, 1979). Currently, Healthy People 2020 is leading the way to achieve increased quality and years of healthy life and the elimination of health disparities. Since 1979, motor vehicle-related trauma has been among the Healthy People objectives targeted for reduction. Specific health objectives for the nation to reduce the traffic injury burden were set for 1990, 2010, and now 2020. These objectives were reviewed every 5 years and expanded with a new set of goals and targets for the years 2010 and 2020. Healthy People 2020 includes a number of specific objectives related to decreasing motor vehicle- and pedestrian-related deaths and injuries; increasing the use of safety belts, child safety seats, and motorcycle and bicycle helmets; and implementing graduated driver licensing laws and bicycle helmet legislation (DHHS, 2010) (Table 32.1). Other objectives not listed here, for example, specify goals related to reducing nonfatal head trauma and spinal cord injury hospitalizations and increasing the use of alternative modes of transportation. The CDC is the lead public health agency for establishing and tracking objectives related to injuries. The NHTSA has been an important partner in these efforts in setting targets and monitoring data for the motor vehicle injury problem since the inception of Healthy People in 1979.

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6.1. Public Health and Traffic Psychology: Areas for Research Collaboration Despite substantial gains in traffic injury prevention during the past 100 years, crashes and the injuries they inflict remain a major public health problem in the twenty-first century. The possibilities for integrated research and cooperative programs of surveillance, intervention, and evaluation in the fields of transportation/traffic psychology and public health are almost limitless. For the driving public, motor vehicle travel will contribute to a number of cross-cutting health problems in the future, from personal safety to concerns regarding obesity and environmental pollution. These problems will only increase with time as a result of increased travel, population growth, an aging society, and our growing reliance on cars for daily living. Global problems associated with traffic injuries will continue to grow, especially in low- and middle-income countries. The 1.3 million deaths and 20e50 million serious nonfatal injuries globally cannot be sustained by societies for much longer, and it is predicted that these losses will only mount (WHO, 2009). Conflict has always existed between the goals of mobility and the goals of safety, and this balance must be continually re-evaluated. For example, although a national

TABLE 32.1 Examples of U.S. Healthy People 2020 Motor Vehicle-Related Objectives Objective No.

Objective

Baseline

2020 Target

IVP-13.1 and IVP-13.2

Reduce motor vehicle crash-related deaths

13.8 deaths per 100,000 population in 2007. 1.3 deaths per 100 million vehicle miles traveled in 2008.

12.4 deaths per 100,000 population 2 deaths per 100 million vehicle miles traveled

IVP-14

Reduce nonfatal motor vehicle crash-related injuries

771.5 nonfatal injuries per 100,000 population in 2008.

694.4 nonfatal injuries per 100,000 population

IVP-15

Increase use of safety belts

84.0% of motor vehicle drivers and right front-seat passengers used safety belts in 2009.

92.4%

IVP-17

Increase the number of states and the District of Columbia with “good” graduated driver licensing laws

35 states (including District of Columbia) had “good” graduated driver licensing laws in 2009.

All states and the District of Columbia

IVP-18

Reduce pedestrian deaths on public roads

1.4 pedestrian deaths per 100,000 population occurred on public roads in 2008.

1.3 deaths per 100,000 population

IVP-20

Reduce pedalcyclist deaths on public roads

0.24 pedalcyclist deaths per 100,000 population occurred on public roads in 2008.

0.22 deaths per 100,000 population

IVP-22

Increase the proportion of motorcycle operators and passengers using helmets

67.0% of all motorcycle operators and passengers used helmets in 2009.

73.7%

Source: U.S. Department of Health and Human Services (2010).

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55-mile-per-hour speed limit was instituted in the United States to conserve fuel, it also resulted in fewer crashes and fewer crash deaths. When fuel availability increased, so did speeds and road deaths, illustrating the trade-off between one aspect of mobility (speed) and traffic safety; the public was not willing to maintain restricted mobility even in light of substantial safety benefits (Dellinger, Sleet, & Jones, 2007). In addition, new conflicts are emerging between “automobility” and the goals of traffic safety and public health. For example, parents are encouraged to increase physical activity for children by promoting walking, but because of traffic safety or security concerns, parents may be reluctant to allow their children to walk near traffic for even short distances (Dellinger & Staunton, 2002). Adults may struggle with the choice of walking or cycling as opposed to driving to work. Fuel-efficient cars may be better for the environment and contribute less to asthma, which accomplishes one public health goal, but driving a fuel-efficient car does not reduce the risk of cardiovascular disease or promote health and fitness, another public health goal (Kelter, 2006). Currently, 82% of American adults own and use a mobile communication device, and an estimated 47% of adults and 34% of teenagers resort to texting while driving (Madden, 2010). In addition, 75% of adults and 52% of teens admit to talking on the phone while driving (Madden, 2010). These additional attentional demands will present unique challenges for the future, and traffic psychologists can play a key role in research leading to new solutions for these and other emerging behavioral issues.

6.1.1. New Technology New vehicle and telematics technology can increase safety but can also introduce new risks for drivers that pose special challenges to traffic psychology and public health. Digital maps, communication links, image processors, and vehicle positioning and tracking devices place new behavioral and attentional demands on drivers. Driving distractions such as cell phone use, texting while driving, in-vehicle entertainment, and vehicle-equipped Internet pose new challenges that could easily undermine and counterbalance further gains in traffic safety. Traffic volume and congestion can be expected to rise in the future, along with changes in the vehicle fleet (e.g., small vs. large vehicles) as petroleum products become more scarce and more expensive. Computerized in-vehicle early warning systems to detect the imminence of a crash present new safety challenges that will require innovative solutions. Safety improvements gained from making cars safer, reducing drunk driving, reducing highway speeds, and increasing safety belt use may be offset by new hazards related to driver behavior, including distraction, fatigue, or sensory overload. The cry for greater mobility will have to be

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balanced with the need for greater safety if the culture of technology begins to overtake the culture of safety. According to Noy (1997), too little research is directed toward advancing the science of humanetechnology interaction, and too little is published that is useful to system designers. The development of intelligent driver interfaces may improve safety even more than the technologies that underlie them. Parkes and Franzen (1993) demonstrated that a purely technology-driven approach to traffic safety is a “recipe for disaster.” Integrated, coordinated, multidisciplinary initiatives in complex system design are needed to deliver system efficiency, acceptability, and safety. Recent interpretations of these challenges have echoed the need for a systems approach (Porter, Bliss, & Sleet, 2010; Shinar, 2007).

6.1.2. Special Populations Special populations will continue to be a focus of research. Interventions to reduce alcoholism and problem drinking at the population level should continue, as should the targeting of “binge” drinking and hard-core drinking drivers. These efforts will benefit traffic safety. Research on improving distribution and adoption of strategies for increasing correct and consistent use of child safety seats is needed because booster seat use and appropriate use of child safety seats remains low. Teen driving risks will also continue to be a problem as cohorts of new inexperienced drivers are added to the driving mix. Research on the effectiveness of graduated drivers licensing programs will remain important, as will as improvements in driver education and training. Because neuroscience is revealing new information about the adolescent brain, the cognitive aspects of adolescent driving will play an increasingly important role in research. As the population ages, older driver crash and injury prevention will become a higher priority. The number of adults older than age 64 years is expected to more than double between 2010 and 2040 (U.S. Census Bureau, 2008). Because people are living longer, older persons will operate a vehicle for a longer period of time, increasing their exposure to crashes and injuries. Assisting older adults in successfully balancing safety and mobility will be an important future challenge and will involve health, social services agencies, and traffic safety. Changes to consider involve the vehicle (i.e., safety belts that are easier to reach, visual displays that are easier to read, and pedals that are easier to find and depress), the roadway (i.e., signs that are easier to read and junctions that are easier to navigate), and driver behavior (i.e., improved functional and cognitive screenings and assessments to identify those who should regulate or stop driving). The availability of practical alternative transportation options for older adults should be a high priority as people’s mobility needs expand with increased longevity.

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6.1.3. Immigration

6.1.5. Evidence-Based Interventions

Immigration will bring new challenges to traffic safety because transplanted drivers and pedestrians carry with them their own cultural patterns of walking and driving, which may be incompatible with traffic safety systems in the United States. The fields of road safety, public health, and traffic psychology will need to work together to determine how shifts in demographics and changes in the economy will impact road safety and the health of future generations to ensure that diverse populations stay safe while remaining mobile. Health and traffic safety literacy programs can contribute to improvements in traffic safety behaviors of immigrants and their families through community education programs (AAA Foundation, 2007a, 2007b; IOM, 2009). With the many challenges facing road safety, it will be vital to take a comprehensive approach and determine how to improve the safety of the motoring public as a whole rather than simply addressing each behavioral risk as it arises. There remains a critical need for support in training new researchers and practitioners in traffic psychology and to broadly address the traffic safety problem as part of public health.

Because traffic crashes involve multiple interrelated causes, interventions will have to be comprehensive and tailored (Dellinger & Sleet, 2010; Dellinger et al., 2006). Prevention efforts in most areas of public health rely on a combination of multiple interventions. Ecological approaches (Allegrante, Marks, & Hanson, 2006) provide a useful framework for accomplishing this because they employ a combination of educational, behavioral, environmental, and policy approaches (Lonero et al., 2006; Sleet, 1984; Sleet, Wagenaar, & Waller, 1989). Interventions that employ ecological approaches often include economic interventions, organizational interventions, policy interventions, and health education interventions (e.g., the use of mass media and school and community education programs) (Allegrante, Hanson, Marks, & Sleet, 2010; Howat, Sleet, Elder, & Maycock, 2004). Integrating health promotion approaches (such as those successfully used for tobacco control and chronic disease prevention) into traffic injury prevention programs can capitalize on cross-disciplinary approaches that can result in parallel successes (Gielen, Sleet, & DiClemente, 2006). Many of the easy fixes in traffic safety have already been made, with changes in vehicle design and road infrastructure. However, many more still need to be discovered. What remains are the more difficult problems that relate to human behavior and the tendency for drivers, pedestrians, and cyclists to succumb to “adaptational failure” as demands on the road outstrip their capacity to perform safely (Porter et al., 2010).

6.1.4. Surveillance Data systems are important for traffic safety. Traffic safety data can be used by a variety of stakeholdersdthe police, transportation departments, health departments, and insurance companies. Reliable data are important in persuading political leaders that traffic injuries are a priority issue. They can be used by traffic and transportation psychologists when talking to the media and to make the public aware of changes in behavior that will improve safety on the road. Comprehensive, integrated surveillance systems will be needed to provide policy makers, planners, and public health officials at the state and local level with timely data on crashes, injuries, and deaths. Crash data are key to identifying risks, developing intervention strategies, and evaluating the impact of programs. This then enables program directors to set realistic priorities and implement prevention strategies that have shown evidence of effectiveness (Espitia-Hardeman & Paulozzi, 2005; Holder et al., 2001; Thacker, Stroup, Parrish, & Anderson, 1996). Greater detail is needed about human factors that contribute to traffic crashes and injuries. Crash investigation teams can assist surveillance efforts by collecting routine human factor and behavioral data. Emerging threats to the safety of drivers, passengers, motorcyclists, and pedestrians related to texting while driving or walking, and driver/passenger/pedestrian distractions, need more robust surveillance.

6.1.6. Public Attitudes and Perceptions One of the remaining obstacles is the public’s misconception that injuries are accidents that occur by chance. It has been difficult to summon popular sentiment for an unrecognized problem such as traffic injury for which there is no single cause or cure and that most people consider outside their own controldthe result of an “accident.” For many, road trauma is simply the price we pay for mobility. Although some progress has been made in changing the perception of injuries as predictable, preventable events, more must be done. Public health has been somewhat successful in framing traffic injuries in the context of other preventable causes of death and disease. Members of the medical profession have been quick to recognize their role as advocates for traffic safety with patients and policy makers and the importance of emphasizing lifestyle changes that include safety behaviors (Sleet, Ballesteros, & Baldwin, 2010). Traffic psychology has been successful in emphasizing the role that human factors play in traffic crashes and the need to address prevention from a safe systems approach (Wegman

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& Aart, 2006)dan approach that implies that in order to prevent injuries, the environment and the task demands that the environment entails have to be adapted to a level with which most road users can cope. Framing the traffic injury problem as predictable and preventable offers a tool to educate the public and influence policy makers that injuries, like many diseases, can be prevented.

7. CONCLUSIONS Only during approximately the past 50 years has public health had an active interest in improving highway safety in the United States through the application of data and surveillance tools, education and training, policy development, and public health practice. Although the death rate per VMT has decreased more than 95% in the United States since 1921 (from 24.08 deaths per 100 million miles traveled in 1921 to 1.16 in 2009), 33,808 people still lost their lives in traffic crashes in 2009. Globally, an estimated 1.3 million people die each year as a result of road traffic crashes, and 20e50 million people suffer serious nonfatal injuries (WHO, 2009). These crashes and injuries have devastating economic, health, and social costs for families and for society. Improving road safety means improving public health. Public health and traffic psychology have important roles to play in improving traffic safety and reversing the worldwide upward trend of traffic injury and death. Integrating more fully the public health function with those in traffic and transportation psychology can yield positive benefits. At the community level, traffic injury prevention can become a regular part of community health, community psychology and primary care, and can be integrated into all preventive health services. Through efforts to change behaviors and systems, traffic psychology can advocate for stronger traffic safety legislation and enforcement, and it can more rapidly translate human factors research results into new appropriate technology and engineering. Public health and traffic psychology can work together to contribute solutions to the traffic safety problem, reduce traffic injuries, and improve the quality of life of populations.

ACKNOWLEDGMENTS We are grateful to Les Fisher, MPH, Archivist for American Public Health Association’s Injury Control and Emergency Health Services section for providing access to key historical documents, and to Dr. Soames Job, Director of the NSW Centre for Road Safety, Roads & Traffic Authority, Sydney, Australia, for his helpful comments on the manuscript.

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on the benefits of seat belt and child safety seat usage. Washington, DC. AAA Foundation for Traffic Safety. (2007b). Improving traffic safety culture in the United states: The journey forward. Washington, DC. Allegrante, J. P., Hanson, D., Marks, R., & Sleet, D. A. (2010). Ecological approaches to the prevention of unintentional injuries. Italian Journal of Public Health, 7(2), 24e31. Allegrante, J. P., Marks, R., & Hanson, D. W. (2006). Ecological models for the prevention and control of unintentional injury. In A. Gielen, D. A. Sleet, & R. DiClemente (Eds.), Injury and violence prevention: Behavioral science theories, methods, and applications (pp. 105e126). San Francisco: Jossey-Bass. American Public Health Association. (1961). Accident prevention: The role of physicians and public health workers. New York: McGrawHill. Association of Schools of Public Health. (2011). What is public health? http://www.whatispublichealth.org. Accessed January 31, 2011. Baker, S. P. (1972). Injury control: Accident prevention and other approaches to reduction of injury. In P. E. Sartwell (Ed.), Preventive medicine and public health (10th ed.). (pp. 987e1005) New York: Appleton-Century. Blincoe, L. J., Seay, A. G., Zaloshnja, E., Miller, T. R., Romano, E. O., Luchter, S., & Spicer, R. S. (2002). The economic impact of motor vehicle crashes 2000 (Report No. DOT HS 809 446). Washington, DC: National Highway Traffic Safety Administration, U.S. Department of Transportation. Brannon, L., & Feist, J. (2009). Health psychology: An introduction to behavior and health. Belmont, CA: Wadsworth. Centers for Disease Control and Prevention. (2001). Motor-vehicle safety: A 20th century public health achievement. Morbidity and Mortality Weekly Report, 48(18), 369e374. Dellinger, A., & Sleet, D. A. (2010). Preventing traffic injuries; Strategies that work. American Journal of Lifestyle Medicine, 4(1), 82e89. Dellinger, A., Sleet, D. A., & Jones, B. H. (2007). Drivers, wheels, and roads: Motor vehicle safety in the twentieth century. In J. Ward, & C. Warren (Eds.), Silent victories: The history and practice of public health in twentieth-century America. New York: Oxford University Press. Dellinger, A. M., Sleet, D. A., Shults, R., & Rinehart, C. (2006). Preventing motor vehicle-related injuries. In L. Doll, S. Bonzo, J. Mercy, & D. Sleet (Eds.), Handbook of injury and violence prevention. New York: Springer. Dellinger, A. M., & Staunton, C. E. (2002). Barriers to children walking and biking to school: United States, 1999. Morbidity and Mortality Weekly Report, 51(32), 701e704. Espitia-Hardeman, V., & Paulozzi, L. (2005). Injury surveillance training manual. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Evans, L. (1991). Traffic safety and the driver. New York: Van Nostrand Reinhold. Evans, L. (2004). Traffic safety. Bloomfield Hills, MI: Science Serving Society. Federal Highway Administration. (2000). Our nation’s highways 2000. Washington, DC: Author. Federal Highway Administration. (2003). Manual on uniform traffic control devices for streets and highways. Washington, DC: U.S. Department of Transportation. In accordance with Title 23 US Code,

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Transportation Research Board. (1990). Safety research for a changing highway environment (Special Report No. 229). Washington, DC: National Research Council, Transportation Research Board. U.S. Census Bureau, Population Division. (2008). National population projections: Projections of the population by selected age groups and sex for the United States: 2010 to 2050 (Table 2). Washington, DC: Author. U.S. Department of Health and Human Services. (1992). Position papers from the Third National Injury Control Conference: Setting the national agenda for injury control in the 1990’s (Publication No. 1992634e666). Washington, DC: U.S. Government Printing Office. U.S. Department of Health and Human Services. (2010). Healthy people 2020. Washington, DC: Author. http://www.healthypeople.gov. Accessed January 31, 2011. U.S. Department of Health, Education and Welfare. (1979). Healthy people: The Surgeon General’s report on health promotion and disease prevention (Publication No. 79-55071). Washington, DC: U.S. Government Printing Office. von Holst, H., Nygren, A., & Andersson, A. E. (Eds.). (1997). Transportation, traffic safety and health (Vol. 3): Proceedings of the third international conference. Washington, DC. Stockholm: Karolinska Institute. Waller, P. F. (2001). Public health’s contribution to motor vehicle injury prevention. American Journal of Preventive Medicine, 21(4S), 3e4. Ward, J. W., & Warren, C. (2006). Preface. In Silent victories: The history and practice of public health in twentieth-century America. New York: Oxford University Press. Wegman, F., & Aart, L. (Eds.). (2006). Advancing sustainable safety. Leidschendam. The Netherlands: SWOV. Weingroff, R. F. (2003). President Dwight D. Eisenhower and the federal role in highway safety. Washington, DC: Federal Highway Administration. World Health Organization. (2009). Global status report on road safety. Geneva.

Chapter 33

Public Policy Rune Elvik Institute of Transport Economics, Oslo, Norway

Public policy refers to any action taken by public bodies in order to influence highway safety. Although transport policy in general has several objectives, the focus in this chapter is on policy designed to improve safety. The role of traffic psychology in contributing to an effective road safety policy is discussed. The following main questions are addressed in this chapter: 1. What are the principal elements of road safety policy? At what stages of policy making can traffic psychology contribute? 2. What is the scope for improving road safety by applying knowledge gained in traffic psychology and related disciplines? It is shown in this chapter that traffic psychology can make a major contribution to improving highway safety by informing public policy.

1. AN ANALYTIC MODEL OF POLICY MAKING Figure 33.1 shows an analytic model of highway safety policy making (Elvik & Veisten, 2005). The model is not intended as a description of actual policy making. It is a purely analytical model intended as a logical framework for identifying the types of reasoning and activities that constitute policy making. The stages identified by the model form a logical sequence; they should not be interpreted as a chronological ordering. The first stage of policy development is to find out what the problem is and identify factors that contribute to it. In short, what are the most important highway safety problems and what are the most important factors contributing to these problems? The next stage is to develop targets for improving safety and decide on whether these targets should be quantified or not. Once the ambitions for improving safety have been defined, a broad survey of potentially effective safety measures (stage 3) is needed to identify those measures that can make the largest contribution to reducing the number of fatalities and injuries. However, for various reasons, it may not be possible to introduce all effective safety measures; an Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10033-5 Copyright Ó 2011 Elsevier Inc. All rights reserved.

explicit consideration of constraints on safety policy can help in developing realistic policy options (stage 4). There will very often be more than one safety measure that can address a given safety problem; hence, developing alternative policy options that can be compared is instructive (stage 5). A key activity in policy development is to estimate the expected effects of safety measures on the number of accidents or the number of killed or injured road users (stage 6). These estimates should ideally be based on the best available knowledge regarding the effects on safety of various measures. Any prediction (i.e., prior estimate) of the safety effects of a program will be uncertain, and it may be useful to explicitly consider sources of uncertainty and how to reduce uncertainty (stage 7). As already mentioned, policy is always made within constraints that are not necessarily chosen or wanted by policy makers; usually, therefore, several considerations are relevant for policy choice, requiring complex trade-offs (stage 8). Once it has been decided to implement a set of safety measures, the effects of these measures should be evaluated in order to increase knowledge of their effects for use in future policy making (stage 9). Traffic psychology is not equally relevant at all stages of policy making. It can contribute in particular at stages 1e3, 6, and 9. A brief review of the potential contribution of traffic psychology to policy making follows.

2. OUTLINE OF THE POTENTIAL CONTRIBUTION OF TRAFFIC PSYCHOLOGY TO POLICY MAKING 2.1. Unsafe Road User Behavior as a Road Safety Problem (Stage 1 of Policy Making) Road accidents are influenced by many factors. One of the most important is unsafe road user behavior. This includes speeding, drinking and driving, not wearing protective devices, talking on cell phones while driving, and a host of other forms of behavior. No study has assessed the contribution of all types of unsafe road user behavior to accidents or injuries. However, Elvik (2010a) tried to assess the risk 471

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Interdisciplinary Issues

FIGURE 33.1 An analytical model of highway safety policy making

attributable to 15 different violations of road traffic law in Norway. Table 33.1 reproduces the estimates of the risk attributable to these violations. These estimates are highly uncertain, but it is not possible to estimate statistically the uncertainty of each of the estimates. Confidence intervals are therefore not provided. Attributable risk shows the potential reduction of the number of fatalities or injured road users if the violation is eliminateddthat is, replaced by driving that complies with the law. It is estimated as follows (Rothman & Greenland, 1998): Attributable risk ¼

PE$ðRR 1Þ ðPE$ðRR 1ÞÞ þ 1

(1)

where PE denotes the proportion of exposure for which the risk factor is presentdfor example, the proportion of vehicles exceeding the speed limit. RR is the relative risk associated with a violationdfor example, it is 2 if risk is doubled. If a violation represents 20% of traffic and doubles risk, the risk attributable to it is 0.167. This means that by eliminating the risk factor, the number of accidents can be reduced by 16.7%, given an unchanged amount of travel. It does not make sense to add the estimates of attributable risk presented in Table 33.1. To estimate the potential

for improving safety by eliminating all the violations, one can apply what has been termed the “method of common residuals” (Elvik, 2009a). The residual of an estimate of attributable risk is its complementary valuedthat is, the share of fatalities or injured road users not eliminated by eliminating the risk factor. Thus, for speeding, the residual with respect to fatalities is 1 0.230 ¼ 0.770. By applying the method of common residuals, it can be estimated that by eliminating the violations listed in Table 33.1, the number of fatalities can be reduced by 61% and the number of injured road users reduced by 35%. For fatalities, the estimate is 1 ð0:770$0:834$0:867$0:907$0:928$0:950$0:962$0:974$ 0:976$0:981$0:990$0:994$0:998$0:998$0:998Þ ¼ 1 0:390 ¼ 0:610 These estimates are probably too optimistic because violations tend to be correlated. A more conservative version of the method of common residuals, which attempts to account for correlations, suggests that eliminating the violations listed in Table 33.1 can reduce fatalities by 52% and injuries by 32%. For fatalities, this was estimated as

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TABLE 33.1 Risks Attributable to Violations of Road Traffic Law in Norway Estimate of attributable risk (proportion) with respect to fatalities and injured road users: Sorted by contribution to fatalities Violation

Fatalities

Injured road users

Speeding

0.230

0.094

Drinking and driving

0.166

0.034

Not wearing seat belts

0.133

0.032

Health problems in drivers

0.093

0.080

Use of illicit drugs and driving

0.072

0.027

Service and resting hours

0.050

0.022

Not yielding at intersections

0.038

0.038

Not yielding to pedestrians

0.026

0.025

Use of cell phone

0.024

0.024

Red light running

0.019

0.019

Illegal overtaking

0.010

0.003

Engine tuning of motorcycles

0.006

0.007

Short following distance

0.002

0.012

Lack of child restraints in cars

0.002

0.001

Non-use of daytime running lights

0.002

0.002

Source: Data from Elvik (2010a).

1 0:3900:770 ¼ 1 0:484 ¼ 0:516 Although these estimates are not very precise, they are probably correct in suggesting that major improvements in highway safety are possible by reducing or eliminating unsafe road user behavior. Traffic psychology can contribute to informing policy in many ways by studying road user behavior. The contributions include the following: 1. Identifying and describing the prevalence of various forms of potentially unsafe road user behavior. Progress in unobtrusive techniques of observation, as illustrated by a large-scale, in-vehicle naturalistic study (i.e., N ¼ 100 cars; Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006), makes it possible to survey behavior that used to be difficult to observe. 2. Estimating the risk associated with unsafe road user behavior, thus providing knowledge about factors contributing to accidents and the size of their contributions. 3. Studying why unsafe road user behavior is widespread: What are the motivations underlying this behavior? Can unsafe behavior be reasonably modeled as (subjectively) rational from the road users’ point of view? If

road users behave unsafely for reasons they think are good, does this imply that efforts designed to modify behavior will be ineffective? 4. To what extent can unsafe road user behavior be influenced by means of technical solutions that make such behavior impossible or unpleasant? These are just some of the questions that are relevant for policy development.

2.2. Developing Targets That Are Motivating (Stage 2) Many countries have developed national safety programs that are based on a quantified target for improving road safety (Organisation for Economic Co-operation and Development (OECD), 2008). International bodies, such as the OECD, recommend setting quantified targets for improving safety. However, setting targets that will motivate both public bodies and others that influence highway safety to make an extra effort involves a number of complexities (Elvik, 2008): 1. The targets should be supported by the top level of government and be developed in a process that involves

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2. 3. 4.

5. 6.

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all relevant stakeholders to ensure a consensus on the targets and a commitment to following them up. The targets set should be challenging but in principle achievabledthey should have the “right” level of ambition. There should not be too many targets in view of the available policy instruments designed to realize them. There should be mechanisms ensuring that responsible agencies have sufficient resources at their disposal to implement all safety measures that are needed to realize the targets. There should be a system for monitoring progress in realizing targets and providing feedback to responsible agencies on their performance. Incentives should exist to ensure commitment to targets from all agencies responsible for realizing them.

Again, both general psychology and traffic psychology can contribute to ensuring that the targets set for improving road safety will be as effective as possible. For example, psychological research (Locke & Latham, 2002) has found that targets that are ambitious are associated with better performance than less ambitious targets. On the other hand, there is risk of fostering a sense of helplessness by setting overly ambitious targets. Such targets may be discounted as utopian and may not have the motivating effects that challenging but achievable targets often have (Anderson & Vedung, 2005). To set ambitious but challenging targets, it helps to know what is the potential for improving safety by introducing various safety measures. A so-called “bottom-up” approach for setting targets derives a “realistic” target by adding up the estimated effects on safety of a number of safety measures that can be implemented. A “top-down” approach, on the other hand, approaches target setting from a more idealistic point of view. In practice, good targets involve a mixture of idealism and realism.

2.3. Surveying Potentially Effective Highway Safety Measures (Stage 3) Many measures may contribute to improving road safety. A comprehensive overview of such measures can be found in The Handbook of Road Safety Measures (Elvik, Høye, Vaa, & Sørensen, 2009), which describes a total of 128 measures addressing the following elements of the transport system: 1. 2. 3. 4.

Highway design (20 measures) Highway maintenance (9 measures) Traffic control (22 measures) Vehicle design, safety standards, and protective devices (29 measures) 5. Vehicle inspection (4 measures) 6. Driver training and regulation of professional driving (12 measures)

7. 8. 9. 10.

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Public education and information (3 measures) Police enforcement and sanctions (13 measures) Post accident care (3 measures) General-purpose policy instruments (13 measures)

Traffic psychology tends to be given blame or credit for safety measures that are directed at behavioral factors, such as driver training, information campaigns, or police enforcement. It is correct that traffic psychology has been involved in developing many of these measures, but it is a misconception to think that traffic psychology does not contribute to measures involving the technical components of the system. Knowledge produced by human factors experts regarding, for example, reaction times, cognitive capacity, visual performance, ergonomics, and many other specialties, has contributed importantly to current design standards for highways, traffic control devices, and automobiles. A freeway, for example, has been designed to minimize the task demands on drivers. It has no access points to properties along the road. There are no at-grade intersections. There are no surprising, sharp curves or steep hills. Pedestrians and cyclists are not permitted to use freeways. The road surface is smooth. Oncoming traffic is separated by a median. The risk involved in striking fixed obstacles has been reduced by impact attenuators. In short, a freeway is the type of road a psychologist might want to design in order to make driving as simple as possible and thus minimize the probability of errors being made. The effects on safety of measures targeted at road user performance and behavior are discussed more extensively in Section 3 (see also Chapter 16 for a focus on human factors).

2.4. Estimating the Expected Effects of Safety Measures (Stage 6) The Handbook of Road Safety Measures (Elvik et al., 2009) contains a wealth of information regarding the effects of road safety measures. However, a mechanical and uncritical use of the book is not recommended when developing road safety policy and estimating the effects of road safety measures. There are three main problems: 1. The Handbook of Road Safety Measures often states only an average effect of a measure, although the effect can reasonably be assumed to vary systematically, depending, for example, on characteristics of the measure. 2. The quality of studies that have evaluated the effects of a measure may vary, and a summary estimate of effect should be based on the best studies. 3. Not all measures have been evaluated with respect to their effect on accidents; in particular, this effect will be unknown, but has to be predicted, for new measures.

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Traffic psychology can contribute in particular with respect to the second and third of these points. Psychology has a long tradition of experimental research, and psychologists have contributed to the development of comprehensive methods for assessing the quality of research (Shadish, Cook, & Campbell, 2002). Any application of the results of road safety evaluation studies should rely on a critical assessment of the quality of this research because poorly designed studies tend to produce misleading estimates of the effects of road safety measures. This topic is discussed in greater detail in Section 3. The effects of well-established road safety measures on accidents reflect the net impacts of all causal pathways generating these impacts. In particular, road user behavioral adaptation will be endogenous with respect to effects on accidents; the effects on accidents always capture the effects of any road user behavioral adaptation. In other words, there is no need to “adjust for” behavioral adaptation when predicting the effects of measures whose effects on accidents have been extensively evaluated. The fact that road users adapt behavior is nevertheless not unproblematic because it normally reduces, and may even eliminate, the intended safety effect of a measure. This is different in the case of new road safety measures. To predict their effects on accidents, it is necessary to predict whether behavioral adaptation is likely to occur. A framework for analyzing and predicting the effects of road safety measures has been proposed by Elvik (2004) and is shown in Figure 33.2. A road safety measure will influence safety by modifying one or more basic risk factors that are associated with accidents. These risk factors include speed, mass, road surface friction, visibility, compatibility (differences in mass and crashworthiness between vehicles), complexity (the richness of information in a traffic environment), predictability (the accuracy of expectations), road user rationality, road user vulnerability, and system forgiveness (the safety margins built into the system). Changes in these risk factors influence the structural safety margindthat is, the safety margin built into roads and vehicles. These changes are sometimes referred to as the “engineering effect” of a road safety measure (Evans, 1985). The effect

475

of a road safety measure on accidents, however, is also determined by the behavioral adaptation it may elicit. Behavioral adaptation is sometimes in response to the risk factors a road safety measure is intended to influence, but it takes place before the measure is introduced. In Figure 33.2, this kind of behavioral adaptation is referred to as antecedent behavioral adaptation. As an example, drivers may adapt behavior to the technical condition of their cars. Technical defects may therefore not increase the risk of accident; once these defects are repaired following periodic motor vehicle inspection, drivers adapt behavior again, knowing that the car is in good technical condition. The net result could be that periodic motor vehicle inspection has no effect on accidents. Behavioral adaptation will sometimes also be the result of a safety measure, particularly if the measure is easily noticed, is associated with a large engineering effect, and road users can obtain an advantage by changing behavior (Amundsen & Bjørnskau, 2003; Bjørnskau, 1994). Will new safety measures, such as intelligent speed adaptation (ISA), intelligent cruise control, lane departure warning, or fatigue monitoring, lead to behavioral adaptation? ISA is a system that supports the driver in complying with speed limits. There are several versions of the system; one of them makes exceeding the speed limit impossible by regulating fuel supply to prevent acceleration to a speed higher than the speed limit. Because speeding is known to be an important risk factor for accidents and injuries (Elvik, 2009b), ISA would seem to be a potentially effective road safety measure. However, will drivers adapt their behavior to ISA? One common form of behavioral adaptation, increasing speed, is blocked by the system. Drivers could, however, adapt by becoming less alert. Some maneuvers, such as overtaking, might require more time and thus become more risky. Speed is such a powerful risk factor that it is difficult to believe that behavioral adaptation would entirely eliminate the effects of ISA, but it could reduce them. Intelligent cruise control is also a system that exists in many versions. The most technically advanced will warn the driver if headway becomes too small and activate the brakes if the driver fails to react. Despite the huge number of FIGURE 33.2 A model of causal chains that generate effects on accidents of a road safety measure. Source: Based on Elvik (2004).

476

rear-end collisions, maintaining a safe distance from vehicles ahead is generally a task drivers perform very well. The reliability of drivers in braking and stopping safely in car-following situations is probably well in excess of 999 in 1000. The challenge for intelligent cruise control is to design a system that is more reliable than the average driver. Whereas drivers can take account of factors such as a slippery road surface, going downhill, and the possibility of avoiding a collision by steering to the right or left, a technical system may not be able to adequately handle these complexities. If drivers come to rely fully on intelligent cruise control to perform a task currently done manually, there is a significant risk that the system will not improve safety. Lane departure warning devices present similar limitations. A lane departure warning system is basically unable to determine if a lane departure is intentional or not. If intentional, it may not necessarily involve any additional risk. The driver may change lanes on a freeway, having checked carefully that it can be done safely, but forget to use the indicator. The warning system may then be activated, possibly irritating the driver. Another problem is that a lane departure warning system may not function if lane markings or edgelines are covered by snow or are worn out. In short, the system may be unreliable and may activate warnings the driver perceives as false alarms. There is a risk that drivers may ignore the system, thus diminishing its potential effects on safety. Concerning fatigue monitoring systems, the major issue is still whether a reliable system can be developed. If a technically reliable system is developed, there is clearly a risk that drivers may utilize the system to drive when they are fatigued, trusting the system to wake them up in time. In short, an important task for traffic psychology is to try to predict if, and the extent to which, new road safety measures will be associated with behavioral adaptations that may reduce or completely offset the intended effects of these measures on safety.

2.5. Evaluating the Effects of Road Safety Measures (Stage 9) To continue to improve highway safety, it is important to evaluate the effects of as many safety measures as possible. With its long tradition of experimental research, traffic psychology can make a key contribution to evaluation by helping to design experimental evaluation studies. There are few such studies (Elvik, 1998), but if the huge advantages of randomized, controlled trials were more widely recognized, road safety evaluation could become a more rigorous discipline, relying less on imperfectly controlled observational studies than it does today. Psychologists should regard it as one of their professional duties to advocate the use of randomized, controlled trials whenever they see a possibility

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for implementing this design. When experimental study designs cannot be implemented, researchers should opt for the best possible quasi-experimental design (Shadish et al., 2002).

3. THE SCOPE FOR IMPROVING ROAD SAFETY: AN OVERVIEW AND A DISCUSSION OF SOME MEASURES 3.1. A Policy Analysis for Norway Highway safety has been greatly improved in many highly motorized countries in the past 35e40 years (Elvik, 2010b). However, there is still potential for considerable improvement of highway safety. A policy analysis for Norway (Elvik, 2007) indicated that the number of road accident fatalities could be reduced by more than 50% by 2020 if all cost-effective road safety measures are fully implemented. The term “cost-effective” denotes a road safety measure whose benefits, according to costebenefit analysis, are greater than its costs. In the road safety policy analysis made for Norway, four main options for road safety policy were developed. Table 33.2 shows the estimated effects on the expected number of fatalities of the main categories of safety measures that were included in each policy option. The mean annual number of fatalities during 2003e2006 was 250. In the baseline situation, involving no new safety measures but continued maintenance of measures already in use, the number of fatalities is expected to increase to 285 in 2020. These assumptions are common to all policy options. The following rows of the table show the estimated contributions of main categories of safety measures to reducing the number of road accident fatalities in Norway until 2020. Exogenous vehicle safety features are those already on the market and whose use is expected to increase in the near future without government regulation. These include air bags, electronic stability control, seat belt reminders, enhanced neck injury protection, and high ratings in new car assessment programs. New vehicle safety features include ISA, intelligent cruise control, eCall (automatic crash notification), and event data recorders. Road-related measures consist of several large or small highway improvements, such as bypass roads, lighting, guardrails, and converting intersections to roundabouts. Enforcement includes both speed cameras and traditional enforcement performed by uniformed police officers. New legislation includes making bicycle helmets and pedestrian reflective devices mandatory. Road user-related measures are older driver retraining and stimulating more hours behind the wheel before licensing of young drivers. Policy option A, optimal use of road safety measures, is not very realistic. It includes introducing a number of new motor vehicle safety standards, which the Norwegian

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477

TABLE 33.2 Potential Reduction of the Number of Road Accident Fatalities in Norway Expected annual number of road accident fatalities: Contribution of main categories of road safety measures to reducing fatalities

Baseline values and main contributing factors

Policy option A: Optimal use of road safety measures

Policy option B: Optimal use of measures controlled by the Norwegian government

Policy option C: Continue present policies

Policy option D: Strengthen present policies

Baseline number of fatalities and forecast for 2020 (common to all policy options) Mean 2003e2006

250

250

250

250

Expected in 2020 as a result of traffic growth

285

285

285

285

Reduction of the number of fatalities attributable to main categories of measures Exogenous vehicle safety features

49

55

58

55

New vehicle safety features

42

0

0

0

Road-related measures

26

28

34

39

Enforcement-related measures

24

29

3

43

New legislation

4

0

0

5

Road user-related measures

2

2

0

0

Total contribution of all measures

147

114

95

142

Expected in 2020 as a result of policy option

138

171

190

143

government cannot do unilaterally. Vehicle safety standards in Europe are introduced by consensus in international bodies, such as the United Nations Economic Commission for Europe or the European Union (Norway is not a member of the European Union). The new vehicle safety features already on the market will contribute to reducing fatalities, but the most effective measures controlled by the Norwegian government are highway improvements and police enforcement. How about driver training? Is it likely that improving driver training can make a major contribution to improving road safety? How about graduated driver licensing, which is widely regarded as a success in North America? Is it really true that road user behavior can only be effectively influenced by means of repressive measures such as enforcement and sanctions? These issues are discussed next, based on a critical review of current knowledge. Other chapters in this book treat some of the measures discussed here in greater detail. The review presented here is based mainly on The Handbook of Road Safety Measures (Elvik et al., 2009).

3.2. Basic Driver Training Elvik et al. (2009) reviewed and synthesized the results of 16 studies that evaluated the effects of basic driver training

on accidents. Basic driver training refers to the formal training of car drivers before they are licensed for the first time. Depending on age limits, most drivers who are trained for the first time in their lives are 15e18 years old. Figure 33.3 shows a funnel plot of 45 estimates of the effect of basic driver training on driver accident rates (accidents per million miles of driving). The abscissa shows estimates of effect; the ordinate shows the statistical weight of each estimate of effect. Statistical weight is based on the number of accidents: Estimates of effect based on a large number of accidents have more weight than estimates based on a small number of accidents. For a more detailed explanation, see Elvik et al. (2009). If estimates originate from the same theoretical population, their distribution should have a shape resembling a funnel turned upside down, with estimates based on small samples (at the bottom of the diagram) displaying a larger spread than estimates based on larger samples. The summary estimate of effect is 0.97, corresponding to a small accident rate reduction of 3%. As can be seen from the diagram, a considerable number of estimates of effect indicate an increase in the accident rate. A closer examination of the studies shows that the effects attributed to driver training vary depending on study design. This is

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FIGURE 33.3 Funnel plot of estimates of effects of basic driver training on accident rates

Statistical weight (fixed-effects model)

1800 Summary estimate (0.97)

1600

Interdisciplinary Issues

1400 1200 1000 800 600 400 200 0 0.100

1.000

10.000

Estimate of effect (log scale; 1.0 = no effect; < 1.0 = accident reduction; > 1.0 = accident increase)

demonstrated in Figure 33.4, which shows mean percentage changes in accident rates in studies employing different study designs. Study designs have been ordered from the strongest to the weakest. A favorable effect on accident rate has only been found in nonexperimental studies. Elvik et al. (2009) discuss various possible explanations of these findings. They

conclude that methodological explanations are unlikely to be correct, given the fact that a number of experimental evaluations have been made. They conclude that the most likely explanation is that drivers adapt their behavior to their perceived skills. In other words, drivers who think they are good drivers may adopt smaller safety margins than drivers who are less confident about their own skills.

15 11

Percentage change in accident rate

10 5 0 -2

-5

-7 -10 -13

-15 -20 -25 -30

-31 -35 Experimental studies

Before-after, matched comparison

Case-control, multivariate analysis

Case-control, stratification on confounders

Study design FIGURE 33.4 Effects of basic driver training on accident rates according to study design

Simple case-control studies

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In short, the challenge in driver training is to teach people that they do not know anythingdor that what they know when obtaining a driving license is only a very small part of what they need to know to drive safely. This is an almost impossible challenge. The basic skills needed to operate a motor vehicle are easy to learndmost teenagers can acquire these skills in just a few hoursdwhereas higher order cognitive skills are perhaps never fully learned.

3.3. Graduated Driver Licensing Graduated driver licensing (GDL) has been introduced as a means of making novice drivers understand that they are not yet mature drivers by restricting driving that involves enhanced risk, such as nighttime driving or carrying teenaged passengers. A large number of studies have evaluated the effects of GDL programs. Most of these studies report that GDL programs are associated with a reduction in the number of accidents (Elvik et al., 2009). However, there is evidence of publication bias, as tested by the trim-and-fill technique (Duval, 2005). Publication bias denotes a tendency not to publish research reports, for example, because the findings are not statistically significant or are regarded as anomalous, difficult to interpret or explain, or even unwanted. The trim-and-fill technique is a nonparametric statistical technique for detecting and adjusting for publication bias based on an analysis of funnel plots. The technique is based on the assumption that in the absence of publication bias, the data points in a funnel plot should be symmetrically distributed around the summary estimate. If there is asymmetry, this is taken to indicate publication bias, and symmetry is restored by adding data points that are presumably missing as a result of publication bias (Høye & Elvik, 2010). The crude summary estimate of effect for all accidents is a reduction of 18%; adjusting for publication bias lowers this to 11%. For injury accidents, the bias appears to be even greater. The crude estimate is a 14% accident reduction; adjusted for publication bias, the accident reduction is 6%. Moreover, a tendency is seen for studies that do not control very well for potentially confounding factors to attribute larger effects to GDL than do studies that control better for potential confounding factors. Despite these reservations, the literature does indicate that GDL programs are associated with modest improvements in novice driver safety. However, the effects are far too small to eliminate the difference in accident rate between novice drivers and experienced drivers.

3.4. Speed Enforcement: An Accident Modification Function The importance of speed enforcement should not be in doubt, given the fact that speeding is widespread and that

479

the risk attributable to it is substantial. It is nevertheless clear that neither police officers nor speed cameras can be deployed at all locations and at all times. To apply speed enforcement optimally, two issues need to be resolved: 1. How is the effect of speed enforcement on accidents related to the amount of enforcement? 2. How should enforcement be carried out in order to maximize its effect in time and space? With respect to the first of these issues, Elvik (2010a) developed an accident modification function for speed enforcement performed by uniformed police officers. Developing this function required considerable data editing and smoothing. Figure 33.5 shows the accident modification function. A reduction of the amount of enforcement from a certain baseline level is associated with an increase in the number of accidents. An increase in the amount of enforcement is associated with a reduction of the number of accidents, but the marginal effect declines rapidly. To maximize the effects of speed enforcement, the deployment of officers should be randomdthat is, the places and times targeted for enforcement should be selected at randomdso that every driver should, in the long term, face the same probability of encountering the police (Bjørnskau & Elvik, 1992). The rationale behind this is that a random deployment of enforcement will prevent road users from detecting any systematic pattern in enforcement and adapt their behavior to this. Moreover, enforcement targeted at particular locations tends to be self-defeating in the long term: Once the police have successfully deterred most violators, there is a tendency for enforcement to be reduced. Violations may then return to the baseline level. Regarding speed cameras, their effects tend to be very local (Ragnøy, 2002). To extend effects to a longer section of road, it may be necessary to electronically link several speed cameras and measure mean speed for the entire length of road covered by the linked cameras.

3.5. The Need for and Setting of Speed Limits The need for enforcing speed limits would not exist if speed limits did not exist. Why not leave the choice of speed to drivers? Is there a need for speed limits? This question is discussed by Elvik (2010c), who argues that although most drivers probably think they choose the right speed and see no need to change it, the choices of speed likely to be made by drivers if speed limits did not exist would not produce optimal outcomes from a societal standpoint. Specifically,

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Accident modification factor (1.0 = no change)

1.400

1.200

1.000

0.800

0.600

0.400

0.200

0.000 0

1

2

3

4

5

6

7

8

9

10

Relative change in enforcement (current level = 1.0; < 1.0 = reduction; 2 = double of current level) FIGURE 33.5 Accident modification function for speed enforcement. Source: Data from Elvik (2010a).

Elvik concludes that speed limits are needed for the following reasons: 1. Drivers tend to ignore, or assign minor importance to, impacts of speed that they do not immediately notice or that do not directly affect their personal utility. Specifically, environmental impacts of speed choice are largely ignored by drivers. 2. Drivers do not correctly perceive the relationship between speed and travel time. Gains in travel time attributable to small increases in high speed are overestimated, whereas corresponding gains attributable to small increases in low speed are underestimated. These misconceptions may lead drivers to commit more serious violations of low speed limits than of high speed limits because drivers erroneously think that they need to increase speed substantially to save time if initial speed is low, whereas small increases in high speeds do not produce the gains in travel time drivers think they do. Data on speed violations in Sweden provide evidence supporting these implications. 3. Drivers underestimate the increased risk of accident associated with increased speed. 4. Drivers underestimate impact speed in situations in which it is clear that an accident is unavoidable, but its severity can be reduced by braking.

5. Driver preferences with regard to safe speed are very heterogeneous, making the coordination of speed choices difficult. In short, driver speed choice is not objectively rational dthat is, it is not based on a correct assessment of all impacts of speeding, leading to a convergence of preferences regarding optimal speed. This does not mean driver speed choice cannot be reasonably modeled as subjectively rationaldthat is, as optimal given driver preferences and perceptions of the impacts of speed choice. A distinction between subjective and objective rationality is almost never made in modern analyses relying on the assumption that road user behavior is rational. This distinction, however, makes perfect sense with respect to speed choice. The implications of the divergence between subjective and objective rationality are profound. Someone who regards his or her choices as rational from his or her point of view will rarely see strong reasons for changing the choices. Making a different choice would suggest that the original choice was somehow stupid or wrong. Most people do not like to be told that they are stupid. To the extent that drivers are satisfied with their choices of speed, persuading them to make a different choice is likely to be difficult. Moreover, because preferences regarding speed vary greatly among drivers, any speed limit is likely to be

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481

unpopular and regarded as either too high or too low by a considerable proportion of drivers, at least if speed limits are close to the median preferences of drivers (i.e., limits are set so that 50% of drivers think they are too low and 50% think they are too high).

accident risk as perfectly acceptable, it is not very likely that they would see any advantage of introducing an invasive technology designed to discourage them from speeding, driving when fatigued, or committing simple errors such as forgetting to signal when turning.

3.6. The Prospects for Rewarding Safe Behaviordand Its Price Tag

4. DISCUSSION AND SUMMARY

Can safer road user behavior be stimulated by rewarding it? Until now, the difficulties of reliably observing road user behavior have precluded the introduction of systems designed to reward safe behavior. Today, technologies for unobtrusive observation of road user behavior are rapidly developing, enabling the introduction of rewarding systems that have so far not been possible. For example, a driving computer, containing a digital map, can record the following: 1. 2. 3. 4. 5. 6.

Route choice Speed Use of daytime running lights Following distance Use of indicators Impact speed in case of an accident

The cost of the equipment needed to record these data is rapidly decreasing. The advantages of recording the data would be huge. One could, for example, in principle eliminate the problem of incomplete accident reporting and inaccurate information regarding where accidents take place. If, in addition to a computer, small cameras were installed in cars, it would become possible to monitor driver alertness and distractions. In principle, a road pricing system can be designed to reward safe behavior by putting a price tag on, for example, speeding or tailgating (Elvik, 2010d). With such a system in place, drivers would soon discover that safe behavior brings a reward in the form of lower charges for using highways. A trial in Sweden, offering rewards for complying with speed limits, found that drivers do respond to economic incentives (Lindberg, 2006). However, many drivers would regard the system as an unacceptable invasion of privacy and might not perceive the lower charges associated with safe behavior as a reward because they would still be paying to use highways. An option that deserves consideration is to charge more for speeding than the societal cost it generates in order to make a surplus. This surplus could then be paid back to law-abiding drivers to make the reward for safe behavior more tangible. Drivers can be provided with monetary incentives for safe behavior if they consent to having their behavior monitored continuously and in great detail by a driving computer and, possibly, a camera capturing their face. Because many drivers probably regard the current level of

Highway safety has been greatly improved in many highly motorized countries in the past 40e50 years. The rate of progress has not been the same in all countries, but there is no doubt that highway travel is considerably safer today than it was when traffic fatalities peaked in the highly motorized countries in 1970e1972. What accounts for the improvement in highway safety? To what extent has knowledge gained in traffic psychology contributed to it? It is difficult to give very precise answers to these questions. The improvement in highway safety is no doubt the result of a large number of safety measures that have been introduced but probably also the result of less tangible factors, such as subtle changes in culture or a higher demand for and valuation of safety as a result of greater wealth. Traffic psychology may, in a sense, be regarded as the dismal science of traffic safety. The term “dismal science” is usually reserved for economics because economists often remind us that resources are scarce, that we cannot get everything we want, that we are greedy and egocentric, that cycles of boom and bust will repeat themselves, and so on. Traffic psychology reminds us that humans are the most difficult part of the highway system to change. Road users will commit errors, misperceive risks, or deliberately take risks, such as drinking and driving, speeding, and so on. One is left with the impression that little can be done to change this. This impression is too pessimistic. During the past 40e50 years, several important changes in road user behavior have contributed to improved safety. The wearing of seat belts has increased in all highly motorized countries. Children are more often restrained in cars than in the past. Drinking and driving has probably been reduced in many countries, although data confirming this are less complete than the data on seat belt wearing and the use of child restraints. In many motorized countries, more motorcycle riders wear helmets today than 40e50 years ago. In the United States, however, laws mandating the use of helmets by motorcyclists have remained controversial and have been repealed in many states. Despite these improvements, unsafe road user behavior continues to be a major road safety problem. What are the prospects of significantly reducing the contribution that unsafe road user behavior makes to traffic fatalities? It depends on which measures are taken to influence road user behavior. Persuasion alone is not likely to be very effective. Most road users think that their behavior is entirely appropriate and see no reason to change it. Telling them to change is

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unlikely to impress them. Repression may be more effective. More police enforcement will contain speeding and other types of unsafe behavior, but it is impossible for the police to be everywhere at all times. The risk of apprehension will remain low. From a theoretical standpoint, rewarding safe behavior is the most attractive option for promoting it. However, to reward safe behavior, it is necessary to observe behavior in some detail, and the technology permitting such observation will probably be regarded as highly intrusive by many drivers. Drivers may reject this technology, although it could make travel much safer than it is today. Perhaps the key contribution that traffic psychology could make to safety policy is therefore, in the manner of a dismal science, to warn against all sorts of wishful thinking that may influence this policy. It is wishful thinking to believe that road users will suddenly realize that their behavior is sometimes unsafe and make amends. It is wishful thinking to believe that massive police enforcement can solve the problem. It is wishful thinking to believe that new technologies, such as intelligent speed adaptation, intelligent cruise control, lane departure warning, or fatigue monitoring, will not elicit behavioral adaptation that may partly or fully offset the safety effects of these technologies. It is wishful thinking to believe that driver training can ever reduce novice driver accident rates to the same level as the accident rate of highly experienced drivers. It is wishful thinking to believe that drivers will welcome technologies that continuously and in great detail monitor their behavior, even if by doing so new opportunities are created for rewarding safe behavior and thus reduce accident rates. Although it is the role of traffic psychology to remind policy makers of the limits of their influence on the human element of the traffic system, psychologists should also point out that effective ways of influencing human behavior exist. Specifically, key contributions of traffic psychology to road safety policy include the following: 1. Encouraging and contributing to systematic surveys of road user behavior, particularly behavior that is important for safety 2. Analyzing the relationship between specific types of behavior and highway safety 3. Modeling road user behavior, particularly by identifying factors that contribute to unsafe behavior 4. Analyzing human capabilities and performance to help develop design guidelines for highways, traffic control devices, and motor vehicles 5. Contributing to the estimation of expected effects of road safety measures, particularly by trying to predict if new safety measures will elicit behavioral adaptation from road users 6. Critically assessing the quality of road safety evaluation research and advocating the use of randomized controlled trials whenever possible

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Interdisciplinary Issues

7. Contributing to the development of targets for improving highway safety that are maximally motivating for all stakeholders involved

REFERENCES Amundsen, A. H., & Bjørnskau, T. (2003). Utrygghet og risikokompensasjon i transportsystemet. (Report No. 622). Oslo, Norway: Transportøkonomisk Institutt. Anderson, M., & Vedung, E. (2005). Ma˚lstyrning pa˚ villova¨gar. Om det trafiksa¨kerhetspolitiska etappma˚let fo¨r a˚r 2007. Uppsala, Sweden: Cajoma Consulting. Bjørnskau, T. (1994). Hypotheses on risk compensation. In Proceedings of the Conference Road Safety in Europe and Strategic Highway Research Program (SHRP), Lille, France, September 26e28, 1994, Vol. 4. (pp. 81e98). Linko¨ping: Swedish Road and Transport Research Institute. Bjørnskau, T., & Elvik, R. (1992). Can road traffic law enforcement permanently reduce the number of accidents? Accident Analysis and Prevention, 24, 507e520. Duval, S. (2005). The trim and fill method. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysisdPrevention, assessment and adjustments (pp. 127e144). Chichester, UK: Wiley. Elvik, R. (1998). Are road safety evaluation studies published in peer reviewed journals more valid than similar studies not published in peer reviewed journals? Accident Analysis and Prevention, 30, 101e118. Elvik, R. (2004). To what extent can theory account for the findings of road safety evaluation studies? Accident Analysis and Prevention, 36, 841e849. Elvik, R. (2007). Prospects for improving road safety in Norway. (Report No. 897). Oslo, Norway: Institute of Transport Economics. Elvik, R. (2008). Road safety management by objectives: A critical analysis of the Norwegian approach. Accident Analysis and Prevention, 40, 1115e1122. Elvik, R. (2009a). An exploratory analysis of models for estimating the combined effects of road safety measures. Accident Analysis and Prevention, 41, 876e880. Elvik, R. (2009b). The power model of the relationship between speed and road safety. Update and new analyses. (Report No. 1034). Oslo, Norway: Institute of Transport Economics. Elvik, R. (2010a). Utviklingen i oppdagelsesrisiko for trafikkforseelser. (Report No. 1059). Oslo, Norway: Transportøkonomisk Institutt. Elvik, R. (2010b). The stability of long-term trends in the number of traffic fatalities in a sample of highly motorised countries. Accident Analysis and Prevention, 42, 245e260. Elvik, R. (2010c). A restatement of the case for speed limits. Transport Policy, 17, 196e204. Elvik, R. (2010d). Strengthening incentives for efficient road safety policy priorities: The roles of costebenefit analysis and road pricing. Safety Science, 48, 1189e1196. Elvik, R., Høye, A., Vaa, T., & Sørensen, M. (2009). The handbook of road safety measures (2nd ed.). Bingley, UK: Emerald. Elvik, R., & Veisten, K. (2005). Barriers to the use of efficiency assessment tools in road safety policy. (Report No. 785). Oslo, Norway: Institute of Transport Economics. Evans, L. (1985). Human behaviour feedback and traffic safety. Human Factors, 27, 555e576. Høye, A., & Elvik, R. (2010). Publication bias in road safety evaluation: How can it be detected and how common is it? Transportation Research Record, 2147, 1e8.

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Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data. (Report No. DOT HS 810 594). Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration. ¨ rebro, Sweden: Lindberg, G. (2006). Valuation and pricing of traffic safety. O ¨ rebro Studies in Economics 13, O ¨ rebro University. PhD dissertation, O Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. American Psychologist, 57, 705e717.

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Organisation for Economic Co-operation and Development. (2008). Towards zero: Ambitious road safety targets and the safe system approach. Paris. Ragnøy, A. (2002). Automatisk trafikkontroll (ATK): Effekt pa˚ kjørefart. (Report No. 573). Oslo, Norway: Transportøkonomisk Institutt. Rothman, K. J., & Greenland, S. (1998). Modern epidemiology. Philadelphia: Lippincott Williams & Wilkins. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.

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Chapter 34

Travel Mode Choice Stephen G. Stradling Edinburgh Napier University, Edinburgh, UK

1. INTRODUCTION AND SOME HISTORY Whereas many previous chapters have examined in detail what people do when they drive cars, this chapter shifts the focus to “Why do we drive?” and how can transport psychology contribute to encouraging a shift to more sustainable and less planetary damaging modes of transport? It begins with some conceptual analysis and historical background to human patterns of moving around; takes as given the need to reduce the impact of fossil fuelpowered transport on the planetary niche we humans occupy and places the transport changes needed in a larger context of changes in consumption patterns; summarizes studies on attitudes to travel change in the United Kingdom; considers the personal costs of such changes in terms of both energy expenditure and lifestyle patterns; briefly examines the journey experience of three travel modesdcar, motorcycle, and urban busdto identify what psychological needs different travel mode choices offer to meet; examines segmentation among people’s attitudes toward car use and the environment, finding a fourfold typology of die-hard drivers, car complacents, malcontented motorists, and aspiring environmentalists; considers the feasibility of people substituting car trips with more sustainable travel modes, distinguishing between those unable to change and those unwilling to change; and concludes by summarizing current methods for attempting to affect the necessary changes, some informed by psychological research and theory and some not, before it is too late to bring about the adaptation and mitigation to natural, managed, and human systems needed to avoid a warming world with its “unprecedented combination of climate change, associated disturbances (e.g., flooding, drought, wildfire, insects, ocean acidification), and other global change drivers (e.g., land-use change, pollution, overexploitation of resources)” (Intergovernmental Panel on Climate Change, 2007, p. 11). Why do we move around at all? Because we can, because we have to, because we like to is the simplest formulation dividing out the different kinds of motive forces driving (sic) travel behaviors and transport choices. All life-forms move, even if only to orient daily toward the Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10034-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

sun while remaining rooted in the earth. Animal life typically requires movement for sustenance, shelter, and mate selection to enhance individual and species survival. One corollary of this formulation is that if we want to change individual travel behavior, we need to vary the travel opportunities, lifestyle obligations, and/or personal inclinations shaping an individual’s activity space. The relatively new field of transport psychology examines psychological factors influencing travel and transport choices and behaviors. Research from this field suggests (at least) three axioms likely to apply in the consideration of what facilitates or constrains people moving arounddtheir travel choices: A1: Travel is an expressive activity; there are affective as well as instrumental components to travel behavior and choices. A2: Persons vary in their travel choices and in the perceptions, conceptions, and values that inform those choices. This variation is both between types of persons (demographic groups and attitude-based segments) and within individuals in situations with different travel agendas. A3: People are simultaneously adaptable and resistant to change. They can and do cope with changing circumstances or operating conditions (new car, car fitted with driver assistance and vehicle control systems, congestion, fuel crises, and inclement weather); they value the comfort and convenience of habits and routines, having typically expended some search effort in acquiring them. Human beings are large-brained bipeds who, although rarely in possession of perfect information, make fast, smart-enough choices based on heuristics that save computation time, avoid frozen stasis, and thus enable action in and on the world. However, we are also, as the title of Aronson’s (2008) standard textbook on social psychology notes, social animals, needing opportunities for social participation and interpersonal interaction and also support networks to alleviate the stress of dealing with the slings and arrows of daily hassles and the occasional, but 485

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inevitable, larger misfortunes; we also like the sense of autonomy, feeling in control, when presenting an identity in public places. Garling (2005) characterized the core determinants of personal travel behavior as in Figure 34.1. The temporal ordering of travel choices in Garling’s model is as follows: 1. Activity choice

What shall I do?

2. Destination choice

Where shall I do it?

3. Mode choice

How shall I get there?

4. Departure time choice

When shall I go?

Activity choiced“What do I need to do next?”dis primary in this formulation. The emphasis on “I” here means that the choice of activities reflectsdexpresses (A1)dthe complex biological and social identities of persons. Travel demand is driven by what people need or want to do and where they have to go to do itdtheir perceived travel obligations. The transport system shapes how they might get there and how much time they should allowdtheir matrix of travel opportunities. Some do and some do not enjoy themselves while doing it (A1 and A2; see also the section on journey experience)dshaping their inclinations to travel by different modes. Changesdto activity choice, destination choice, mode choice, departure time choice, or the intelligence of the transport infrastructuredwill be perceived as a challenge or an annoyance or both (A2 and A3). (This analysis applies to personal travel choices. Freight transport, which also burns much fossil fuel, requires a somewhat different, more instrumental account.) In 1964, Russian archaeologists found the remains of a wooden ski preserved in the acid soil of a Siberian peat bog that they dated to approximately 6000 BCE (Woods & Woods, 2000). A 4500-year-old rock carving in Norway shows a skier using a single pole for propulsion on skis probably 3 m long. Skis may be the earliest example of technological innovation being used to amplify the speed and distance of individualized land-based travel above our

Activity choice Travel demand

Spatial organization of the environment

Transport system

Travel choice

Travel

FIGURE 34.1 Core determinants of travel behavior. Source: Garling (2005).

Interdisciplinary Issues

natural endowment of a long-distance walking speed of approximately 4 mph (6 kph) and short-distance running speed of perhaps 15 mph (25 kph). The potters of Mesopotamia, between the rivers, are thought to have invented the wheeld“wooden discs spun in a horizontal position used to shape lumps of clay into vessels” (Woods & Woods, 2000, p. 34)dat least 5,000 years ago. There is evidence of the wheel being rotated from horizontal to vertical and used on sledges to facilitate freight transport by the Sumerians and also in India and China soon after 3500 BCE and in Egypt by 2500 BCE. By 1400 BCE, Egyptian craftsmen were making “strong, light wheels with separate rims, spokes, and hubs” (Woods & Woods, p. 35) that were used on fast chariots by elite soldiers and wealthy civilians. Thus, approximately 3,500 years ago, technological innovation was driving specialization of form and function, and access to fast wheeled vehicles was serving as a marker and amplifier of status differentials. Transport modes may be classified into three types: wholly self-propelled modes, such as walking, running, and swimming; augmented modes that amplify bodily effort, such as rowing, cycling, and skiing, or focus natural resources, such as sailing and paragliding; and fuelled modes, whether hay-powered such as horse-drawn carriages and farm wagons or motorized modes such as motorcycle, car, SUV, van, truck, bus, tram, ferry, train, and plane, which currently deplete natural fossil fuel resources. Technological effort and expertise is currently being directed at harvesting more of the earth’s natural resources such as biomass, hydrogen, wind, and solar energy to source sufficient quantities of renewable fuel to continue powering individual motorized modes in the future. As successive transport innovations have been introduceddhorses, mules, camels, trains, electric trams, buses, subways, cars, and commercial aircraft, along with their associated infrastructuredthe effective radius of people’s activity patterns has grown with increases in the speed of transport. However, there seems to be an average annual travel time budget per person that is relatively constant and has remained so historically and spatially. The UK National Travel Survey indicates that average travel time has held steady at between 350 and 380 h per person per year, or approximately 1 h per day, during the past 30 years (Department for Transport (DfT), 2006, Table 2.1). International compilations of travel time data show that this figure of approximately 1 h applies across all cultures and states of development for which data may be discerned (Metz, 2004). Indeed, Metz, following Marchetti (1994), argues that the origins of this average travel time of 1 h per day may date from the earliest human settlements, where the mean area of the territory of long-established villages was approximately 20 km2, corresponding to a radius of approximately 1.6 miles (2.5 km) or approximately 1 h’s

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walk from the periphery to the center and backdfrom farmstead to market and home againdat 4 mph (6 kph).

2. IMPACT OF MOTORIZED TRANSPORT ON THE PLANET Ponting (2007) asserts that after the agricultural revolution, which gradually but inexorably facilitated population growth across the planet, the second great transition in human history involved the exploitation of the earth’s vast (but limited) stocks of fossil fuels. It led to the creation of societies dependent on high energy use. This was a fundamental changeduntil the nineteenth century every society across the globe had very few sources of energy and the total amount of energy they could generate was small. This transformation was at least as important as the development of agriculture and the rise of settled societies. In its impact on the environment its effects were far greater and took place over a shorter period of time. Until this transition all the forms of energy used by human societies were renewable.. The last two centuries have been characterized not just by the use of nonrenewable fossil fuels (coal, oil, and natural gas) but by a vast increase in energy consumption. (p. 265)

Direct impacts of motorized transport on the planet include global warming through the production of greenhouse gases from the burning of fossil fuel; vehicle emissions affecting local pollution and health; vehicle noise; land take for roads, parking, and other infrastructure; extraction of materials for manufacture; and waste from scrapped vehicles. To maintain the habitability of the planet, transport choices need to be smarter choices. Transport psychology, an applied science, involves understanding and influencing transport choices. This chapter takes as given that it is true, if inconvenient (Gore, 2006, 2007), that l

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the world is warming, and a further rise of 3 or 4 C could be catastrophic for continued human habitability; warming is partly driven by an increase in greenhouse gas (GHG) emissions; GHGs are emitted by motorized transport powered by fossil fuels (not forgetting the particulate problem with otherwise more efficient diesel fuel and also the carbon cost of the manufacture, distribution, and disposal of vehicles and supporting infrastructure, including future electric vehicles); and GHG emissions need to be reduced to maintain the rather narrow habitable planetary niche to which humans are adapted.

As we enter the era of peak oil, problems with energy security and scarcity generating diplomatic incidents and oil wars, increased emissions fueling anthropogenic

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climate change, increased road congestion, and rapid growth in domestic and international aviation, there is an urgent need to burn less carbon-based fuel as we go about our daily business (Industry Taskforce on Peak Oil & Energy Security, 2008; Transform Scotland Trust, 2008). Table 34.1 shows a “wish list” compilation (Hounsham, 2005) of the behavior changes needed to reduce anthropogenic GHG emission levels, indicating that changes to transport behavior form just part of a large array of consumption behaviors needing remediation. What are the avenues for changing human travel behavior, making it less fossil fuel dependent? In 2007, the DfT noted that the [UK] Government fully recognizes the need to tackle the problem of CO2 emissions, and is taking action to: encourage more environmentally friendly means of transport; improve the fuel efficiency of vehicles; reduce the fossil carbon content of transport fuel; and increase the care that people take over fuel consumption while driving. (DfT, 2007b, p. 2)

TABLE 34.1 Behavior Changes to Current Human Consumption Patterns Needed to Reduce Greenhouse Gas Emissions l

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Transport: Minimizing use of cars, trying public transport, and walking or cycling to get from A to B Holidays, leisure, and travel: Choosing locations, activities, and transport modes to help the environment Rubbish: Minimizing waste, recycling, composting, and disposing properly of unwanted goods Food purchasing: Buying local produce, choosing organic items, avoiding depleted wild foods, adopting “seasonality,” choosing vegetarianism, and growing food at home Energy use in the home: Turning down heating, using low-energy lighting, switching off appliances, reducing energy demand through less “home mechanization,” insulating, and sourcing greener energy Chemicals: Reducing release of damaging or polluting chemicals through use of detergents, bleaches, garden chemicals, etc. Sourcing materials: Refusing items made from depleted resources (e.g., tropical timber) while actively seeking those made from recycled materials (e.g., waste paper) Water use: Cutting consumption, cutting waste, home gathering, and reusing Consumer hardware: Repairing rather than replacing, passing on unwanted goods, and disposing of items at the end of their life properly Green investment: Choosing environmental savings accounts, mortgages, etc. Active participation: Donating and joining and taking part in green activities Voting: Casting votes on environmental grounds Bearing witness: Promoting environmentally friendly behavior to others

Source: Hounsham (2005).

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Improving the fuel efficiency of vehicles and reducing the fossil carbon content of transport fuel (Figure 34.2) are supply-side measures, whereas encouraging use of more sustainable forms of transport and more fuel-conscious driving, for example, are demand-side measures. In 2010, Paul Clark, UK Parliamentary Under Secretary of State for Transport, in a speech to the Brake Best of the Best Fleet Safety Forum Conference in Birmingham, England, noted the following: The Department’s strategy is firmly focused on reducing CO2. Passenger cars generate over 58% of greenhouse gases from transport. Nearly 40% of CO2 emissions come from cars on commuting and business journeys. What’s more, these journeys also have the highest proportion of single car occupancy. If eco driving becomes part of the periodic training for the Heavy Goods Vehicle Drivers’ Certificate of Professional Competence, a preliminary assessment suggests that up to £300 m in fuel costsdand 600,000 tons of CO2dcan be saved annually if 90% are trained and drive in that manner. That is a telling statistic and we will be consulting shortly on how to achieve such an uptake. Where it is necessary to drive, then smarter driving techniques can help reduce emissions and improve safety in all types of vehicles. This has been clearly demonstrated through schemes like the Department’s Safe and Fuel Efficient Driving program SAFED. SAFED trains company van and lorry drivers to use their vehicles more fuel efficiently and more safely. There are numerous case histories showing that companies can enjoy significant cost savings by engaging with the scheme.

Unfortunately, as has been noted by several researchers (Midden, Kaiser, & McCalley, 2007; Vlek & Steg, 2007), the adoption of cleaner cars may still lead to overall increases in environmental burden through sheer growth in activity volumes as well as through rebound effects that are

Encouraging environmentally friendly forms of transport

Improving the fuel efficiency of vehicles

the result of successful implementation of a more efficient technology, which compensates for some of its environmental gains or even negates them entirely by stimulating additional, unanticipated resource consumption and/or use of the technology. Thus, supply-side measures such as improving the fuel efficiency of vehicles or reducing the fossil carbon content of transport fuel may actually stimulate demand by increasing distances traveled. Indeed, in the worst-case scenario, motorists, given more environmentally friendly cars and fuel, may believe they can thus drive more frequently, farther, and faster (with self-talk taking the form, “If I drive a car that is better for the environment, I can drive it more frequently/farther/faster without causing any more damage to the environment than I was before. And if I drive just a little bit more often/farther/faster I’ll still be doing less damage than I was before.”). This is akin to the risk compensation or behavioral adaptation drivers show in consuming car safety benefits as performance benefits (“With ABS and side air bags I will be more protected from the consequences of driving less safelydand can thus drive less safely!”) (Organisation for Economic Co-operation and Development, 1990; Stradling & Anable, 2008). In his foreword to Moser and Dilling’s (2007) Creating a Climate for Change: Communicating Climate Change and Facilitating Social Change, Robert W. Kates (2007) writes the following: My colleagues Anthony Leiserowitz, Tom Parris, and I have recently argued that at least four conditions are required for . accelerations in collective action. These include changes in public values and attitudes, vivid focusing events, an existing structure of institutions and organizations capable of encouraging and

Reducing the fossil carbon content of transport fuel

Increasing the care taken over fuel consumption while driving

Fuel duty differential

Fuel duty

Fuel duty Graduted VED

Fuel duty Investment in public transport Smarter Choices

CO2-based company car tax Enhanced Capital Allowances Vehicle labeling

Demand management

EU-level car industry targets Support for innovation

Renewable Transport Fuels Obilgation Enhanced Capital Allowances Support for innovation

Interdisciplinary Issues

Eco-driving in driving test Sustainable distribution program Act on CO2 campaign

FIGURE 34.2 Road transport CO2 policy measures in the United Kingdom. Source: Department for Transport (2007).

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fostering action, and practical available solutions to the problems requiring change.

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propensity for recycling behavior change amongst 90% propensity for household energy reduction amongst 89% propensity for car-related behavior change amongst 77% propensity for plane-related behavior change amongst 17%

This chapter next collates some evidence, from Scotland and the United Kingdom, on the demand-side changes that are necessary and particularly on motorists’ readiness for reduction in car use, focusing on the first of Kates’ conditionsdchanges in public values and attitudes.

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3. PUBLIC VALUES AND ATTITUDES ON CAR USE AND CLIMATE CHANGE IN THE UNITED KINGDOM

Thus, in August 2006 when the survey was undertaken, just prior to the release in the United Kingdom of An Inconvenient Truth (Gore, 2006) and concomitant substantial media coverage of the issue, already most (90 and 89%) of the UK public were persuaded of the need to modify their recycling and household energy use behavior, many (77%) of the need for reduction in car use, but few (17%) of the need for reduction in flying.

A module of questions about public attitudes on climate change and the impact of transport were included in the Office for National Statistics’ Omnibus Survey of August 2006 (DfT, 2007): l

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81% of UK adults said they were very or fairly concerned about climate change. 62% believed that “Individuals should try to limit their car use for the sake of the environment.” 44% agreed that “Air travel should be limited for the sake of the environment.”

Respondents were asked, “Which transport modes, if any, do you think are major contributors to climate change?” Overall, 80% said cars, 78% vans and trucks, 75% airplanes, 62% buses and coaches, 30% motorbikes, 25% ships and ferries, 24% trains, and 1% answered “None of these.” The relative rank ordering of their responses from most to least polluting roughly accords with the objective evidence, but perhaps most critically, hardly any (1%) thought that “none of these” motorized transport modes were major contributors to climate change. In terms of readiness to take personal remedial action, 78% of respondents agreed that they would be prepared to change their behavior in some way to help limit climate change. Those who were very or fairly concerned about climate change were more likely to say they would change their behavior than those who were not very or not at all concerned. The survey asked, “In the next 12 months which, if any, of the following things are you likely to do due to concerns about climate change?” Overall, 90% said recycle household rubbish; 71% be careful about using energy at home (e.g., TVs on standby); 66% use energy-saving lightbulbs; 51% walk some of the short car journeys you currently make; 44% buy more energy-efficient products; 40% cut out some nonessential car journeys; 32% use public transport for some current short car journeys; 29% share car journeys with others to reduce total journeys made; 18% cycle some of the short car journeys you currently make; 12% use other forms of transport instead of flying; and 9% reduce the number of flights you make.

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4. THE COSTS OF CHANGE A transport economist might summarize these findings as showing a readiness for change conditioned by the costs of changing. What would be the costs to the individual of changing? It has been suggested (Stradling, 2003, 2005, 2007) that all travel choices involve the interaction of three overarching factors: obligations (“What journeys do I have to make?”), opportunities (“How can I make those journeys?”), and inclinations (“How would I like to make those journeys?”). Changes to travel patterns first require people to articulate these questions about their current and planned traveldto show “travel awareness”dand second will involve changes to patterns of life (obligations), provision of alternatives (opportunities), and current preferences (inclinations). Joseph (2008, p. 15) noted that “economic appraisal still gives value and priority to small time savings (even though surveys and businesses say they value reliability more)” in the evaluation of proposed transport infrastructure projects. Transport psychology, when dealing with the costs of change from the standpoint of individual behavior, might begin by pointing out that all journeys involve a cost to the individual traveler in the expenditure of calories, concentration, and concern (Stradling & Anable, 2008), and thus an “energy cost,” as well as the expenditure of time and moneydthe availability of all of which will “condition,” in the economist’s sense, the likelihood of making a journey and of making it in a particular manner. Three types of “personal energy costs” in trip making have been suggested (Stradling, 2002a, 2007): 1. Physical effort when traveling is used for maintaining body posture in walking, waiting, or carrying. Comfortable seats will reduce the amount of such effort expended. Negotiating an awkward interchange while

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burdened with infants and baggage will increase it. In Scotland, 11% of adults report difficulty standing for 10 min and 12% difficulty walking for 10 min (Stradling et al., 2005), constraining their travel mode preferences. 2. Cognitive effort is needed to collect and process information before and during a journey. Route familiarity will reduce the amount of cognitive effort needed. Habitual journeys typically impose a lower cognitive load, which is part of the reason why forming travel habits is attractive. If the journey needs constant monitoring of progress and the seeking out or interpretation of information, this will tend to increase cognitive load. Both too much and too little cognitive effort are unattractive, but a Goldilocks amountdjust rightdwill tend to make the journey interesting and engaging (Stradling, 2001). 3. Nervous energy is expended on worry about whether the journey will be successfully and safely accomplished. Uncertainty about connection and arrival (“I don’t enjoy it. I’m in a rush and worry [whether] the bus will be on time, to get [me] to work”) or personal vulnerability (“I wouldn’t like to be there after darkdthe bus station has a reputation”) will tend to increase the amount of emotional spend on a journey (Stradling, 2002a). This suggests that the reason why “surveys . say they value reliability more” (Joseph, 2008) is psychological: Service reliability enables travelers to meet their travel plans and obligationsdthey can rely on itdwhereas an unreliable transport service entails l

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uncertainty and worry, and thus additional affective effort; making remedial plans, entailing additional mental effort; and undertaking remedial actions, requiring additional physical effort.

5. THE JOURNEY EXPERIENCE 5.1. Cars The actual or anticipated journey experience associated with different travel modes will affect travel mode choices. Although the disbenefits of car travel and threats to the quality of life from car traffic are increasingly apparent (Adams, 2000; Engwicht, 1999; Garling, Garling, & Loukopoulos, 2002; Garling & Steg, 2007; Goodwin, 2001; Litman, 1999; Newman & Kenworthy, 1999; Royal Automobile Club (RAC), 1995; Semlyen, 2000; Sloman, 2003; Stradling, 2002b, 2002c), car ownership continues to rise worldwide, despite a growing policy focus on reducing car dependency and achieving a shift in travel mode choice.

Interdisciplinary Issues

Were the automobile an organism, we would deem it as having been remarkably successful in carving out an environmental niche and in adapting the behavior of its host to its requirements. In little more a century, cars have colonized the planet. Future historians may well characterize the twentieth century as the century of the fossilfueled car, during which approximately 1 billion cars were manufactured (Urry, 1999), of which more than 500 million (Shove, 1998) are currently occupying the streets, garages, car parks, and grass verges of the world. What is the hold that this most successful of technological developments has over the human psyche, sufficient to induce “car dependence” (Newman & Kenworthy, 1999; RAC, 1995)? In the latter half of the past century, the car established itself as the dominant mode of travel in developed countries, whether measured by distance, frequency, or duration of travel. Even so, data for Great Britain, indicating that the car is used for approximately 60% of the average 1-h daily travel time (Stradling, 2001), suggest that the average car is idle for more than 23 h out of 24 h, consuming parking space and inexorably depreciating in value but not actually moving. However, although stationary for more than 95% of the day, the car while waiting in some convenient location embodies the potential for travel and for access to distant destinationsd“I could just jump in the car and go, if I wanted to”dand this potential for spontaneous travel is one of the psychological attractions of the car (Stradling, 2002a; Stradling, Meadows, & Beatty, 1998, 1999, 2000). A number of studies attest to the car as a symbolic object (Maxwell, 2001; Sachs, 1984) and to the importance of affective motivation rather than instrumental motives such as availability and directness in choosing a car over other transport modes (Abrahamse, Steg, Gifford, & Vlek, 2004; Bamberg & Schmidt, 2003; Ellaway, Macintyre, Hiscock, & Hearns, 2003; Exley & Christie, 2002; Gatersleben, 2004; Gatersleben & Uzzell, 2003; Jensen, 1999; Mann & Abraham, 2006; Maxwell, 2001; Reid, Armitage, & Spencer, 2004; Steg, 2004; Steg & Gatersleben, 2000; Steg, Geurs, & Ras, 2001a, 2001b; Steg & Tertoolen, 1999; Steg & Uneken, 2002; Steg, Vlek, & Slotegraaf, 2001; Stradling, 2002a, 2002b, 2002c, 2003; Stradling, Carreno, Rye, & Noble, 2007; Stradling, Hine, & Wardman, 2000; Stradling, Meadows, et al., 1998; Stradling, Meadows, & Beatty, 2001; Tertoolen, van Kreveld, & Verstraten, 1998; Wall, Devine-Wright, & Mill, 2004; Wardman, Hine, & Stradling, 2001; Wright & Egan, 2000) and in influencing driving style (Lajunen, Parker, & Stradling, 1998; Stradling, 2003). In the United Kingdom, the future travel behavior intentions of young people between the ages of 11 and 18 years are dominated by the desire to drive and/or own a car (Derek Halden Consultancy, 2003; Line, Chatterjee, & Lyons, 2010; Storey & Brannen, 2000), with predrivers

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Travel Mode Choice

aspiring to the perceived benefits of car driving: “Like you’re in control of loads of speed aren’t you?” (boy, aged 14 years; Step Beyond, 2006). A core component apparent from studies of the attractions of the car is the sense of autonomydfeeling in control. Many car drivers appreciate the autonomy that the automobile conveys: “I just like driving . I only go places when I can drive”; “One of the reasons I like driving is because I’m in control” (Stradling, 2007); “What do you enjoy most about driving?” “I suppose it would just have to be the independence, the feeling of freedom it gives and just the actual feeling of driving yourself, the speed, the cornering.. I just like the feeling of being able to control a vehicle in a competent manner” (Stradling et al., 1998). Many, although not all, users of public transport lament the lack of autonomy: “The problem I have with public transport is that I don’t feel in control”; “You don’t feel in control at all on public transport and you’re worried about connections all the time so you’re having to be aware of what the time is every moment”; “Last year I came in by public transport for about 2 weeks. It was hell. Freezing to death on platforms waiting for trains that were late. You’re not in control of your lifedthat’s the only way I can describe it, you’re just not in control” (Stradling et al., 1998). Young drivers aged 17e24 years score highest on a scale measuring the sense of identity gained from becoming a driver, a part of the expressive component of driving (A1; Stradling, 2007), endorsing items such as the following: Driving a car . l l l

l l l l

is a way of projecting a particular image of myself. gives me a feeling of pride in myself. gives me the chance to express myself by driving the way I want to. gives me a feeling of power. gives me the feeling of being in control. gives me a feeling of self confidence. gives me a sense of personal safety. (Stradling, Meadows, et al., 2001)

The automobile promises both autonomy and mobility, and the mobility conferred by the car brings access privileges. In Scotland, 97% of those in the top household income quintile have access to a car for private use compared to 32% of those in the bottom quintile (Stradling et al., 2005). Those from households with access to a car travel more often, farther, and for longer durations, thereby increasing the number and variety of destinations to which they have access. Ratings of convenience of access from home to local life-support services such as money (bank or building society), food (supermarkets and local shops), and health (general practice clinic and hospital outpatient department) are higher for those with a car; they enjoy more frequent

491

social interactions with their support network of relatives and friends and are thus less likely to suffer social isolation; more visit sports and cultural facilities; they report better health status, and fewer of them have disabilities causing difficulties with traveling; they rate themselves higher on indices of civic participation; more of them live in nicer neighborhoods; and fewer of them had used the local bus service in the past month (Stradling et al., 2005). These are just some of the benefitsdinstrumental, symbolic, and affective (Steg, Vlek, et al., 2001)dthat would be diminished were car use constrained, and they will likely form a central part of drivers’ calculation of the costs of change. Of course, such benefit does not come without cost. In an era of highly reliable cars, we grind toward gridlock as we suffer increasingly from congestion, resulting in unreliable journey times, and as Featherstone (2004) notes, Automobility makes possible the division of the home from the workplace, of business and industrial districts from homes, of retail outlets from city centers. It encourages and demands an intense flexibility as people seek to juggle and schedule their daily set of work, family, and leisure journeys . on the calculation of the vagaries of traffic flows. (p. 2)

Also, the task load a driver endures on a car journey in search of the psychological satisfactions is high. Driving is a skill-based, rule-governed, expressive activity requiring real-time negotiation with co-present transient others with whom the driver is currently sharing the public highway and seeking safe and timely arrival while avoiding intersecting trajectories. There are task demands at the strategic, tactical, and control level. Speed is varied to manipulate perceived task difficulty (Fuller, 2005). Panou, Bekiaris, and Papakostopoulos (2005) characterized eight operational levels to the driving task, and Table 34.2 adds two more. Driving is an attractive travel mode choice despite consuming calories, requiring concentration, causing concern (Stradling & Anable, 2008), and making many demands on the driver.

5.2. Motorcycles There are a number of important differences between driving a car and riding a powered two-wheeler, including the sources of psychological satisfaction and hence the journey experience (Broughton, 2006, 2007, 2008; Broughton & Walker, 2009; Broughton et al., 2009; Mannering & Grodsky, 1995). Broughton (2007) showed motorcyclists photographs of various road scenes and asked them to rate on 5-point scales how risky and how enjoyable riding would be in each. He identified three types of bikers, shown in Figure 34.3. For a small group, approximately 8%, rated enjoyment increased as rated risk increased and enjoyment peaked at high risk levels. They were labeled

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PART | VI

TABLE 34.2 Ten Components of the Driving Task 01. Strategic tasks

Activity choice and mode and departure time choice. Discern route alternatives and travel time.

02. Navigation tasks

Find and follow chosen or changed route; identify and use landmarks and other cues.

03. Road tasks

Choose and keep correct position on road.

04. Traffic tasks

Maintain mobility (“making progress”) while avoiding collisions.

05. Rule tasks

Obey rules, regulations, signs, and signals.

06. Handling tasks

Use in-car controls correctly and appropriately.

07. Secondary tasks

Use in-car equipment, such as cruise control, climate control, radio, and mobile telephone, without distracting from performance on primary tasks.

08. Speed task

Maintain a speed appropriate to the conditions.

09. Mood management task

Maintain driver subjective well-being, avoiding boredom and anxiety.

10. Capability maintenance task

Avoid compromising driver capability with alcohol or other drugs (both illegal and prescription), fatigue, or distraction.

Source: Components 1e8 from Panou et al. (2005).

(48%; for whom enjoyment peaked at low risk)dboth of which, although to differing degrees, ride despite the risks rather than because of them. Factor analysis of ratings of features of the photos (Broughton, 2005; Broughton & Stradling, 2005) showed two sources of enjoyment for bikers in general: speed in a straight line and the mastery challenge of taking the right line through bends (some preferred one, some the other, and some liked both). Riders readily attest to both speed and bends “feeling good” and to enjoyment of the ride being a prime motivation for biking, suggesting a high expressive component (A1) in riding.

5.3. Urban Buses One barrier to increased bus patronage has been held to be the image of bus services as “a transport mode that has become associated with young people . elderly people . and people on low incomes . i.e., a mode of last resort” (Bus Partnership Forum, 2003, p. 9). However, a study in Edinburgh, Scotland (Stradling et al., 2007), found image to be the factor of least concern to urban bus users: In descending order of endorsement, the factors that generated dislike or discouragement of bus use were as follows: l

l

l

“risk seekers.” Their motivations for riding and while riding were different from those of the other two, equal-sized groupsdrisk acceptors (48%; for whom rated enjoyment peaked at middling levels of rated risk) and risk aversive

l

Feeling unsafe (e.g., “Drunk people put me off traveling by bus at night”) Preference for walking or cycling (e.g., “I prefer to walk”) Problems with service provision (e.g., “No direct route”) Intrusive arousal (e.g., “The buses are too crowded,” “The seats are too cramped,” “People using mobile phones,” and “The drivers often brake too harshly”) FIGURE 34.3 Rated risk and enjoyment of powered two-wheeler users. Source: Broughton (2007).

5

4

Enjoyment

Interdisciplinary Issues

3

2

1 1

2

3

5

4

Risk Risk acceptors

Risk aversive

Risk seeker

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l l

l

l

Travel Mode Choice

493

Cost (e.g., “The fares are too expensive”) Preference for car use (e.g., “I feel more in control when I drive”) Disability and discomfort (e.g., “There are not enough hand rails inside the bus”) Self-image (e.g., “Traveling by bus does not create the right impression”)

These factors all show social and affective concerns with the quality of the urban bus travel experience as well as more instrumental reasons for service dissatisfaction. Drunks and groups of youths on the bus were perceived as threatening, and the effect was amplified during the hours of darkness. The uncertainty of waiting for the bus, especially at night, was also a source of anxiety to some bus users. Such factors may, in the limiting case, induce avoidance behavior: “I refuse to travel to Leith or West Edinburgh by bus at night. I don’t feel safe and other passengers can be very intimidating” (female, age 28 years); “I would like to travel to and from Leith in the evening, but I don’t because the direct buses are infrequent and I am fearful of the bus stops and of drunks” (female, age 56 years). One respondent in the study by Stradling et al. (2007) indeed disliked the core premise of public transport: “What do you like and dislike about traveling by bus in Edinburgh?” “General dislike of public transport as have to travel with general public” (female, age 26 years). However, a number of comments revealed that public transport was, for some, an opportunity to engage in positive interpersonal interactions with fellow passengersd social exchangedwhether with friends, acquaintances, or co-present strangers: “I enjoy traveling by bus in Edinburgh, because you can see what is going on and sometimes you can get into conversation with other passengers” (female, age 58 years). Engwicht (1993, 1999), in characterizing cities as inventions to maximize exchange opportunities and minimize travel, regarded “streets as a dual space for both movement and exchange” (Engwicht, 1999, p. 19) with “plenty of opportunities for spontaneous

Pleasant

Deactivated

Activated

Ideal bus journey

Social exchange (chatting)

Feeling unsafe Unpleasant Intrusive arousal FIGURE 34.4 Russell’s (1980, 2003) typology of affective states and the urban bus journey experience

exchanges on the walk to the public transport stop, and while riding with others” (p. 19). However, for the bus, as with other forms of public transportation, there is permanent tension between the exchange and movement roles. On a bus, the rules of social exchange including the etiquettes of co-presence apply when “having to travel with the general public” and endure enforced proximity while respecting private space, whereas for the bus as occupier of a movement space, destination choice is constrained by routes, service frequency and journey duration are fixed by timetabling, and fare collection and verification of travel entitlement govern place and pace of entry. Russell (1980, 2003) characterized affective states in a typology involving two orthogonal dimensionsdpleasant/unpleasant and activated/deactivateddgiving four emotion quadrants. In describing what they liked about bus travel in Edinburgh, a number of respondents indicated a state of mind that is in contradistinction to the annoyances and intrusions of “unwanted arousal” and may be the reverie that unwanted distractions intrude on. This state of mind appears to involve being transported while switched off. It is smooth, tranquil, undisturbed, relaxed, absorbed, engaged with the moment yet “elsewhere,” and is pleasurable without being ecstatic. It exemplifies the passive nature of being a passenger. Figure 34.4 uses Russell’s scheme to suggest possible transitions between pleasant and unpleasant states (arrows)dbetween greater and lesser amounts of journey pleasure and thus, potentially, between greater and lesser amounts of customer satisfaction and repeat patronage. The ideal urban bus journey experience is pleasant/deactivated, and bus rides that bring about unpleasant/activated journey experiences are to be avoided.

6. ATTITUDES TOWARD CAR USE AND THE ENVIRONMENT Current attitudes toward car use in the United Kingdom may be characterized as ambivalent. Table 34.3 shows results from a number of surveys that demonstrate this ambivalence in simultaneously espoused attitudes (Dudleston, Hewitt, Stradling, & Anable, 2005; Stradling, 2006; Stradling, Meadows, et al., 2001). Table 34.4, using data from the British Social Attitudes survey collected during the summer of 2006 (Stradling, Anable, Anderson, & Cronberg, 2008), shows both consensus and differentiation. A substantial majority of both the general adult population (“all adults”) and motorists in Britain (“drivers”) are convinced and concerned about the influence of transport on climate change. Indeed, two-thirds agree, with only 1 in 10 disagreeing, that “for the sake of the environment, everyone should reduce how much they use their cars.” Similar numbers agree that individual efforts should be made and will contribute. However, approximately one-fourth of

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TABLE 34.3 Ambivalent Attitudes on Car Use in the United Kingdom (n ¼ 656e791)

TABLE 34.4 Attitudes on Car Use and the Environment in Britain

Driving a car gives me freedom to go where I want when I want

95%

Driving a car is a convenient way of traveling

93%

I like traveling in a car

84% BUT

Driving a car is stressful because of congestion on the roads

53%

The current level of car use has a serious effect on climate change.

Driving a car is stressful because of the behavior of other drivers

53% AND

I am concerned about Concerned the effect of transport on climate change.

I am trying to use my car less

43%

I would like to reduce my car use but there are no practical alternatives

57%

both drivers and adults in the United Kingdom still espoused the position that “people should be allowed to use their cars as much as they like, even if it causes damage to the environment.” More high-mileage drivers, likely to be particularly affected by constraints on car use, support unlimited car use, with 35%of high-mileage drivers (>10,000 miles/16,000 km a year) agreeing with the statement compared to 15% of low-mileage drivers (<5,000 miles/8,000 km a year). Even so, that means there are many, indeed a majority (62%; 3% responded “don’t know”), of high-mileage drivers who do not think that people (such as themselves) should be allowed to use their cars as much as they like, despite the ensuing inconvenience. From a series of questions on attitudes toward car use and the environment, drawing on work by Anable (2005), cluster analysis derived four discernibly different groups of motorists (Dudleston et al., 2005; Stradling, 2007a): die-hard drivers, car complacents, malcontented motorists, and aspiring environmentalists. The segments differ in the extent to which they exhibit attachment to the car, are willing to consider alternative modes, are already multimodal, feel willing and able to reduce their car use, are aware of transport issues, acknowledge the transport contribution to environmental problems, and say they are prepared to bear additional cost for continuing car use: Die-hard drivers (DHD) (~24% of UK drivers) like driving and would use the bus only if they had to do so. Few believe that higher motoring taxes should be introduced for the sake of the environment, and many support more road building to reduce congestion. Car complacents (CC) (29% of drivers) are less attached to their cars but currently see no reason to change. They generally do not consider using transport modes other

Interdisciplinary Issues

All Adults

Drivers

n [ 3220

n [ 2233

80%

82%

81%

84%

n [ 930

n [ 541

Agree Disagree

66% 10%

66% 11%

Anyone who thinks Disagree that reducing their own car use will help the environment is wrongdone person doesn’t make any difference.

59%

62%

People should be allowed to use their cars as much as they like, even if it causes damage to the environment. High-mileage drivers (>10,000 miles/16,000 km per year)

23%

24%

For the sake of the environment, everyone should reduce how much they use their cars.

Agree*

Agree

Agree

35%

*Agree ¼ strongly agree þ agree; concerned ¼ very concerned þ fairly concerned; disagree ¼ strongly disagree þ disagree. Source: Stradling et al. (2008).

than the car, and faced with a journey to make, they will commonly reach for the car keys. Malcontented motorists (MM) (23% of drivers) find that current conditions on the road, such as congestion and the behavior of other drivers, make driving stressful. They would like to reduce their car use but cannot see how. They say that being able to reduce their car use would make them feel good, but they believe there are no practical alternatives for the journeys they have to make. In Scotland, more members of this group live in accessible rural areas. Aspiring environmentalists (AE) (23% of drivers) are actively trying to reduce their car use, already use many other modes, and are driven by an awareness of environmental issues and a sense of responsibility for their

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Travel Mode Choice

495

DHD CC

MM

AE

thus, potentially, to be avoided. Most of the CCsdand more than in the other segmentsddo not consider other mode options but simply get in the car. Although equivalent proportions of MMs and AEs are trying to use the car less, hardly any of the MMs think it will be easy, unlike the AEs. MMs see themselves as willing but unable; they have the inclination to cut car use but lack the opportunity. More of the DHDs and CCs would like more roads built to ease congestion; many more DHDs support unrestricted car use and the “right to automobility” and also think global warming threats are exaggerated. On the other hand, more AEs think car users should pay higher taxes, and more say they are prepared to pay them if the revenue is directed to public transport improvements. Cluster analysis also identified three types of nondrivers from the third of Scottish households that do not have access to a car:

24%

29%

23%

23%

l

I like traveling in a car.

98

82

82

73

l

I find car driving can be stressful sometimes.

25

28

66

67

I am trying to use the car less.

8

29

62

83

Reducing my car use would make me feel good.

5

21

65

78

I would like to reduce my car use but there are no practical alternatives.

49

54

81

46

Being environmentally responsible is important to me.

61

76

85

89

Environmental threats such as global warming have been exaggerated.

39

19

20

9

People should be allowed to use their cars as much as they like, even if it causes damage to the environment.

48

13

19

7

For the sake of the environment, car users should pay higher taxes.

4

5

17

39

I would be willing to pay higher taxes on car use if I knew the revenue would be used to support public transport.

11

9

38

46

It is important to build more roads to reduce congestion.

72

23

60

30

contribution to planetary degradation (Anable, 2005; Dudleston et al., 2005; Stradling, 2007). Table 34.5 shows examples of differences between the four car driver groups. Most motorists, especially DHDs and even AEs who are keen to cut car use, like traveling in cars: Cars are comfortable, convenient, convey autonomy and mobility, and promise the benefits of speed, which is why cutting car use is a challenge. However, many drivers, except the DHDs and CCs, find that car use can be stressful and is

TABLE 34.5 Proportions of Each Driver Type Agreeing with Environmental Attitude Items

Weighted percentage over four UK samples, n ¼ 3471 Percentage strongly agree þ agree

AE, aspiring environmentalists; CC, car complacents; DHD, die-hard drivers; MM, malcontented motorists.

l

Car skeptics (8% of adults) are travel aware, environmentally aware, managing without a car, and more likely to use bicycles and to support constraints on unfettered car use. Reluctant riders (7% of adults) tend to be older and less well off, involuntarily dependent on public transport, and where possible travel as passengers in other people’s cars. Car aspirers (7% of adults), more of whom are unemployed, from lower social classes, and environmentally unaware, need better access to destinations than their current high bus use provides and for this and other reasons aspire to car ownership.

7. SUBSTITUTING MORE SUSTAINABLE MODES FOR CAR USE Do motorists make all their journeys by car? In asking them to cut their car use and substitute more sustainable modes, are they being called upon to venture into the unknown? Studies in Scotland (Stradling, 2005, 2007) show that although most drivers drive frequently, with 96% reporting using the car “once or twice a week” or more often, almost half (46%) report traveling as a passenger in a car with the same frequency, and 9% take a taxi that often. More than half (56%) have used bus and train, 1 in 6 say they cycle once a month or more often, and 8 of 10 say they walk for at least 10 min once a week or more often. Indeed, only 1.1% of Scottish drivers use only one mode and thus do all their traveling by car. Six in 10 report use ofdand thus familiarity withdfive or more modes. In three studies of travel awareness (Dudleston et al., 2005; NFO World Group & Napier University Transport Research Institute, 2001, 2003), Scottish drivers were asked how often they undertook various lifestyle maintenance activities and, for those they undertook, how often

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PART | VI

they used various travel modes, including car, to access these activities. Those who undertook each activity by car were then asked whether it would be practical for them to use each of four more sustainable modes (walk, cycle, bus, and train) for that activity. Table 34.6 shows, for a set of trip types currently undertaken by car, the percentage of drivers who say they could undertake such trips by each of four other modes (rows may total more than 100% because some respondents indicated it would be practical for them to use more than one alternative mode). The activities are arranged in ascending order by the percentage saying that “none of these” would be a practical alternative for them, thereby indicating the substitutability of trip types from most (child escort to school: only one-fourth of parents would not be able to do it otherwise) to least (supermarket shopping: slightly more than half say they could not do it other than by car, which leaves 43% who could). Farrington, Gray, Martin, and Roberts (1998, p. 3) deemed as structurally dependent on the car “those who are dependent . because there are no viable alternatives” and as consciously dependent on the car “those who rely on their vehicle but could realistically undertake their journeys by alternative modes.” The former are unable to switch modes, whereas the latter are unwilling.

TABLE 34.6 Substitutability of Current Car Journeys: Percentage Who Could Substitute Current Car Journey by Other Mode n ¼ 392e1598

Walk Cycle Bus

Train None of These

Take children to/from school*

59

3

16

<1

28

Town center shopping

23

2

43

13

31

Visit friends or relatives

39

9

28

11

35

Evenings out for leisure purposes

26

1

34

9

42

Leisure activities 21 during the weekend

9

27

12

48

Take children to 29 leisure activities*

4

27

4

49

Overall, only 11% of car drivers in Scotland indicated that they could not practically use a bus, train, walk, or cycle for any of their journeys and are thus structurally car dependent. They see themselves as having no opportunity to do otherwise. Seven percent were consciously car dependent: They could realistically undertake all the current car trip types about which they were questioned by more sustainable modes, but they had no inclination to do so. These two figures establish the ends of the potential modal shift distributiondthose who cannot and those who will not cut car use. The segmentation analysis on driver types detailed previously provides additional differentiation of the terrain.

8. DEMAND-SIDE BEHAVIOR CHANGE Transport researchers in the United Kingdom (Cairns, Davies, Newson, & Swiderska, 2002; Cairns et al., 2004; Government Operational Research Service, 2005; Rye, 2002; Steer Davies Gleave, 2003) have attempted to estimate the effects of mobility management measures on future car use. Mobility management measures are techniques that seek to persuade and assist people in changing travel habits and patterns. Cairns et al. (2004) concluded that if such demand-side measures were given greater policy priority in the United Kingdom, they have the potential to achieve a reduction in peak urban traffic of approximately 21% (off peak, 13%) and a UK nationwide reduction of all traffic of approximately 11%. They suggested that workplace travel plans could achieve between a 10 and 30% reduction in solo car use, school travel plans between 8 and 15%, and personalized travel planning initiatives between 7 and 15%. On the other hand, in a meta-analysis, Mo¨ser and Bamberg (2008) suggest rather lower effects because, in practice, transport evaluations typically employ one or more of the following: l l

l

l

Go away for a weekend

<1

<1

20

40

53

Travel to worky

15

10

28

9

55

Supermarket shopping

19

3

26

<1

57

*Respondents with children in the household. y Respondents who travel to work by car.

Interdisciplinary Issues

A one-group pre-post test design Weak analytical techniques to synthesize the data obtained (e.g., narrative-style analysis) Sample sizes too small to allow statistical effects to be establisheddunrepresentative samples A tendency to report only “good practice” case studies

Researchers in the neighboring domain of health psychology have collated a number of tested techniques for changing the behavior of individuals (Abraham & Michie, 2008). Such techniques, which typically go beyond reasoned argument in attempting to inculcate demand-side behavior change, might successfully transfer to transport psychology interventionsdan empirical matter germane for investigation. Table 34.7 provides a brief description of 26 techniques of demonstrated effectiveness. Abraham and Michie (2008) also give the theoretical provenance of each technique. To date, however, there is

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Travel Mode Choice

497

TABLE 34.7 Behavior Change Techniques Effective in Health Interventions Technique

Description

01. Provide information about the behaviorehealth link

General information about behavioral risk, such as susceptibility to poor health outcomes or mortality risk in relation to the behavior.

02. Provide information on consequences

Information about the benefits and costs of action or inaction, focusing on what will happen if the person does or does not perform the behavior.

03. Provide information about others’ approval

Information about what others think about the person’s behavior and whether others will approve or disapprove of any proposed behavior change.

04. Prompt intention formation

Encouraging the person to decide to act or set a general goaldfor example, to make a behavioral resolution such as “I will take more exercise next week.”

05. Prompt barrier identification

Identify barriers to performing the behavior and plan ways of overcoming them.

06. Provide general encouragement

Praising or rewarding the person for effort or performance without this being contingent on specified behaviors or standards of performance.

07. Set graded tasks

Set easy tasks, and increase difficulty until target behavior is performed.

08. Provide instruction

Telling the person how to perform a behavior and/or preparatory behaviors.

09. Model or demonstrate the behavior

An expert shows the person how to correctly perform a behavior (e.g., in class or on video).

10. Prompt specific goal setting

Involves detailed planning of what the person will do, including a definition of the behavior specifying frequency, intensity, or duration as well as specification or at least one context (i.e., where, when, how, or with whom).

11. Prompt review of behavioral goals

Review and/or reconsideration of previously set goals or intentions.

12. Prompt self-monitoring of behavior

The person is asked to keep a record of specified behaviors (e.g., in a diary).

13. Provide feedback on performance

Providing data about recorded behavior or evaluating performance in relation to a set standard or others’ performancedthat is, the person receives feedback on his or her behavior.

14. Provide contingent rewards

Praise, encouragement, or material rewards that are explicitly linked to the achievement of specified behaviors.

15. Teach to use prompts/cues

Teach the person to identify environmental cues that can be used to remind him or her to perform a behavior, including times of day or elements of contexts.

16. Agree behavioral contract

Agreement (e.g., signing) of a contract specifying behavior to be performed so that there is a written record of the person’s resolution witnessed by another.

17. Prompt practice

Prompt the person to rehearse and repeat the behavior or preparatory behaviors.

18. Use follow-up prompts

Contacting the person again after the main part of the intervention is complete.

19. Provide opportunities for social comparison

Facilitate observation of non-expert others’ performance (e.g., in a group class or using video or case study).

20. Plan social support/social change

Prompting consideration of how others could change their behavior to offer the person help or (instrumental) social support, including “buddy” systems, and/or providing social support.

21. Prompt identification as role model

Indicating how the person may be an example and influence others’ behavior or providing an opportunity for the person to set a good example.

22. Prompt self-talk

Encourage the use of self-instruction and self-encouragement (aloud or silently) to support action.

23. Relapse prevention

Following initial change, identify situations likely to result in re-adopting risk behaviors or failing to maintain new behaviors and help the person to plan to avoid or manage these situations.

24. Stress management

May involve a variety of specific techniques (e.g., progressive relaxation) that do not target the behavior but seek to reduce anxiety and stress. (Continued)

498

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Interdisciplinary Issues

TABLE 34.7 Behavior Change Techniques Effective in Health Interventionsdcont’d Technique

Description

25. Motivational interviewing

Prompting the person to provide self-motivating statements and evaluations of his or her own behavior to minimize resistance to change.

26. Time management

Helping the person make time for the behavior (e.g., fit it into a daily schedule).

Source: Abraham and Michie (2008).

no consensus among researchers in transport psychology regarding the best theoretical framework to explain or inform change in travel mode choice (Anable, Lane, & Kelay, 2006; Darnton, 2008). Accounts have been proposed using the norm activation model (Hunecke, Blobaum, Matthies, & Hoger, 2001), theory of planned behavior (Bamberg, Ajzen, & Schmidt, 2003; Heath & Gifford, 2002), grounded theory (Gardner & Abraham, 2007), the model of action phases (Bamberg, 2007), and the transtheoretical model (Gatersleben & Appleton (2007). The MaxSem model (Carreno, Bamberg, & Rye, 2009), shown in Figure 34.5, attempts to combine a stage approach with social psychology constructs of the kind listed in Table 34.7, such as goal intention (technique 4), behavioral intention (technique 10), and implementation intention (technique 16) (Bamberg, 2000). Each construct is hypothesized to be at its most applicable at a particular stage in the change process and, indeed, its application at other points may be otiose or worse. Thus, the model seeks to identify which vectors will press the person toward

forming a goal intention at the precontemplation stage; toward forming a behavioral intention at the contemplation stage; toward agreeing to an implementation intention at the preparation/testing stage; and preventing relapse (Table 34.7, technique 23) during the establishment of a new, more sustainable, travel habit at the maintenance stage.

9. CONCLUSIONS This chapter discussed in some depth travel mode choices. Travel mode choices vary with person characteristics such as age, gender, and disability; with household characteristics such as income, location, and transport availability; with journey purpose; and with attitude/value clusters. They also vary with environment characteristics, such as land use; the location of trip origins, such as homes; and trip destinations, such as jobs, shops, and recreations. Travel links the places where people go to lead their lives and meet their obligations (Stradling, 2002a, 2002b; Stradling, Meadows, et al., 2000).

Emotion anticipated with goal progress

Salient social norms

Felt obligation to “fulfill” personal standards

Goal intention

Behavioral intention

Implementation intention

New habit

Negative affect

Perceived responsibility Perceived negative consequences of own behavior

Pre-contemplation stage

FIGURE 34.5

Perceived goal feasibility

Perceived behavioral control over different behavioral change strategies Attitude towards different behavioral change strategies

Contemplation stage

Cognitive planning abilities

Skills to resist temptation

Skills to cope with implementation problems

Skills to recover from relapse

Preparation/testing stage

MaxSem model of travel mode change. Source: Carreno et al. (2009).

Maintenance stage

Chapter | 34

Travel Mode Choice

499

The UK data show high levels of public concern; reasonably accurate knowledge of which transport modes are most to blame; evidence, following the pioneering work of Linda Steg and colleagues (Steg, 2004; Steg & Gatersleben, 2000; Steg, Geurs, et al., 2001a, 2001b; Steg & Tertoolen, 1999; Steg & Uneken, 2002; Steg, Vlek, et al., 2001), of the importance of affect in travel mode choice; different psychological satisfactions (as well as different risks) associated with different motorized modes; and differentiation among different segments of the population defined by their attitudes toward car use and the environment. Although there are barriers to change, with cardependent places, car-dependent trips, and car-dependent people requiring different, detailed remediations from “hard” engineering and infrastructure measures (e.g., building more dedicated cycle lanes to enhance the opportunities for cycling) to “soft” psychological measures (e.g., segmenting citizens by their inclination for change and designing targeted persuasions), there are prospects for demand-side reduction in car use in the United Kingdom. There seems to be a readiness for change. Evidence from elsewhere is less heartening. Although many of the world’s population have long held the inclination to meet their travel obligations by car but have been unable to afford to do so, increasing affluence in developing countries means that many more now have the opportunity to own and drive automobiles. The ACNielsen Car Aspiration Index for 2004 (ACNielsen, 2005) showed large countries (e.g., China, India, and the Philippines) with currently low levels of motorization but high levels of aspiration toward car ownership. Impact equals population multiplied by consumption (Figure 34.6). The global impact of GHG emissions from fossil fuel-powered motorized vehicles is the product of the number of such vehicles (population) multiplied by the average GHG emission rate (consumption). Although supply-side changes such as increased engine and fuel efficiency are reducing the unit rate of consumption of fossil fuels and emission of GHGs, these are undermined by rebound effects that offset carbon gains, and the size of the worldwide Impact = Population x Consumption Global GHG emissions from fossil fuel powered motorized vehicles Growing inexorably Faster than efficiency gains

Rebound effects

= Population of vehicles x Average GHG Emission rate Engine efficiency gains

FIGURE 34.6 Global GHG emissions from fossil fuel-powered vehicles

fossil-fueled vehicle fleet is increasing inexorably. This is why the challenge to transport psychology to help the world burn less fossil fuel, and soon, is a demanding one.

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Chapter 35

Road Use Behavior in Sub-Saharan Africa Karl Peltzer Human Sciences Research Council, Pretoria, South Africa, and University of the Free State, Bloemfontein, South Africa

1. INTRODUCTION

2. METHOD

The World Health Organization (WHO, 2007) predicted that by 2020, road traffic injuries will rank as high as third among causes of disability-adjusted life years lost. The road traffic injury mortality rate is highest in Africa (28.3 per 100,000 population when corrected for underreporting, compared with 11.0 in Europe) (Peden et al., 2004). African countries from the WHO AFRO region with available data on road mortality and number of vehicles in use are shown in Table 35.1. The rate of road traffic deaths in AFRO is 40% higher than that in all other low- and middle-income countries (28.3 compared to 20.2 per 100,000) and 50% higher than the world level (28.3 compared to 19.0 deaths per 100,000 population) (Peden et al., 2004; WHO, 2010), making traffic injuries the 10th leading cause of death in the region (WHO, 2010). When comparing deaths per 10,000 vehicles, the contrast appears even starker, with 1.7 deaths per 10,000 vehicles in highincome countries throughout the world and more than 50 in most low-income African countries. The number of vehicles per inhabitant is less than 1 licensed vehicle per 100 inhabitants in most low-income African countries versus 60 in high-income countries (Lagarde, 2007). Fleet growth leads to increased road insecurity in developing countries. This explains, for example, the reported 400% increase in road deaths in Nigeria between the 1960s and the 1980s (Oluwasanmi, 1993). In addition, in Africa, it has been estimated that 59,000 people lost their lives in road traffic crashes in 1990 and that by 2020 the number of deaths will be 144,000, a 144% increase. The same model predicts a 27% reduction in high-income countries from 2000 to 2020 (Kopits & Cropper, 2005; Peden, 2005) (Table 35.2). The aim of this review was to study (1) road use behavior, (2) causes of road traffic accidents, (3) behavioral factors, and (4) interventions aimed at prevention of road traffic injuries in sub-Saharan Africa. This information provides guidance for future work in sub-Saharan Africa and perhaps information to assist in countermeasure development to reduce the increasing rates of crashes and fatalities in the region.

For this review, studies of road use behavior in sub-Saharan Africa were identified from electronic databases (Cochrane databases, MEDLINE, Embase, and CINAHL) and by hand-searching peer-reviewed journals in the injury and transportation field (specifically, Injury Prevention, Injury, Journal of Trauma, Accident Analysis and Prevention, International Journal of Injury Control and Safety Promotion, Injury Control and Safety Promotion, Traffic Injury Prevention, Journal of Crash Prevention and Injury Control, Transportation Research Part F: Traffic Psychology and Behavior, African Safety Promotion, and African Journal of Trauma). Relevant studies were also found in the 2004 “World Report on Road Traffic Injury Prevention” (WHO, 2004), in bibliographies of contentspecific articles and reports, and from sources of information identified by the authors, colleagues, and partners in the Road Traffic Research Network. A combination of keywords were used, including driver, pedestrian, motorcyclist, cyclist, culture of driving, driver’s perception of speed, dimensions of aberrant driver behavior, drivers’ emotions, speeding, drink-driving, and risky driving behavior. Country names were added to these terms and further searches conducted. Abstracts of selected articles were reviewed, and full texts of those that provided road use behavior were obtained. Additional publications were identified from the reference list of selected articles. Articles published in English and French, between 1980 and 2008, were included in this review. The choice of articles was restricted to those with information on sub-Saharan Africa and with relevant information on road use behavior. A search for accessible web-based literature on road use behavior in the region was also conducted through the search engine Google and relevant websites such as SafetyLit: Injury Research and Prevention Literature Update (www.safetylit.org), Transport Research Laboratory (http:// www.trl.co.uk), the U.S. Department of Transportation (http://trisonline.bts.gov/search.cfm), the World Health Organization (www.who.int), the World Bank (www. worldbank.org), Global Road Safety Partnership (http://www.grsproadsafety.org), Road Traffic Injuries

Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10035-9 Copyright Ó 2011 Elsevier Inc. All rights reserved.

503

504

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TABLE 35.1 Traffic Fatality Indicators in Sub-Saharan Africa (WHO AFRO Region) Gross National Income per Capita for 2007 ($)

Deaths per 100,000 population

Deaths per 10,000 Vehicles

Income Level

Country

Low income

Burundi

110

23.4

649.0

Zimbabwe

131

27.5

23.6

Democratic Republic of the Congo

140

32.2

649.0

Liberia

150

32.9

1122.7

Guinea Bissau

200

34.4

100.9

Ethiopia

220

35.0

1193.2

Eritrea

230

48.4

383.2

Malawi

250

26.0

278.0

Sierra Leone

260

28.3

425.9

Niger

280

37.7

704.9

Gambia

320

36.6

434.0

Madagascar

320

33.7

335.6

Mozambique

320

34.7

287.3

Rwanda

320

31.6

504.4

Uganda

340

24.7

209.7

Togo

360

28.1

385.6

Tanzania

400

34.3

240.2

Burkina Faso

430

31.1

89.2

Mali

500

32.1

237.1

Chad

540

34.3

298.1

Benin

570

31.1

126.2

Ghana

590

29.6

74.6

Kenya

680

34.4

37.5

Zambia

800

27.5

137.7

Senegal

820

32.5

143.2

Mauritania

840

35.5

31.7

Nigeria

930

32.3

63.0

Cameroon

1050

28.1

166.9

Congo

1540

28.8

108.4

Angola

2560

37.7

95.8

Swaziland

2580

22.8

29.8

Namibia

3360

16.9

20.4

Mauritius

5450

11.1

4.2

South Africa

5760

26.8

18.6

Botswana

5840

33.8

21.6

10.8

1.7

Middle income

High income Source: WHO (2010).

Non-Africa

Chapter | 35

Road Use Behavior in Sub-Saharan Africa

TABLE 35.2 Predicted Road Traffic Fatalities by Region (in Thousands), 2000e2020 World Bank Region

2000

2020

% Change, 2000 to 2020

South Asia (7)*

135

330

144

Sub-Saharan Africa (46)

46

80

80

East Asia and Pacific (15)

188

337

79

Middle East and North Africa (13)

56

94

68

Latin America and Caribbean (31)

122

180

48

East Europe and Central Asia (9)

32

38

19

613

1124

83

110

80

-27

723

1204

67

Subtotal (121) High-income countries (35) Global total (156)

Source: Kopits and Cropper (2005). *Number of countries surveyed indicated in parentheses.

Research Network (www.rtirn.net), and DFID Transport Links (http://www.transport-links.org/transport_links/ index.asp).

3. RESULTS The review is described under four major topics: (1) road use behavior (road user type, vehicle type, and social characteristics), (2) causes of road traffic accidents, (3) behavioral factors (excessive speeding and driver negligence, alcohol and drug use, information/knowledge, driver fatigue, stress and aggression, and seat belt and helmet use), and (4) intervention.

505

Most pedestrians and cyclists take shorter and easier paths, even if this is less safe (Peden et al., 2004). Studies in Brazil, Mexico, and Uganda found that pedestrians would rather cross a dangerous road than go out of their way to take pedestrian bridges (Forjuoh, 2003; Mutto, Kobusingye, & Lett, 2002).

3.1.2. Vehicle Type Various studies show higher crash involvement of buses, minibuses, lorries, and trucks than motor cars. For example, in South Africa in 2006 and 2007, buses were involved in more than 100 vehicle crashes per 10,000 registered vehicles. The next most frequent group was minibuses with 60, followed by truck (58) and motorcars (18) (Road Traffic Management Cooperation, 2008). The average number of fatalities per crash per vehicle type in South Africa in 2003 was 1.31 for buses, followed by minibus taxis (1.23), minibuses (1.10), motorcycles (1.00), light delivery vehicles/bakkies (0.97), motorcars (0.96), and trucks (0.61) (Road Traffic Management Cooperation, 2008). Passengers in formal and informal modes of public and mass transportation constitute another important road user group that is a common feature in road crash data from less resourced environments, particularly Africa (Afukaar, Antwi, & Ofosu-Amah, 2003; Odero, Khayesi, & Heda, 2003; Roma˜o et al., 2003). Vehicles associated with road crashes are typically privately owned buses, minibuses, converted pickup trucks, and taxis. The postulated risk factors include driver fatigue and other forms of risky driving, overcrowding of vehicles, poor conditions of vehicles, and poor road networks. Pedestrians are often doubly exposed to high risks of injury because they are the most likely to also use these forms of transport (Ameratunga, Hijar, & Norton, 2006). Afukaar (2001) found that fatal pedestrian crashes in Ghana mostly involved pedestrians in collisions with cars or taxis (37.8%). Although heavy goods vehicles were only involved in 18.6% of all the fatal pedestrian crashes, they caused 42% of the pedestrian fatalities (Table 35.4).

3.1. Road Use Behavior 3.1.1. Road User Type Based on data from 24 studies, pedestrians are the most frequently injured road users in Africa. In almost all countries, pedestrians have the largest share of road traffic fatalities, with more than 40%; only Botswana (29%) and Zimbabwe (31%) have lower pedestrian fatalities. Passengers represent the second largest group with road traffic fatalities, with more than 30% of the fatalities in most countries. Drivers account for a small share of fatalitiesdless than 10% in most countries; only Botswana, South Africa, and Zimbabwe have a larger share of driver fatalities of more than 20% (Table 35.3).

3.1.3. Social Characteristics of Road Users 3.1.3.1. Sex and Age As in other developing countries, males are overinvolved in road traffic crashes and account for more than 67% of those killed (Odero, Garner, & Zwi, 1997). This can partly be explained by their greater exposure to traffic as drivers and as frequent travelers in motor vehicles for work and leisure activities. Females are involved mainly as passengers and pedestrians. The TRL global fatality study found females to rarely account for more than 25e30% of road casualties in developing countries. Ethiopia reported a relatively high female casualty involvement rate (34%), whereas in

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PART | VI

Interdisciplinary Issues

TABLE 35.3 Percentage of Traffic Fatalities by Country, Source, and Class of Road User Class of Road User

Country

Reference

Pedestrian

Passenger

Driver

Motorcyclist/ Bicyclist

Botswana (1998)

Jacobs and Aeron-Thomas (2000)

29

46

23

2

Ethiopia (1997e1998)

Jacobs and Aeron-Thomas (2000)

51

53

5

2

Ghana (1989e1991)

Gorell (1997)

67

Ghana (1994e1998)

Afukaar et al. (2003)

46.2

Car occupants ¼ 10Bus/ minibus/truck/pickup occupants ¼ 46.3

5.7

Kenya

Odero (1995)

42

38

12

8

Kenya (1998)

Jacobs and Aeron-Thomas (2000)

43

11

9

37

Kenya

Odero et al. (2003)

80.0

Mozambique

Roma˜o et al. (2003)

55

South Africa (1998)

Jacobs and Aeron-Thomas (2000)

38

37

30

d

South Africa

Butchart et al. (2001)

41.9

14.6

13.1

2.2

South Africa

Road Traffic Management Cooperation (2008)

38.5

32.2

22.3

South Africa

Road Traffic Management Cooperation (2008)

36.0

34.4

22.3

Tanzania (1997)

Jacobs and Aeron-Thomas (2000)

41

36

6

16

Tanzania, three districts

Moshiro et al. (2001)

45, 37, and 61

Zambia (1996)

Jacobs and Aeron-Thomas (2000)

46

38

8

8

Zimbabwe (1998)

Jacobs and Aeron-Thomas (2000)

31

37

25

7

TABLE 35.4 Frequency of Involvement of Different Vehicles in Pedestrian Fatalities in Ghana, 1998e2000 % involvement in fatal crashes

Fatalities per 100 crashes involving the vehicle type

Heavy goods vehicles

18.6

42.0

Buses/minibuses

31.8

22.2

Pickup trucks

7.6

19.5

Motorcycles

2.1

12.1

Cars/taxis

37.8

11.4

Bicycles

0.8

2.5

Other

1.3

23.5

Source: Afukaar (2001).

Zimbabwe, females represented 14% of road fatalities (1998) and this rate was confirmed by hospital studies (Jacobs & Aeron-Thomas, 2000). Females tend to have a higher pedestrian involvement rate; Botswana reported females accounting for as high as one-third of all pedestrian fatalities and 43% of all pedestrian casualties. Only 6% of driver casualties were females in Ethiopia (Jacobs & Aeron-Thomas, 2000). More than 75% of road traffic casualties in Africa are in the economic productive age group of 16e65 years. Those older than 65 years account for a small proportion of road casualties, partly due to their small numbers in the general population. Children are often injured as pedestrians; for example, up to 30% of Botswana’s pedestrian casualties were younger than 16 years (Jacobs & Aeron-Thomas, 2000). 3.1.3.2. Socioeconomic Status Several studies (Evans & Brown, 2003; Nantulya & MuliMusiime, 2001; Nantulya & Reich, 2002; Odero et al., 2003)

Chapter | 35

Road Use Behavior in Sub-Saharan Africa

have shown that people from less privileged socioeconomic groups are at greater risk of injury from all causes, including road crashes. In the case of road crashes, the explanation may lie in their greater exposure to risk (Peden et al., 2004). A 2002 study in Kenya (Odero et al., 2003), for example, found that 27% of commuters with no formal education traveled on foot, 55% used buses or minibuses, and only 8% used private cars. By contrast, 81% of people with a secondary-level education traveled in private cars, 19% used buses, and none walked. Where people live can also influence their exposure to road traffic risk. In general, people living in urban areas are at greater risk of being involved in road crashes, but people living in rural areas are more likely to be killed or seriously injured if they are involved in crashes. One reason is that motor vehicles tend to travel faster in rural areas. In many low- and middle-income countries, many people are exposed to new risks when new highways are built through their communities (Nantulya et al., 2003). Very few countries monitor the income level or occupation status of their road casualties. A practical assumption is that although not all pedestrians are poor, the poor will be pedestrians. The DFID-funded Pedestrian Vulnerability/Accidents Study surveyed both pedestrian victims and pedestrians (as a control group) to ascertain the associated socioeconomic characteristics. The share of the lowest income group, both personal and household, from two cities in Africa shows that pedestrian victims were reported to be much poorer than the control sample in Accra and in Harare (Jacobs & Aeron-Thomas, 2000).

3.2. Causes of Road Traffic Accidents Reports from various countries (Kenya, Uganda, Ethiopia, Tanzania, Ghana, South Africa, and Zimbabwe) show that most of the road crashes are largely due to a range of human error, road, and vehicle factors that include (1) speeding and perilous overtaking, (2) alcohol and drug abuse, (3) driver negligence and poor driving standards, (4) vehicle overload, (5) poor maintenance of vehicles, (6) bad roads and hilly terrain, (7) negligence of pedestrians, and (8) distraction of drivers (e.g., speaking on cell phones). According to Odero (1995), in Kenya, behavioral factors were responsible for 85% of all causes. Vehicleepedestrian collisions were most severe and had the highest case fatality rate of 24%, whereas only 12% of injuries resulting from vehicleevehicle accidents were fatal. Utility vehicles, “matatus” (minibuses), and buses were involved in 62% of the injury-producing accidents (Odero, 1995). Studies from police reports in South Africa, Tanzania, and Nigeria also show behavioral factors followed by road environment and vehicle factors as major causes of road traffic accidents (Table 35.5).

507

Causes of motor vehicle crashes are multifactorial and involve the interaction of a number of precrash factors that include people, vehicles, and the road environment. Human error is estimated to account for between 64 and 95% of all causes of traffic crashes in developing countries (Petridou & Moustaki, 2000). Dagona and Best (1996) identified human, vehicular, and road environmental circumstances as the most important causal factors for road traffic accidents in Nigeria. A high prevalence of old vehicles that often carry many more people than they are designed to carry, lack of safety belt and helmet use, poor road design and maintenance, and the traffic mix on roads are other factors that contribute to the high rate of crashes in less developed countries (Odero et al., 1997). Forjuoh, Zwi, and Mock (1998) note that the main causes of road traffic crashes are speeding, overloading, non-observation of traffic rules, lack of effective and continued law enforcement, nonstandardized methods of issuing drivers’ licenses and administering driving tests, poor road design and maintenance, and alcohol and drug abuse.

3.2.1. Risk Perception African public risk perception of road traffic injury needs to be understood in the context of culture in order to apply interventions that have proven successful elsewhere (Lagarde, 2007). One of the possible sources of bias in judgments concerning risk and accidents may be found in culture. Cultural influences may contribute to a large extent, as Kouabenan (1998) noted on the basis of results from the Ivory Coast (West Africa) that professional drivers expressed an especially high degree of fatalistic beliefs. Many drivers share deep-routed mystical and fatalistic attitudes that may lead to systematic errors in the appraisal of risks and possible causes of road traffic accidents. In a South African study, Peltzer (2002) found fatalistic beliefs in 16% of black and 21% of white drivers, and there was a significant relationship between a nonfatalistic attitude and seat belt use. Peltzer and Renner (2003) found that South African taxi drivers had largely fatalistic attitudes and expressed a high degree of risktaking behavior toward road traffic accidents. In the cultural context of South Africa, a high degree of superstitious beliefs among drivers should be considered. For example, I think that witchcraft is one of the factors that causes accidents on our roads. You may find that I buy a new car and my neighbors are not happy about that, they are going to bewitch me so that my car gets destroyed and may even kill me. Sometimes you may find some stick (dikotana) in the morning in the car and you get an accident the following day, it shows that they worked those sticks so that you get involved in an accident. There are times when a driver can cause an accident claiming he sees a cow in front but passengers do not see that. Or a fly will just enter into the vehicle and even if

508

PART | VI

Interdisciplinary Issues

TABLE 35.5 Major Causes of Road Traffic Accidents Department of Transport (2001)

Road Crashes and DeathsdKey Contributory Factors, South Africa

Driver

80e90%

Vehicle

10e30%

Road environment

5e15%

Nzegwu and Nzegwu (2007)

Causes of Road Traffic Accidents in Benin City, Nigeria, August 2003 to July 2004 Reason

Morbidity (%)

Mortality (%)

Behavioral factor

Alcohol influence Burst tires only Traffic rule violations Speeding

23.3 28.3 31.1 3.1

53.9 23 7.7 0

Vehicle condition

Brake failure

1.4

0

Environment

Poor road conditions

7.1

7.7

Barengo, Mkamba, Mshana, and Miettola (2006)

Causes for Accidents (N [ 5985) in the Dar-es-Salam Region, Tanzania, 2001 Reason

%

Behavioral factor

Speeding Careless driving Overtaking Crossing pedestrian/cyclist Intoxication

25.1 20.1 12.0 12.0 5.0

Vehicles and equipment

Mechanical defect

14.0

Environment

Bad road conditions Stray animal

6.7 4.4

you try to kill it (by Doom and others) it will not die. It will go to the driver and start flying in his eyes and an accident may occur. (Passenger; Peltzer & Mashego, 2003, p. 36)

˚ strøm, Moshiro, Hemed, Heuch, and Kvale (2006) A found that consistent with the acknowledgment that perception of risk frequently shows little correspondence to epidemiological injury statistics, the risk perceptions of Tanzanian adults did differ from known actual risk estimates. Although men rated their road traffic vulnerability as similar to that of women, men were at higher risk than women for experiencing such injuries according to injury morbidity rates of Dar es Salaam and Hai districts (Moshiro et al., 2005). A plausible explanation for this inaccuracy is that men and women respectively underestimate and overestimate their personal risk compared to the actual risk that they face. It is evident that whereas girls are more inclined than boys to judge risks as being likely, boys are more optimistic about avoiding injuries and view injuries as occurring due to misfortune (Millstein & Halpern-Felsher, 2002). Museru, Leshabari, and Mbembati (2002) studied the patterns of road traffic injuries and associated factors among

school-aged children (N ¼ 286) in Dar Es Salaam, Tanzania: 52% were primary schoolchildren and 23.4% were preschool children. More than 95% of the injured children were pedestrians. Almost one-third of the victims and 36% of guardians were unaware of safer walking on the road, such as walking along the streets relative to oncoming vehicles and walking across the roads. Parents or guardians perceived the risk of road traffic injuries as low, and two-thirds thought that collisions to children could not be prevented. Vanlaar and Yannis (2006) studied perception of road accident causes in 23 European countries (N ¼ 24,372) and found high perceived risk to be associated with high perceived prevalence driver behavior (taking drugs and driving, drinking and driving, and taking medicines and driving). Perceived high risk was also associated with low perceived prevalence vehicle characteristics (defective steering, poor brakes, bald tires, and faulty lights). High perceived prevalence was found to be associated with low perceived risk driver behavior (driving when tired, driving too fast, following a vehicle too closely, and using a handheld mobile phone), and low perceived prevalence was associated with low-risk road characteristics (bad weather

Chapter | 35

Road Use Behavior in Sub-Saharan Africa

509

TABLE 35.6 Perceived Causes of Road Traffic Accidents, Rated by Rank Order (1 ¼ Highest) Environment (Road Signs, Road Layout, Speed Limits, and Pedestrian Facilities)*

Vehicles and Equipment (Roadworthiness, Lighting, Braking, Handling, and Speed Management)*

Response Format

Human (Information, Attitudes, Impairment, and Police Enforcement)*

Mock, Amegeshi, Ghana et al. (1999); Mock, Forjuoh, and Rivara (1999)

Structured

1

Kouabenan (2002)

Ivory Coast

Structured

3

1

2

Abiero-Gariy (2007)

Kenya

Structured

1

2

3

Ndwigah (2003)

Kenya, drivers (N ¼ 200)

Structured

1

3

2

Roma˜o et al. (2003)

Mozambique

Structured

1

2

Butchart, Kruger, and Lekoba (2000)

South Africa

Open-ended

1

2

3

Peltzer and Mashego (2003)

South Africa

Open-ended

1

3

2

Peltzer and Renner (2003)

South Africa

Structured

1

2

3

A˚strøm et al. (2006)

Tanzania

Open-ended

1

2

3

Kobusingye, Hyder, and Ali (2006)

Uganda

Structured

1

Vanlaar and Yannis (2006)

23 European countries

Structured

1

3

2

References

Country

2

*The Haddon matrix as applied to road traffic injuries (phase, precrash) (Haddon, 1980).

conditions, poorly maintained roads, and traffic congestion). Table 35.6 summarizes perceived causes of road traffic accidents from different studies; most studies in African countries rate the behavioral factor as the highest contributor to road traffic accidents, followed by environmental and vehicle factors. In the large study including 23 European countries and in two studies in Africa, the vehicle factors were rated second and the environmental factors third.

speeds and increased risk of crash and injury, both for motor vehicle occupants and for vulnerable road users, particularly pedestrians (Norton, Hyder, Bishai, & Peden, 2004). This relationship is likely to be true for African countries. Indeed, data obtained from routinely collected police reports in a number of African countries show that speed is the leading cause of road traffic crashes, accounting for up to 50% of all crashes (Afukaar, 2003; Department of Transport, 2001; Muhlrad, 1987) (Table 35.7).

3.3. Behavioral Factors: Road User (Information, Attitudes, Impairment, and Police Enforcement)

3.3.2. Alcohol and Drug Use

3.3.1. Excessive Speeding Studies undertaken primarily in high-income countries show a strong relationship between an increase in vehicle

Several caseecontrol studies in high-income countries found an association between alcohol and increased risk of road crashes (Peden et al., 2004). Studies in African countries showed that drivers had consumed alcohol in 33e69% of crashes in which drivers were fatally injured (Butchart et al., 2001; National Injury Mortality

510

PART | VI

TABLE 35.7 Speeding Reference

Country

Excessive Speeding

Afukaar (2003)

Ghana

50% of all Ghanaian road traffic crashes between 1998 and 2000

Derry, Donkor, and Mock (2007)

Ghana

95% of vehicles traveled above posted speed limit of 50 km/h

Department of Transport (2001)

South Africa

30% of all crashes and approximately 50% in the case of commercial freight and public passenger vehicles

Department of Transport (2003)

South Africa

Percentage exceeding speed limit in urban areas Light vehicles: 61% Minibus taxi: 59% Truck: 31%

Sukhai, Seedat, Jordaan, and Jackson (2005)

South Africa, Durban

Drive above speed limit (52.6%) Speed through yellow light or drive through red light (47.6%)

Surveillance System (NIMSS), 2002e2005) and in 8e60% of crashes in which drivers were not fatally injured (Odero, 1998; Odero & Zwi, 1997; Peden, Van der Spuy, Smith, & Bautz, 2000). Alcohol consumption by pedestrians also increases their risk of injuries in African countries; for example, in South Africa, almost 60% of fatally injured pedestrians had consumed alcohol (NIMSS, 2005). Hedden and Wannenburg (1994) found in a South African (Durban) hospital accident and emergency unit that among 530 injured drivers, passengers, and pedestrians, 52% had blood alcohol content (BAC) levels greater than 0.08 g/100 ml, 35% were cannabis positive, and 19% had alcohol and cannabis. Drivers were most commonly intoxicated with alcohol, whereas pedestrians were frequently intoxicated with both alcohol and cannabis (Table 35.8). Some contributing aspects of accidents involving drivers have been described by Bekibele et al. (2007); for example, drivers take alcohol and believe it provides a boost to driving or use nonprescribed amphetamines or kola nuts to stay awake without having any idea of the negative consequences. In South Africa, a series of roadside surveys carried out by De Jager (1988) between 1981 and 1985 found that, on average, 7% of drivers and 16% of pedestrians randomly selected from traffic and breath-tested had BAC levels higher than the legal limit of 0.08 g/100 ml. Research conducted by the Department of Transport in South Africa

Interdisciplinary Issues

found that the national daily average of persons driving under the influence of alcohol increased from 1.8% in 2002 to 2.1% in 2003 (Arrive Alive, 2005). Mock, Asiamah, and Amegashie (2001) breathalyzed 722 drivers stopped by the roadside in Accra: 21% of the drivers had detectable BAC, whereas 7.3% had BAC in excess of 0.08 g/100 ml. In Kenya, of the 479 drivers who were breath-tested during a roadside survey conducted by Odero and Zwi (1997), 19.9% were positive for alcohol, 8.3% had BAC in excess of 0.05 g/100 ml, and 4% exceeded the BAC level of 0.08 g/100 ml.

3.3.3. Information, Training, and Licensing Various studies have investigated the state of drivers’ licenses. For example, the Department of Transport (2003) in South Africa found that 16.5% of drivers of trucks, buses, and minibus taxis did not have professional driving permits. Amoran, Eme, Giwa, and Gbolahan (2005/2006) found that 38.8% of Nigerian commercial motorcyclists had no driver’s license (Table 35.9).

3.3.4. Driver Fatigue, Stress, and Aggression Other factors that increase the risk of road crashes in African countries include fatigue, stress, and aggression. Surveys of commercial and public road transport in a number of African countries have shown that drivers often work long hours and go to work exhausted (Mock, Amegeshi, & Darteh, 1999). For example, Maldonado, Mitchell, Taylor, and Driver (2002) found that falling asleep at the wheel has been implicated in 24% of heavy vehicle road accidents in South Africa. Surveys in low- and middle-income countries (Mock, Amegeshi, et al., 1999; Nafukho & Khayesi, 2002; Nantulya & Muli-Musiime, 2001) have revealed that transport company owners frequently force their drivers to work long hours, to work when exhausted, and to drive at excessive speeds. Marcus (1997) found that truck drivers in the KwaZulu-Natal Midlands (South Africa) work on average 16 h per day, and that approximately 70% spend 2½ days or less at home per month. Focus group discussions with commercial drivers in Ghana revealed that demands for increased returns (by transport owners) force drivers to speed and work when exhausted (Mock et al., 2001). Peltzer and Mashego (2003) found in qualitative research in South Africa that truck drivers especially drive long distances without sleep (when people are tired, they tend to be impatient and inconsiderate of other drivers) and feel pressure from employers to drive without rest. Moreover, excitement (e.g., among truck drivers who drive long distances, “they do not rest even if they feel tired”) and being angry (e.g., “they tend to drive their cars very fast”) were perceived by some participants to lead to an accident. Khoza and Potgieter

Chapter | 35

Road Use Behavior in Sub-Saharan Africa

511

TABLE 35.8 Alcohol-Related Road Traffic Deaths and Accidents Country

Reference

Driver

Pedestrian

Cyclist

All road users

56.7

Alcohol-related Road Traffic Deaths BAC positive (mean BAC) South Africa

Butchart et al. (2001)

54.9

33.3

3.1

South Africa

NIMSS (2002)

55.3 (0.17)

59.4 (0.22)

36.9 (0.2)

South Africa

NIMSS (2003)

58.2 (0.18)

60.8 (0.22)

40.0 (0.14)

South Africa

NIMSS (2004)

50.9 (0.17)

59.7 (0.21)

38.7 (0.15)

52.4 (0.19)

South Africa

NIMSS (2005)

53.5 (0.16)

58.7 (0.15)

45.0 (0.16)

51.8 (0.18)

Alcohol and Other Drug Use in Nonfatal Accident Victims (by Blood Analysis or Breath Test) N ¼ 530 52% BAC >0.08 g/100 ml 35% cannabis 19% alcohol and cannabis

South Africa

Hedden and Wannenburg (1994)

South Africa

Peden et al. (1996)

Kenya

Odero (1998)

n ¼ 25; 60%

n ¼ 30; 33.3%

South Africa

Peden et al. (2000)

n ¼ 44; 50.4%

n ¼ 14; 50.7%

South Africa

Peden et al. (2000)

N ¼ 196; 61.2% n ¼ 24; 8.3%

N ¼ 188 23.4% BAC positive 12.2% BAC 0.5 g/100 ml

N ¼ 281 32.5% cannabis 14.5% methaqualone (mandrax) 4.2% cocaine

BAC, blood alcohol content; NIMSS, National Injury Mortality Surveillance System.

(2005) found that professional drivers in South Africa drive offensively and are more involved in aggressive driving behavior (Table 35.10).

3.3.5. Lack of Seat Belt Use Studies on observed seat belt use reported non-seat belt use ranging from 99% among drivers in Kenya (Nantulya & Muli-Musiime, 2001) to approximately 50% in Nigeria (Iribhogbe & Osime, 2008; Sangowawa et al., 2006) and South Africa (Peltzer, 2003). Seat belt use was generally lower among passengers, particularly rear passengers, and child restraints were hardly used, as reported in one study in Nigeria (Sangowawa et al., 2006). Peltzer (2003) found that among drivers in South Africa, a nonfatalistic orientation (attribution of accidents more readily to factors in the driver’s control, such as excessive speeding) was significantly associated with observed seat belt use and selfreported seat belt use (Table 35.11). In studies of self-reported seat belt non-use, different use of measures also found low seat belt use of vehicle occupants (Table 35.12).

3.3.6. Lack of Helmet Use A significant factor for increased severity of injuries of users of motorized two-wheeled vehicles is riders’ failure to use helmets (Norton et al., 2004). Studies in a number of African countries have shown that failure to use helmets, use of nonstandard helmets, and use of improperly secured helmets are not uncommon, even in countries with mandatory helmet laws (Flisher et al., 1993) (Table 35.13). Failure to wear helmets is also a risk factor for increased injury severity among bicyclists (Norton et al., 2004).

3.4. Intervention Analysis of risk and intervention components of road user, vehicle, and road environment need to be seen in a systems framework, in which interactions among different components are taken into account (Peden et al., 2004). Intervention strategies, including those in some developing countries, aimed at improving road user behavior are increasingly focusing with success on the introduction and enforcement of relevant legislation such as increasing

512

PART | VI

TABLE 35.9 Information, Training, and Licensing Reference

Country

Traffic Violation

Ministry of Botswana Works (1992) cited in Oladiran and Pheko (1995)

36% of drivers killed in road accidents were unlicensed.

Flisher et al. (1993)

South Africa, high school students in the Cape Peninsula

Of those who had driven a vehicle, 63.2% reported driving without a license.

Abane (1994)

Ghana, Accra, 40% were issued drivers (N ¼ 107), a driver’s license 64.3% commercial drivers without a test. Roadside observations (N ¼ 302 vehicles) (478 individual offenses)

Department of Transport (2003)

South Africa, national

16.5% of drivers of trucks, buses, and minibus taxis without professional driving permit.

Ndwigah (2003)

Kenya, drivers (N ¼ 200)

13% did not have a driver’s license.

Nuntsu (2004)

South Africa, university student drivers (N ¼ 110) (60 male and 50 female, 17e24 years old)

89.2% had no driver’s license but drove anyway.

Amoran et al. Nigeria, commercial (2005/2006) motorcyclists (N ¼ 299), rural town, Igboora

38.8% had no driver’s license, and 75.9% were not able to produce a driver’s license on demand.

fines and suspending drivers’ licenses rather than on education efforts (Norton et al., 2004). In considering options for technology transfer to African countries, a careful evaluation of what might work in these settings is essential. What has been found effective in a high-income setting may not necessarily be effective in a low-income setting. In some instances, some modification or adaptation of the interventions may be required to maximize the likelihood of success in African countries (Forjuoh, 2003).

3.4.1. Speed Limits and Traffic Calming Speed limits must go hand in hand with strict enforcement of speed limits, as well as traffic-calming strategies such as speed bumps, speed humps, and speed strips (Forjuoh, 2003).

Interdisciplinary Issues

Speed control probably has the greatest potential to save lives. The key factor in the effectiveness of traffic regulations is the drivers’ perceptions that they run a high risk of being detected and punished for infractions (O’Neill & Mohan, 2002). Unfortunately, in Africa, the combination of a low enforcement level, frequent corruption of police officers, and low public awareness dooms any traffic regulation, including speed control, to failure. A report from Ghana illustrated how, in recognition of the shortcomings of enforcement, speed bumps and rumble strips were installed and reduced fatalities by 55% (Afukaar, 2003). It appears, however, that direct adoption of speed control measures proven effective in developed countries such as speed camera networks and speed calming (Richter, Berman, Friedman, & Ben-David, 2006) often will not produce the same safety improvement for African countries. Failure of law enforcement agents may deter speed violators “due to lack of resources for traffic police, bribery and corrupt practices, shortcomings of transport policies, weak political support for road traffic injury prevention and control, and low public awareness and participation in the adoption of speed control measures” (Afukaar, 2003, p. 82). It may be more appropriate and cost-effective for African countries to utilize speed control measures such as physical speeding restraints; for example, rumble strips and speed humps have been found to be effective on Ghanaian roads (Afukaar, 2003). Road safety authorities should take into account that speeding behavior frequently is not associated with expected situation-specific consequences, and especially in sub-Saharan Africa, accidents tend to be attributed to nonrational causes. Thus, safety campaigns emphasizing possible consequences of speeding are not expected to be effective. Rather, speeding per se as a generalized behavior should be addressed by enhanced traffic control and traffic policing measures (Renner & Peltzer, 2004).

3.4.2. Alcohol and Driving A large body of research, although little of it conducted in low- and middle-income countries, shows that road traffic injuries are reduced in varying magnitudes by setting and enforcing legal blood alcohol limits and minimum drinking age laws, using alcohol checkpoints, and running mass media campaigns aimed at reducing drinking and driving (Peden et al., 2004). Breath-testing devices that provide objective evidence of BAC are the most effective enforcement tool. Although they are used in most high-income countries, they are not used in most low- and middleincome countries. In any case, the deterrent effect of breath testing depends on the laws governing its use (Zaal, 1994). Police powers vary among jurisdictions. Widespread random breath testing, applied to at least 1 in 10 drivers every year, achieves the highest compliance with laws

Chapter | 35

Road Use Behavior in Sub-Saharan Africa

513

TABLE 35.10 Driver Fatigue, Stress, and Aggression Reference

Country

Study Measurement

Outcome

Booysen (1988)

South Africa, colored (mixed-race) bus drivers (N ¼ 199) The test group was divided into three subgroups according to involvement in accidents and the degree of seriousness of accidents.

Test battery, opinion questionnaire, Sixteen Personality Factor Questionnaire, the Picture Situation Test, an attitude scale to measure aggression,and an information processing test

Accident risk persons tended to be aggressive, impulsive.

Maldonado et al. (2002)

South Africa, male truck drivers (N ¼ 102)

Driving, sleep, and social habits with accident reports

58% drove at least 10.5 h per day. 58% drove more than 70 h (legal limit).

Sukhai et al. (2005)

South Africa, motorists (N ¼ 1006), Durban (from petrol stations)

Road rage and aggressive driving behaviors

Weave in traffic (20.6%) Drink and drive (11.4%) Carry a weapon when driving (7.0%) Become aggressive when drinking and driving (4.6%)

setting BAC limits. Enforcement should be unpredictable with regard to time and place so that drivers cannot avoid being tested (Elder et al., 2004). Enforcement is most effective at reducing the frequency of driving with BAC exceeding legal limits if it is accompanied by mass media campaigns that increase public perception of the risk of being caught, reduce public acceptance of drinking and driving, and increase public acceptance of enforcement (Elder et al., 2004).

3.4.3. Seat Belt Use Seat belts are useable as a traffic injury intervention in lowincome countries because they are affordable and their implementation is feasible (Stevenson et al., 2008). To derive the maximum benefit from seat belts, however, several stringent strategies are required, such as mandatory seat belt laws, public education on the benefits of seat belts, and legislation on the availability of functional seat belts in vehicles. Seat belt laws may be implemented through several strategies, including primary enforcement, whereby a law enforcement officer may stop a driver based solely on a safety seat belt violation, or secondary enforcement. In a secondary enforcement situation, an officer may only address a seat belt violation after stopping the driver for some other purpose. In African countries, enforcement of seat belt laws is lacking (Forjuoh, 2003).

3.4.4. Helmet Use Both bicycle and motorcycle helmets reduce head injuries among riders by up to 85%. Although education may be

effective in increasing helmet use, the effect is highest when combined with legislation and enforcement, as demonstrated in some developing countries (Norton et al., 2004). Helmets and mandatory helmet laws are clearly transferable to low-income countries. The acquisition of motorcycle helmets could be within the budgets of the people who can afford motorcycles in these countries

TABLE 35.11 Non-Use of Seat Belts Nonwearing of Seat Belt (Observed)

References

Country

Nantulya and Muli-Musiime (2001)

Kenya

99% of car occupants injured in crashes

Peltzer (2003)

South Africa

56% black and 50% white drivers

Department of Transport (2003); Olukoga and Mongezi (2005)

South Africa, rural roads

Driver unobserved ¼ 67.5% Driver roadblock ¼ 14.2% Front passenger roadblock ¼ 33.3% Back passenger roadblock ¼ 92.3%

Sangowawa et al. (2006)

Nigeria, 440 vehicles, urban, Ibadan

52% drivers 95.9% among children

Iribhogbe and Osime (2008)

Nigeria

47.7% drivers 81.6% front seat passengers 93.9% rear seat passengers

514

PART | VI

TABLE 35.12 Self-Reported Lack of Seat Belt Use Reference

Country

Nonwearing of Seat Belt, Self-Reported

Flisher et al. (1993)

South Africa, high school students in the Cape Peninsula

37.3% had failed to wear one on the last occasion when they were in the front seat of a vehicle.

Andrews, Uganda Kobusinggye, and Lett (1999)

No vehicle occupants were using safety belts.

Reddy et al. (2003)

85.7% when driven by others. Among those who had driven a vehicle, 78.6% of drivers not always wore a seat belt when driving.

Flisher et al. (2006)

South Africa, schoolchildren

South Africa, schoolchildren

TABLE 35.13 Lack of Motorcycle Helmet Use Reference

Country, Sample

Non-Helmet Use

Asogwa (1980)

Nigeria, motorcyclists

8%

Flisher et al. (1993)

South Africa, schoolchildren

Of those who had been on a motorcycle, 47.9% reported riding without a helmet.

Amoran et al. (2005/2006)

Nigeria, commercial motorcyclists (N ¼ 299)

100%

Flisher et al. (2006)

South Africa, schoolchildren (grades 8, 9, and 11) urban, Cape Town, Port Elizabeth, Mankweng, Durban

18.9%.

Oginni, Ugboko, and Adewole (2007)

Nigeria commercial motorcyclists (N ¼ 224), Lagos and Ile-Ife

82.4%

Oluwadiya et al. (2009)

363 motorcycle road traffic injury patients in tertiary hospitals in southwest Nigeria

96.5%

52.8% in the front seat of a motor vehicle without a seat belt.

(Forjuoh, 2003). The full benefit from helmet use increases when combined with other proven or promising interventions or strategies, such as construction of bicycle paths or lanes, bicycle safety programs, bicycle skills training programs, and conspicuity-enhancement measures (e.g., in some cultures, it is culturally not appropriate to wear a helmet). Barriers to implementation include attitudes (uncomfortable and hot) and possibly the high cost of helmets (Forjuoh, 2003).

3.4.5. Protection of Vulnerable Road Users Nantulya and Reich (2002) describe strategies to prevent and protect pedestrians, passengers who ride crash-prone buses and minibuses, and bicyclists because these constitute more than 80% of vulnerable road users. Pedestrians should be separated from vehicles through provision of pedestrian walkways, safe pedestrian crossings, and trafficcalming measures. Public awareness raising and participatory approaches are needed to implement, for example, overpasses for pedestrians and participatory and targeted public educational programs. Passengers who ride on accident-prone buses, minibuses, and trucks can be protected by regulating the industry and integrating it into a safe and organized part of the transport system for the use by the public. For example, if the remuneration system used by the private owners of buses or minibuses leads drivers to overwork without rest, driver and passengers could be placed at risk. Passenger safety can be enhanced through protection of the labor rights of bus and minibus drivers to job security, by regulating the working hours for drivers of

Interdisciplinary Issues

buses and minibuses, and by speed regulation through the use of speed governors. Table 35.14 summarizes interventions to make people safer according to proven evidence and applicability to African countries.

4. SUMMARY Barriers to road safety implementation in low-income countries are summarized as follows (Forjuoh, 2003; WHO, 2010): l l l l

l l l l

Economy (low for safety measures) Political attitudes Cultural beliefs (injuries are part of destiny) Low literacy rates (public education and comprehension of traffic signs) Competing health problems (HIV and tuberculosis) Traffic mix (motorized, nonmotorized, and animals) Lack of data-collection systems Lack of research on and implementation of programs for the prevention of road traffic injuries

Ribbens (2003, p. 29) proposed the following measures to promote safety in African countries: l

Increase political will and visionary leadership to initiate appropriate road safety policies and strategies.

Chapter | 35

Road Use Behavior in Sub-Saharan Africa

TABLE 35.14 Interventions to Make People Safer Prevention Target

l

l l

l

l

l

partnership development between the government and private sector; vulnerable road user business plan that focuses on identifying hazardous locations, countermeasures, and implementation costs; and dedicated funding to address the vulnerable road user problem.

Proven

Occupant

Seat belt* Air bags Child safety seats Seat belt use laws Child seat use laws

Affordable/feasible Combined strategy (laws, public education) Lack of enforcement (primary and secondary)

Motorcyclist

Helmets*

Affordable/feasible

REFERENCES

Bicyclist

Helmets*

Readily usable Combined with other strategies Policies? Barriers (attitudes/costs)

Pedestrian

Sidewalks Roadway barriers* Pedestrian crossing signs* Education on conspicuityenhancement measures Separating from vehicles

Feasible Combined with public education

Cross-cutting

Speed limits* Speed ramps/bumps Alcohol sobriety checkpoints Lower BAC laws Minimum drinking age laws

Useable Need strict enforcement and other traffic-calming strategies

Abane, A. M. (1994). Driver behaviour and city traffic: Empirical observations from Acrra, Ghana. Research Review, 10(1/2), 1e13. Abiero-Gariy, Z. (2007). The role of speed control in prevention of road traffic crashes: Experience from Kenya. Paper presented at the Africa Road Safety Conference, Accra, Ghana, February 5-7, 2007. Afukaar, F. K. (2001). The characteristics of pedestrian accidents in Ghana. Bi-Annual Journal of Building & Road Research Institute, 7, 1e5. Afukaar, F. K. (2003). Speed control in developing countries: Issues, challenges and opportunities in reducing road traffic injuries. Injury Control and Safety Promotion, 10, 77e81. Afukaar, F. K., Antwi, P., & Ofosu-Amah, S. (2003). Pattern of road traffic injuries in Ghana: Implications for control. Injury Control and Safety Promotion, 10, 69e76. Ameratunga, S., Hijar, M., & Norton, R. (2006). Road-traffic injuries: Confronting disparities to address a global-health problem. Lancet, 367(6), 1533e1539. Amoran, O. E., Eme, O., Giwa, O. A., & Gbolahan, O. B. (2005/2006). Road safety practices among commercial motorcyclists in a rural town in Nigeria: Implications for health education. International Quarterly of Community Health Education, 24(1), 55e64. Andrews, C. N., Kobusinggye, O. C., & Lett, R. (1999). Road traffic accident injuries in Kampala. East African Medical Journal, 76(4), 189e194. Arrive Alive. (2005). Drinking and driving reaches alarming proportions. Retrieved from. http://www.arrivealive.co. Accessed August 30, 2005. Asogwa, S. E. (1980). The crash helmet legislation in Nigeria: Before and after study. Accident Analysis and Prevention, 12, 213e216. ˚ strøm, A. N., Moshiro, C., Hemed, Y., Heuch, I., & Kvale, G. (2006). A Perceived susceptibility to and perceived causes of road traffic injuries in an urban and rural area of Tanzania. Accident Analysis and Prevention, 38, 54e62. Barengo, N. C., Mkamba, M., Mshana, S. M., & Miettola, J. (2006). Road traffic accidents in Dar-es-Salaam, Tanzania during 1999 and 2001. Injury Control and Safety Promotion, 13, 52e54. Bekibele, A. M., Fawole, O. I., Bamgboye, A. E., Adekunle, L. V., Ajav, R., & Baiyeroju, A. M. (2007). Risk factors for road traffic accidents among drivers of public institutions in Ibadan, Nigeria. African Journal of Health Sciences, 14, 137e142. Booysen, A. E. (1988). Die Verband tussen Persoonlikheidsaspekte en roekelose of nalatige bestuur (The relationship between aspects of personality and reckless or negligent driving). Master’s thesis, University of Pretoria, Fakulteit Lettere en Wysbegeerte. Butchart, A., Kruger, J., & Lekoba, R. (2000). Perceptions of injury causes and solutions in a Johannesburg township: Implications for prevention. Social Science & Medicine, 50, 331e344.

Source: Reproduced with permission from Forjuoh (2003).

l

l

Applicability in African Countries

BAC, blood alcohol content. *Denotes intervention with some evaluation in low-income countries.

l

515

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l

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Index

ABS, see Antilock braking system ACC, see Adaptive cruise control Accessible pedestrian signal (APS), 362 Accident self-reports, see Self-report Accompanied driving, 411e413, 415e416 AD, see Alzheimer’s disease Adaptive cruise control (ACC), 202e203 ADAS, see Advanced driver assistance system ADHD, see Attention deficit/hyperactivity disorder Adolescents, see Young drivers Advanced driver assistance system (ADAS), 202, 333 Africa, see Sub-Saharan Africa Age, see also Older drivers; Young drivers crash epidemiology, 316e317 human factors/ergonomics, 201e202 motorcycle crash correlation, 378e379 red light runner studies, 269 seat belt use rates, 222e223 Aggression negative driving outcomes, 151e152 sub-Saharan Africa, 510 Aging, see Older drivers Air bags, see Child safety, 308 AISS, see Arnett Inventory of Sensation seeking Alcohol, see Driving under the influence Alzheimer’s disease (AD), older drivers, 128e130, 342, 346 Amygdala, maturation in young drivers, 111e112 Anger, negative driving outcomes, 152e153, 166e167 Antilock braking system (ABS), 202e203, 261 Anxiety negative driving outcomes, 167 travel anxiety following motor vehicle crash, 172 APS, see Accessible pedestrian signal AR, see Attributable risk Arnett Inventory of Sensation seeking (AISS), 150 Attention, see Driver distraction; Driver inattention Attention deficit/hyperactivity disorder (ADHD) negative driving outcomes, 167e168 young drivers driving outcomes, 326e327 licensure status, 326

prospects for study, 328 treatment, 334 Attributable risk (AR), calculation, 472 Autism driving research, 328 emotional regulation difficulties, 327 motor and cognitive effects, 327 strengths, 327 visual perception effects, 327 Automated enforcement live officer comparison, 445 revenue and perception, 449 AVECLOS, fatigue monitoring, 293 Avoidance behavior, enforcement, 448 BAC, see Blood alcohol content Balanced Inventory of Desirable Responding (BIDR), 53e54 Behavioral Risk Factor Surveillance System (BRFSS) Bicyclists driver inattention, 368e370 human nature of bicyclists, 371 minority status of bicycling, 370e371 naturalistic observation of behavior, 64 overview, 367e368 prospects for study, 371e372 public attitudes, 368 BIDR, see Balanced Inventory of Desirable Responding BIFA, see Buses Involved in Fatal Accidents Blindness, see Visual impairment Blood alcohol content (BAC), 232, 234, 237e241, 255, 348, 353 Boredom susceptibility (BS), 390 Brain aging, see Older drivers development, see Young drivers Breath test, see Compulsory breath testing BRFSS, see Behavioral Risk Factor Surveillance System BS, see Boredom susceptibility BTW driving evaluation, 129 Bus driver characteristics, 394e396 journey experience of city bus, 492e493 Buses Involved in Fatal Accidents (BIFA), 394 CABG, see Coronary artery bypass graft Caffeine, driver fatigue avoidance, 292e293 Car, journey experience, 490e491

Caseecontrol study advantages and disadvantages, 40e41 applications, 30 bias, 39e40 case definition, 30e31 identification and selection, 30e32 characteristics, 29e30 classification, 38 controls advantages and disadvantages by type, 33e34 definition, 32 identification and selection, 32 matching definition, 35 disadvantages, 35e36 forms, 35 rationale, 36 number control groups, 37 one group for several diseases, 37 ratio to cases, 36e37 sources, 32e35 definition, 29 odds ratio in analysis, 37e38 representativeness, 40e41 CBT, see Compulsory breath testing CC, see Corpus callosum CDC, see Centers for Disease Control and Prevention Centers for Disease Control and Prevention (CDC), traffic injury prevention, 460e461 Cerebellum, maturation in young drivers, 112 Cerebral vascular accident, see Stroke Checkpoints program, 333 Child safety air bags, 308 caregiver naivete´ and complacency, 309 global considerations economic disparities in road traffic injuries, 302 regional differences in priority issues and standard practices, 302e303 injury and mortality statistics, 301e302 intervention recommendations, 309e310 prevention strategies, 303 prospects for study, 310e311 rear seating, 307e308 restraints

519

520

Child safety (Continued ) booster seats, 306e307 crash dynamics, 304e305 forward-facing seats with harness systems, 306 lap/shoulder safety belts, 307 misuse, 308e309 rear-facing seats, 305e306 special needs children, 307 use factors, 308 Chronic obstructive pulmonary disease (COPD), driving outcomes, 347 CIOT, see Click It or Ticket Circadian rhythm, driver fatigue, 290 Click It or Ticket (CIOT), 219, 222 Climate change, 487e489, 499 CODES, see Crash Outcomes Evaluation System Cognition, see Executive function; Older drivers; Young drivers Comfort zone model, 33 Commercial drivers, see Professional drivers Communications, see Message development Community Trials Project, 243e244 Compulsory breath testing (CBT), 5 Congestion, personality context in driving outcomes, 156e157 Control, see Caseecontrol study Control theory alternative conceptualizations of driver goals, 23e24 compliance, 20e21 interference and driver distraction, 281 overview, 13 risk allostasis theory feelings and role in decision making, 21e23 overview, 21 somatic marker hypothesis, 23 taskecapability interface model driver capability, 15 driving task demand, 15 difficulty, 14e15 individual preferences in preferred task demand and difficulty, 18e19 starting point, 13e14 task difficulty homeostasis boundaries of preferred task demand, 17e18 calibration, 16e17 evidence, 17 hysteresis and top-down feed-forward control, 17 principles, 15e16 task difficulty as risk feeling, 18 task difficulty allostasis and temporary influences in risk threshold, 20 COPD, see Chronic obstructive pulmonary disease Coronary artery bypass graft (CABG), driving outcomes, 347 Corpus callosum (CC), maturation in young drivers, 111, 117

Index

Crash data analysis aggregate data analysis example, 103 frequency analysis, 103 rate analysis, 103e104 regression-based data analysis, 104 tasks aggregation, 102 integration, 102e103 normalization, 103 selection, 102 trend analysis, 104 disaggregate data analysis, 104e105 traffic safety data medical crash data, 99e100 police crash data databases, 98e99 elements, 98 supplement data license and registration data, 101 roadway and traffic data, 100e101 sociodemographic data, 101 travel survey data, 101 traffic safety survey data, 100 Crash Outcomes Evaluation System (CODES), 100 Crosswalk, see Pedestrian safety Culture, driving, 158e159 DAS, see Driving Anger Scale Data coding, see Naturalistic driving studies DBQ, see Driver Behaviour Inventory DD, see Designated driver DDAB, see National Survey of Drinking and Driving Attitudes and Behavior Dementia, see Alzheimer’s disease; Huntington’s disease; Older drivers; Parkinson’s disease Department of Transportation, traffic injury prevention, 460 Depression, negative driving outcomes, 167 Designated driver (DD), 239e240 Deterrence theory, enforcement, 443 Distraction, see Driver distraction Driver Behaviour Inventory (DBQ) assessment construct validity, 52e55 content validity, 52 criterion-related validity, 55 reliability, 51e52 overview, 45e47 socially desirable responding, 56e57 stress evaluation, 154 Driver control theory, see Control theory Driver distraction definition, 275e276 interference, 281e282 management, 284 moderating factors, 280e281 performance impact, 282 safety impact, 282e284 sources, 279 types, 279e280 young drivers, 318e319

Driver education and training definition, 403 driver licensing models accompanied driving, 411e413, 415e416 combined and multiphase models, 413, 416e417 driving school education, 410e411 graduated driving licensing, 330e331, 333e334, 404e405, 411e413 overview, 410 Goals for Driver Education, 404e405, 410, 417e418 Norway policy analysis, 477e479 novice driver accident relationship with goals and contents of education complex cognitive skill aspects of driving, 407 hierarchical levels of driver behavior goals and context of driving, 409 goals for life and skills for living, 408e409 importance for safe driving, 407e410 mastery of traffic situations, 409 vehicle maneuvering, 409 motivated theories of behavior, 406e407 overview, 405e406 prospects, 418e419 road safety, 3e4 sub-Saharan Africa, 510 training without theory, 404e405 Driver fatigue, see Fatigue Driver inattention bicyclist safety, 368e370 definition, 276 driver-diverted attention, 278 management, 284 model, 277e279 safety impact, 282e284 taxonomy, 277 Driver licensing, see Driver education and training Driver Skill Inventory (DSI), 45e48 Driver Social Desirability Scale (DSDS), 53e54, 56e57 Driver speed, see Speed (ing) Driver Stress Inventory (DSI), 154 Driving Anger Scale (DAS), 152e153 Driving simulator experimental design, 95 motion platforms, 89e90 overview, 87e88 rationale for use, 88e89 requirements, 90e92 simulator sickness, 94e95 validity as research tool, 92e94 Driving under the influence (DUI) alcohol effects on driving ability, 231e232 blood alcohol concentration, 232, 237e241 drug-impaired driving, 232 enforcement impact, 446e447 large-scale prevention of alcohol-impaired driving effectiveness evaluation, 235e236 implementation versus interventions, 235

Index

multicomponent community systems approaches Community Trials Project, 243e244 Saving Lives Program, 243 non-policy programs blood alcohol concentration feedback intervention, 237e238 designated driver programs, 239e240 grassroot advocacy, 236 mass media interventions, 236e237 normative feedback intervention, 238 server intervention and safe ride programs, 238e239 overview, 235 policy and legal initiatives alcohol control policies, 242 general deterrence strategies, 241e242 local policies, 243 specific deterrence strategies, 240e241 message development, see Message development motorcycle crash correlation, 379e380 public perceptions, 234 risk factors, 232e233 seat belt use, 233 self-reports, 233e234 statistics, 231 sub-Saharan Africa interventions, 512e513 overview, 509e511 trends in alcohol-involved crashes, 234 young drivers, 319, 323 Driving Vengeance Questionnaire (DVQ), 153 Drowsy driving, see Fatigue Drunk driving, see Driving under the influence DSDS, see Driver Social Desirability Scale DSI, see Driver Skill Inventory; Driver Stress Inventory DUI, see Driving under the influence DVQ, see Driving Vengeance Questionnaire Education, see Driver education and training; Message development EF, see Executive function Elderly, see Older drivers Electronic stability control (ESC), 261 Emergency Medical Service (EMS) run report database, 99 Emergency response, road safety, 8e9 Emotions, processing in young drivers, 115e116 EMS run report database, see Emergency Medical Service run report database Enforcement automated enforcement versus live officers, 445 challenges avoidance behavior, 448 punishment versus rehabilitation, 449 unequal outcomes of punishment, 448e449 vulnerability perception, 447e448 deterrence theory, 443

521

impact crash and casualty deduction, 447 driving under the influence, 446e447 seat belt use, 445e446 speeding, 447 judicial system, 442 learning theory, 443e445 legislation, 442 Norway policy analysis of speed limits, 479 officers, 442 overview, 441e442 pedestrian safety, 355e356 prospects for study automated enforcement revenue and perception, 449 behavior of enforcers, 449e450 punishment-based enforcement alternatives, 450e451 red light camera, 271e272 road safety, 4e6 Engineering, road safety, 6 Epidemiological study caseecontrol study, see Caseecontrol study classification, 28e29 Epilepsy, older drivers, 346 Ergonomics, see Human factors/ergonomics ESC, see Electronic stability control E’s in road safety education, 3e4 emergency response, 8e9 enforcement, 4e6 engineering, 6 evaluation, 9e10 examination of competence and fitness, 7e8 exposure, 6e7 Ethnicity red light running studies, 270 seat belt use rate studies, 224e225 young driver crashes, 317 Evaluation, road safety, 9e10 Examination, competence and fitness, 7e8 Executive function (EF) cool executive function, 341e342 driving task, 342 hot executive function, 341e342 overview, 339e341 Exposure, road safety, 6e7 Eye movement critical information, 138e140 driverevehicle interactions in human factors/ ergonomics, 200e201 driving tasks, 138e138 fixation versus saccades, 138 glance duration measurement, 140e142 prospects for study, 145e146 spread measures, 142e145 tracking areas of interest, 138 FARS, see Fatality Analysis Reporting System Fatality Analysis Reporting System (FARS), 98e99

Fatigue causes causal interactions, 290 sleep-related fatigue, 289e290 task-related fatigue, 288e289 countermeasures breaks, 292e293 caffeine, 292e293 fatigue detection and warning systems, 293 rumble strip, 293 crash statistics, 287 high-risk populations commercial drivers,, 291e292, 391, 395, 397 shift workers, 292 sleep disorders, 290e291 young drivers, 292 overview, 287 prospects for study, 294 sub-Saharan Africa, 510 surveys, 288 young drivers, 319e320, 324 FFD, see First fixation duration First fixation duration (FFD), glance duration measurement, 140e142 GA, see Guardian Angel GADGET study, 405, 417 GANT chart, 435 GDE, see Goals for Driver Education GDL, see Graduated driver licensing Gender, see Sex differences General Estimate System (GES), 99 GES, see General Estimate System GHG, see Greenhouse gas Goals for Driver Education (GDE), 404e405, 410, 417e418 Grade point average, young driver crash relationship, 317 Graduated driving licensing (GDL), 330e331, 333e334, 404e405, 411e415, 479 Gray matter, maturation in young drivers, 111 Greenhouse gas (GHG), 487, 499 Guardian Angel (GA), 237 HD, see Huntington’s disease Health belief model, traffic safety applications, 428e430 Heinrich’s triangle, industrial safety, 74 HFE, see Human factors/ergonomics Higher functioning autism spectrum disorders, see Autism Highway Performance Monitoring System (HPMS), 101 Highway Safety Information System (HSIS), 99 Hippocampus, maturation in young drivers, 111e112 Hospital discharge database, 100 HPMS, see Highway Performance Monitoring System HSIS, see Highway Safety Information System

522

Human factors/ergonomics (HFE) driver behavior models, 194e198 typical versus performance, 198e199 driverevehicle interactions driver variables affecting anthropometry and age, 201e202 decision making and memory, 201 eye movements and perception, 200e201 environmental variables affecting illumination, 207 roadway design, 207 traffic control devices, 207e208 vehicle variables affecting automatic versus manual gears, 205e206 cockpit design, 204 communication, 205 field view and mirrors, 204e205 intelligent transport systems, 202e203 overview, 193 Huntington’s disease (HD), older drivers, 131 Ignition interlock device, driving under the influence prevention, 240e241 Immigration, traffic safety challenges, 467 Inattention, see Driver inattention Independent Transportation Network (ITN), 349 Injury Surveillance System (ISS), 99 Intellectual disability, young drivers, 328 Intelligent speed adaptation (ISA), 465 Intelligent transport system (ITS), driverevehicle interactions in human factors/ergonomics, 202e203 In-vehicle information system (IVIS), 202 In-vehicle technology, driverevehicle interactions in human factors/ ergonomics, 202e204 ISA, see Intelligent speed adaptation Ishikawa diagram, 250e252, 260, 262 ISS, see Injury Surveillance System ITN, see Independent Transportation Network ITS, see Intelligent transport system IVIS, see In-vehicle information system Journey experience car, 490e491 city bus, 492e493 motorcycle, 491e492 KABCO severity scale, 98 Lane position errors, older drivers, 344 Learning theory, enforcement, 443e445 Life expectancy, versus driving life expectancy, 349 LOC, see Locus of control Locus of control (LOC) driving outcomes, 155e156 speeding analysis, 255

Index

MADD, see Mothers against Drunk Driving MaxSem model, travel mode change, 498 Medications, older drivers education, 134 emotional impairment and self-awareness, 134 side effects, 348e349 Melatonin, considerations in young drivers, 112 Mental health, see also specific conditions driving effects on mental health, 168e172 impact on driving, 165e168 Message development literature review, 423e424 overview, 422e423, 437e438 steps assumption clarification, 427e430 audience understanding, 426e427 content building, 433e434 foundations, 424e425 implementation, 435e436 issue defining, 425e426 pilot test and refinement, 434e435 plan preparation, 430e432 review, refinement, and regeneration, 436e438 Mileage, self-report, 49e51 MMUCC, see Model Uniform Crash Criteria Mobileye, 205 Mode choice, see Travel mode Model Uniform Crash Criteria (MMUCC), 98 Monitor model, 23 Mothers against Drunk Driving (MADD), 236e237 Motivational messaging, see Message development Motor Vehicle Occupant Safety Survey (MVOSS), 100, 216, 225 Motorcycle Rider Behavior Questionnaire (MRBQ), 381 Motorcyclists behavior studies, 380e383 crashes characteristics, 377e378 correlates, 378e380 helmet use in sub-Saharan Africa interventions, 513e514 overview, 511 journey experience, 491e492 prospects for study, 383e384 trends in use and safety, 376e377 vulnerability, 375e376 MRBQ, see Motorcycle Rider Behavior Questionnaire MSLT, see Multiple sleep latency test Multiphase models, driver licensing, 413, 416e417 Multiple sleep latency test (MSLT), 288e290 MVOSS, see Motor Vehicle Occupant Safety Survey National Occupant Protection Use Survey (NOPUS), 100

National Survey of Drinking and Driving Attitudes and Behavior (DDAB), 100 Nationwide Household Transportation Survey (NHTS), 101 Naturalistic driving studies advanced product testing, 83e84 contributing factors for crashes and nearcrashes, 82e83 crash risk assessment, 82 data coding coder training, 78, 80 data delivery, 81 data reduction, 78 overview, 76e78 quality assurance/quality control protocol development, 78, 80 tools, 80e81 workflow, 79 data preparation and storage, 75e76 driving exposure analysis, 82 philosophy of large-scale instrumented vehicle studies, 74e75 sensor technologies, 76 study design and data collection, 75 traffic conflict technique, 73e74 Naturalistic observation applications driver distraction, 64e65 pedestrian and bicyclist behavior, 64 risky driving behavior, 65 seat belt use, 65e66 design of survey data collection, 69 observation site selection, 67 organization, 67 statistical analysis, 69 survey scheduling, 67e69 direct versus unobtrusive observation, 62 nighttime observations, 63e64 observer training, 63 overview, 61 rationale, 61e62 sampling design, 62e63 variables, 62 NHTS, see Nationwide Household Transportation Survey Nighttime driving, see Fatigue; Naturalistic observation NOPUS, see National Occupant Protection Use Survey Norway, public policy analysis driver training, 477e479 graduated driver licensing, 479 overview, 476e477 speed limits enforcement, 479 rewarding safe behavior, 481 setting, 479e481 Occupant protection, see Child safety; Seat belt Odds ratio caseecontrol study analysis, 37e38

Index

crash risk assessment in naturalistic driving studies, 82 Older drivers characteristics, 128e129 cognition changes in aging, 127e128 demands of driving task, 339 costs of motor vehicle injuries, 342e343 dementia Alzheimer’s disease, 128e130 Huntington’s disease, 131 Parkinson’s disease, 128, 130e131 driving errors crashes, 344 hazardous error, 343e344 lane position errors, 344 pedal errors, 343 speed control, 344 traffic control light compliance, 344 executive function cool executive function, 341e342 driving task, 342 hot executive function, 341e342 overview, 339e341 medications emotional impairment and self-awareness, 134 education, 134 side effects, 348e349 neuroanatomy changes in aging, 127 physical changes in aging and driving impact cardiovascular system, 346e347 endocrine system, 347 musculoskeletal impairment, 348 neurological impairment, 346 overview, 344e345 respiratory system, 347 visual impairment, 345e346 psychosocial challenges, 349 recommendations, 349e350 stroke cognition and perception effects, 133e134 etiology, 132 impact in aging, 134 right versus left hemisphere stroke effects on driving, 132e133 Omnibus Sleep in America Poll, 288 Osteoarthritis, older drivers, 348 PAPM, see Precaution adoption process mode Parietal lobe, maturation in young drivers, 112e113 Parkinson’s disease (PD), older drivers, 128, 130e131 PD, see Parkinson’s disease Pedal errors, older drivers, 343 Pedestrian safety crashes screening crashes, 356e359 traffic signals, 360e362 uncontrolled crosswalks, 362 education, 355 enforcement, 355e356

523

engineering elements roundabout, 355 signals and beacons, 354 signs and markings, 354 speed hump, 354 speed table, 354 multifaceted program needs, 353e354 naturalistic observation of behavior, 64 statistics, 353 visually impaired pedestrians, 362e363 Percent eye closure (PERCLOS), fatigue monitoring, 293 PERCLOS, see Percent eye closure Personality aggression, 151e152 anger, 152e153 context in driving outcomes driving culture and norms, 158e159 physical environment, 157e158 traffic congestion, 156e157 definition, 149 locus of control, 155e156 motorcyclist behavior, 383 negative driving outcomes, 150 prospects for study of driving influences, 159e160 sensation seeking, 150e151 stress, 153e155 Persuasion, see Message development PERT, see Program Evaluation and Review Technique PFC, see Prefrontal cortex Phobia, following motor vehicle crash, 172 Pineal gland, maturation and driving influences, 112 POCD, see Postoperative cognitive dysfunction Post-traumatic stress disorder (PTSD), motor vehicle crashes, 169e172 Postoperative cognitive dysfunction (POCD), 347 Precaution adoption process model (PAPM), 309 Prefrontal cortex (PFC), see also Executive function maturation in young drivers, 109e110, 113e115 Professional drivers bus drivers, 394e396 fatigue risks,, 291e292, 391, 395, 397 overview, 389e390 risk taking, 389e390 taxi drivers, 396e397 training, 405 truck drivers, 390e394 Program Evaluation and Review Technique (PERT), 435 Project Alcohol Risk Management (ARM), 239 Project HUSSAAR, 18e19, 21 PSA, see Public service announcement Psychomotor vigilance test (PVT), 289 PTSD, see Post-traumatic stress disorder Public awareness, see Message development

Public health leaders in traffic injury prevention Centers for Disease Control and Prevention, 460e461 collaboration in medicine, 461e462 Department of Transportation, 460 state and local public health departments, 461 model for prevention application to traffic injury, 459e460 prospects for study in traffic injury prevention evidence-based interventions, 467 immigration, 467 overview, 464e465 public attitudes and perceptions, 467e468 special populations, 466 surveillance, 467 technology, 466 traffic psychology and public health collaboration, 465e466 traffic injury history and burden, 457e458 traffic safety problem addressing, 458e459 progress documentation, 462e463 traffic psychology and public health combined contributions, 463e464 Public policy analytical model of policy making, 471 large-scale prevention of alcohol-impaired driving alcohol control policies, 242 general deterrence strategies, 241e242 local policies, 243 specific deterrence strategies, 240e241 Norway policy analysis driver training, 477e479 graduated driver licensing, 479 overview, 476e477 speed limits enforcement, 479 rewarding safe behavior, 481 setting, 479e481 traffic psychology contribution to policy making developing motivating targets, 473e474 highway safety measures effect estimation, 474 evaluation of effects, 476 expected effect estimation, 474e476 survey of effective measures, 474 prospects, 481e482 unsafe road user behavior as road safety problem, 471e473 Public service announcement (PSA), drunk driving prevention, 236e237 Punishment, see Enforcement PVT, see Psychomotor vigilance test Race, see Ethnicity RAT, see Risk allostasis theory RBS, see Responsible beverage service Rectangular rapid flashing beacon (RRFB), pedestrian safety, 358e359

524

Red light cameras, 271e272 crash statistics, 267 driver attitudes, 271 driver characteristics of runners age, 269 ethnicity, 270 seat belt use, 270e271 sex differences, 269 enforcement response, 271e272 older driver compliance, 344 pedestrian crashes, 360e362 violation frequency, 267e268 Responsible beverage service (RBS), 242 Rheumatoid arthritis, older drivers, 348 RHT, see Risk homeostasis theory Ride Like a Friend (RLAF), 331e333 Risk allostasis theory (RAT) alternative conceptualizations of driver goals, 23e24 feelings and role in decision making, 21e23 overview, 21 somatic marker hypothesis, 23 Risk feeling, see Control theory Risk homeostasis theory (RHT), speeding analysis, 256e257 RLAF, see Ride Like a Friend Roadway inventory database, 101 Roundabout, pedestrian safety, 355 RRFB, see Rectangular rapid flashing beacon Rumble strip, driver fatigue avoidance, 293 SADD, see Students against Destructive Decisions SafeTRAC, fatigue monitoring, 293 Safety belt, see Seat belt Safety promotion, see Message development Saving Lives Program, 243 Screening crashes, pedestrians, 356e359 Seat belt child safety, see Child safety effectiveness, 215e216 historical perspective, 215 naturalistic observation of use applications, 65e66 survey design data collection, 69 observation site selection, 67 organization, 67 statistical analysis, 69 survey scheduling, 67e69 sub-Saharan Africa use interventions, 513e514 overview, 511 use rate driving under the influence, 233 enforcement impact, 445e446 factors affecting age, 222e223 ethnicity, 224e225 income and education, 225 nighttime, 226 part-time and non-users, 220e221

Index

population density, 223e224 seating position, 224 sex differences, 223 vehicle purpose, 225 vehicle type, 221e222 international, 217e218 measurement, 216e217 red light runners, 270e271 taxi drivers, 396 United States, 218e220 Self-report accident involvement, 48e49 alcohol-impaired driving, 233e234 Driver Behaviour Inventory assessment construct validity, 52e55 content validity, 52 criterion-related validity, 55 reliability, 51e52 overview, 45e47 Driver Skill Inventory, 45e48 driver performance and behavior in crashes, 43e45 mileage, 49e51 near accident report as criterion for safe driving, 49 research applications, 43e48 socially desirable responding impression management and selfdeception, 55e56 management, 57 self-reports of driving behavior, 56e57 Sensation seeking, negative driving outcomes, 150e151 Sex differences driving under the influence, 232 red light runner studies, 269e270 seat belt use, 223 young driver behavior, 116e117, 317 Simulator, see Driving simulator Sleep in America Poll, 288 Sleepy driving, see Fatigue SME, see Subject matter expert Speed hump, pedestrian safety, 354 Speed(ing) driver factors behavior factors, 257e258 cultural factors, 258e259 person factors, 254e257 enforcement impact, 447 environmental factors roadway dynamics, 259e261 vehicle systems and controls, 261 motorcycle crash correlation, 379 Norway policy analysis of speed limits enforcement, 479 setting, 479e481 older drivers and speed control, 344 overview, 249e250 postmortem analysis of consequences cost of vehicle crashes, 251e253 social and economic consequences, 253e254 traffic fines and penalties, 253

research as quality control initiative, 250e251, 261e263 risk homeostasis theory, 256e257 sub-Saharan Africa interventions, 512 overview, 509e510 Speed table, pedestrian safety, 354 Stages of change model, 428 Stop sign crash statistics, 267 violation frequency, 268e269 Stress, negative driving outcomes, 153e155, 168 Stroke cognition and perception effects, 133e134 etiology, 132 impact in aging, 134, 346e347 right versus left hemisphere stroke effects on driving, 132e133 Students against Destructive Decisions (SADD), 236 Sub-Saharan Africa interventions driving under the influence, 512e513 helmet use, 513e514 prospects, 514e515 protection of vulnerable road users, 514 seat belt use, 513e514 speeding, 512 road use behavior accident causes, 507e509 aggression, 510 driver education and training, 510 driving under the influence, 509e511 fatigue, 510 helmet use, 511 methodology for study, 503, 505 risk perception, 507e509 road users age, 506 sex, 505e506 socioeconomic status, 506e507 types, 505 seat belt use, 511 speeding, 509e510 vehicle type, 505 traffic mortality rates and indicators, 503e504 Subject matter expert (SME), Ishikawa process, 250 Substance abuse, see also Driving under the influence motor vehicle crash risks, 166 Supplemental restraint systems, see Air bags Tangential driving responses (TDRs), speed influences, 257e258 Target audience, see Message development TAS, see Thrill and Adventure Taskecapability interface (TCI) model driver capability, 15 driving task demand, 15 difficulty, 14e15

Index

individual preferences in preferred task demand and difficulty, 18e19 starting point, 13e14 task difficulty homeostasis boundaries of preferred task demand, 17e18 calibration, 16e17 evidence, 17 hysteresis and top-down feed-forward control, 17 principles, 15e16 task difficulty as risk feeling, 18 Task difficulty homeostasis, see Control theory Taxi drivers, 396e397 TCI model, see Taskecapability interface model TDRs, see Tangential driving responses TDT, see Total dwell time Teens, see Young drivers Theory of planned behavior (TPB) overview, 19 speeding analysis, 256e257, 259 Thrill and Adventure (TAS), 390 TIPS, see Training for Intervention Procedures by Servers of Alcohol Total dwell time (TDT), glance duration measurement, 140 TPB, see Theory of planned behavior Traffic conflict technique, see Naturalistic driving studies Traffic controls, see Red light; Stop sign Traffic culture accidents behavioral factors, 180 causation, 179e180 climate comparison, 187e188 levels eccocultural/sociopolitical level factors culture, 183e184 economy, 182e183 individual level, 180e181 meso level group/community level factors, 181e182 organizational/company level factors, 181 national level factors, 182 summary of interactions, 184e186 prospects for study, 189e190 traffic safety culture, 187e189 Traffic light, see Red light Traffic safety data analysis, see Crash data analysis medical crash data, 99e100

525

police crash data databases, 98e99 elements, 98 supplement data license and registration data, 101 roadway and traffic data, 100e101 sociodemographic data, 101 travel survey data, 101 traffic safety survey data, 100s Traffic database, 101 Training, see Driver education and training Training for Intervention Procedures by Servers of Alcohol (TIPS), 238 Trauma registry database, 99e100 Travel anxiety, following motor vehicle crash, 172 Travel mode behavior change, 496e498 car substitution, 495e496 costs of change, 489e490 Garling’s model of mode choice, 486e487 historical perspective, 485e486 journey experience car, 490e491 city bus, 492e493 motorcycle, 491e492 motorized transport impact on planet, 487e489 United Kingdom attitudes on car use and climate change, 489, 493e495 Truck drivers, 390e394 United Kingdom, attitudes on car use and climate change, 489, 493e495 Visual attention, see Driver inattention; Eye movement Visual impairment older drivers, 345e346 pedestrian safety, 362e363 Walk signal, see Pedestrian safety White matter, maturation in young drivers, 110e111 Yield Here to Pedestrians sign, 358 Young drivers attention deficit/hyperactivity disorder driving outcomes, 326e327 licensure status, 326 treatment, 334 autism driving research, 328 emotional regulation difficulties, 327

motor and cognitive effects, 327 strengths, 327 visual perception effects, 327 characteristics, 109 controlling risky behavior, 113e115 crash epidemiology age, 316e317 ethnicity, 317 fatalities, 316 grade point average, 317 modifiable factors driver distraction, 318e319 driving experience, 318 driving under the influence, 319 fatigue, 319e320 parenting, 320 risk taking, 318 seat belt use, 318 sex differences, 116e117, 317 crash sequences model, 321e322 driving as hierarchical model of competencies, 321 emotion processing, 115e116 fatigue risks, 292 global differences, 320 intellectual disability impact, 328 interventions for safe driving behavioral objectives, 329 family level interventions, 333 individual level interventions, 333e334 practical approach for development, 329e330 school level interventions, 332e333 societal level interventions, 330e332 neurological development and driving effects alcohol effects, 323 amygdala, 111e112 cerebellum, 112 corpus callosum, 111, 117 fatigue susceptibility, 324 gray matter, 111 higher order cognitive development expertise, 324 risk taking, 324e325 hippocampus, 111e112 myths, 322e323 parietal lobe, 112e113 prefrontal cortex, 109e110, 113e115 summary of key findings, 117e122 white matter, 110e111 psychosocial factors personality, 325 social context and influences, 325e326 response inhibition, 113 teen drivers versus young adults, 320e321

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