Crash rates over time among younger and older drivers in the SHRP 2 naturalistic driving study

Objective: To examine crash rates over time among 16–17-year-old drivers compared to older drivers. Methods: Data were from a random sample of 854 of the 3,500 study participants in SHRP 2, a U.S. national, naturalistic driving (instrumented vehicle) study. Crashes/10,000 miles by driver age group, 3-month period, and sex were examined within generalized linear mixed models. Results: Analyses of individual differences between age cohorts indicated higher incidence rates in the 16–17-year old cohort relative to older age groups each of the first four quarters (except the first quarter compared to 18–20 year old drivers) with incident rate ratios (IRR) ranging from 1.98 to 18.90, and for the full study period compared with drivers 18–20 (IRR = 1.69, CI = 1.00, 2.86), 21 to 25 (IRR = 2.27, CI = 1.31, 3.91), and 35 to 55 (IRR = 4.00, CI = 2.28, 7.03). Within the 16–17-year old cohort no differences were found in rates among males and females and the decline in rates over the 24-month study period was not significant. Conclusions: The prolonged period of elevated crash rates suggests the need to enhance novice young driver prevention approaches such as Graduated Driver’s Licensing limits, parent restrictions, and post-licensure supervision and monitoring. Practical Applications: Increases are needed in Graduated Driver’s Licensing limits, parent restrictions, and postlicensure supervision and monitoring.     

Effectiveness of mandatory license testing for older drivers in reducing crash risk among urban older Australian drivers

Most licensing jurisdictions in Australia maintain mandatory assessment programs targeting older drivers, whereby a driver reaching a specified age is required to prove his or her fitness to drive through medical assessment and/or on-road testing. Previous studies both in Australia and elsewhere have consistently failed to demonstrate that age-based mandatory assessment results in reduced crash involvement for older drivers. However studies that have based their results upon either per-population or per-driver crash rates fail to take into account possible differences in driving activity. Because some older people maintain their driving licenses but rarely if ever drive, the proportion of inactive license-holders might be higher in jurisdictions without mandatory assessment relative to jurisdictions with periodic license assessment, where inactive drivers may more readily either surrender or lose their licenses. The failure to control for possible differences in driving activity across jurisdictions may be disguising possible safety benefits associated with mandatory assessment. The current study compared the crash rates of drivers in Melbourne, Australia, where there is no mandatory assessment and Sydney, Australia, where there is regular mandatory assessment from 80 years of age onward. The crash rate comparisons were based on four exposure measures: per population, per licensed driver, per distance driven, and per time spent driving. Poisson regression analysis incorporating an offset to control for inter-jurisdictional road safety differences indicated that there was no difference in crash risk for older drivers based on population. However drivers aged 80 years and older in the Sydney region had statistically higher rates of casualty crash involvement than their Melbourne counterparts on a per license issued basis (RR: 1.15, 1.02-1.29, p=0.02) and time spent driving basis (RR: 1.19, 1.06-1.34, p=0.03). A similar trend was apparent based on distance travelled but was of borderline statistical significance (RR: 1.11, 0.99-1.25, p=0.07). Collectively, it can be inferred from these findings that mandatory license re-testing schemes of the type evaluated have no demonstrable road safety benefits overall. Further research to resolve this on-going policy debate is discussed and recommended.     

Difficulties in emotion regulation and risky driving among Lithuanian drivers

Background: Risky driving is a common cause of traffic accidents and injuries. However, there is no clear evidence of how difficulties in emotion regulation contribute to risky driving behavior, particularly in small post-Soviet countries. The present study aimed to investigate the relationship between difficulties in emotion regulation and self-reported risky driving behavior in a sample of Lithuanian drivers.

Methods: A total of 246 nonprofessional Lithuanian drivers participated in a cross-sectional survey. Difficulties in emotion regulation were assessed using the Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer 2004), and risky driving behavior was assessed using the Manchester Driver Behaviour Questionnaire (DBQ; Lajunen et al. 2004).

Results: Males scored higher than females in aggressive violations and ordinary violations. Females scored higher for the nonacceptance of emotional responses, whereas males had more difficulties with emotional awareness than females. More difficulties in emotion regulation were positively correlated with driving errors, lapses, aggressive violations, and ordinary violations for both males and females. Structural equation modeling showed that difficulties in emotion regulation explained aggressive and ordinary violations more clearly than lapses and errors. When controlling for interactions among the distinct regulation difficulties, difficulties with impulse control and difficulties engaging in goal-directed behavior predicted risky driving. Furthermore, nonacceptance of emotional responses and limited access to emotion regulation strategies were related to less violations and more driving errors.

Conclusion: Emotion regulation difficulties were associated with the self-reported risky driving behaviors of Lithuanian drivers. This provides useful hints for improving driver training programs in order to prevent traffic injuries.     

Working conditions and sleepiness while driving among truck drivers

Objective: The objective of this study is to identify the role of working conditions as predictors of sleepiness while driving among truck drivers.

Methods: This was a cross-sectional study carried out among truck drivers who transported grains to Paranaguá Port, Paraná, Brazil. The truck drivers were interviewed and completed a self-administered questionnaire to collect data on sociodemographic and behavioral variables, working conditions, consumption of illicit psychoactive substances, and sleep patterns. Drivers were considered to be sleepy while driving if they reported a medium or high probability of napping while driving at night, during the daytime, or while stopped in traffic. The statistical analysis used logistic regression models progressively adjusted for age, behavioral variables, sleep duration, and other working conditions.

Results: In total, 670 male drivers, with a mean age of 41.9 (±11.1) years, were enrolled. The prevalence of sleepiness while driving was 31.5%. After model adjustments, the following working conditions were associated with sleepiness while driving: Distance from the last shipment of more than 1,000?km (odds ratio [OR]?=?1.54; 95% confidence interval [CI], 1.07–2.23) and a formal labor contract with a productivity-based salary (OR = 2.65; 95% CI, 1.86–3.78). Consumption of illicit psychoactive substances (OR = 1.99; 95% CI, 1.14–3.47) was also associated with sleepiness while driving.

Conclusions: Distance traveled and a formal labor contract with productivity-based earnings were the working conditions associated with sleepiness while driving, regardless of other working or behavioral characteristics, age, consumption of illicit psychoactive substances, and sleep duration.     

Healthcare utilization and costs among older adult female drivers and former drivers

PurposeThis study compared the healthcare utilization and costs for specific types of medical services among older adult women who currently drive and those who no longer drive.MethodsThis study included 347 women aged 65 or older who were either former (had stopped driving) or current drivers, randomly sampled from a large U.S. health plan to participate in a telephone survey, and who had automated health records with healthcare utilization and cost data. Bivariate analyses and generalized linear modeling were used to examine associations between driving status and healthcare utilization and costs.ResultsAdjusting for age, income, and marital status, former drivers were more likely than current drivers to use mental health care services (RR = 3.37; 95% CI: 1.03, 10.98). Former drivers also tended to use more inpatient (RR = 1.85; 95% CI: 0.88, 3.87) and emergency services (RR = 1.89; 95% CI: 0.96, 3.70), but results did not reach statistical significance. Total annual healthcare costs in 2005 were almost twice as high for former drivers compared with current drivers ($13,046 vs. $7,054; mean difference = $5,992; 95% CI: -$360, $12,344), although this relationship was not statistically significant (CR = 1.61; 95% CI: 0.88, 2.96).Impact on IndustryFormer drivers were more than three times as likely as current drivers to use mental health services, and tended to use more emergency and inpatient services. Further research on factors that potentially mediate the relationship between driving status and health service use is warranted.     

Adaptive response criteria in road hazard detection among older drivers

Objectives: The majority of existing investigations on attention, aging, and driving have focused on the negative impacts of age-related declines in attention on hazard detection and driver performance. However, driving skills and behavioral compensation may accommodate for the negative effects that age-related attentional decline places on driving performance. In this study, we examined an important question that had been largely neglected in the literature linking attention, aging, and driving: can top-down factors such as behavioral compensation, specifically adaptive response criteria, accommodate the negative impacts from age-related attention declines on hazard detection during driving?

Methods: In the experiment, we used the Drive Aware Task, a task combining the driving context with well-controlled laboratory procedures measuring attention. We compared younger (n = 16, age 21–30) and older (n = 21, age 65–79) drivers on their attentional processing of hazards in driving scenes, indexed by percentage of correct responses and reaction time of hazard detection, as well as sensitivity and response criteria using signal detection analysis.

Results: Older drivers, in general, were less accurate and slower on the task than younger drivers. However, results from this experiment revealed that older, but not younger, drivers adapted their response criteria when the traffic condition changed in the driving scenes. When there was more traffic in the driving scene, older drivers became more liberal in their responses, meaning that they were more likely to report that a driving hazard was detected.

Conclusions: Older drivers adopt compensatory strategies for hazard detection during driving. Our findings showed that, in the driving context, even at an older age our attentional functions are still adaptive according to environmental conditions. This leads to considerations on potential training methods to promote adaptive strategies that may help older drivers maintain performance in road hazard detection.     

Individual difference factors in risky driving among older adults

INTRODUCTION: Motor-vehicle crashes kill roughly 4,500 American adults over the age of 75 annually. Among younger adults, one behavioral factor consistently linked to risky driving is personality, but this predictor has been overshadowed by research on cognitive, perceptual, and motor processes among older drivers. METHOD: In this study, a sample of 101 licensed drivers, all age 75 and over, were recruited to complete self-report measures on personality, temperament, and driving history. Participants also completed a virtual environment (VE) course designed to assess risk-taking driving behavior. State records of motor-vehicle crashes were collected. RESULTS: Results suggest both a sensation-seeking personality and an undercontrolled temperament are related to risky driving among older adults. Sensation-seeking was particularly related to history of violations and tickets, while temperamental control was more broadly related to a number of risky driving measures. Methodological and crash prevention issues are discussed.     

Increasing self-awareness among older drivers: The role of self-screening

Introduction

Self-screening by older drivers has shown considerable promise for increasing self-awareness about functional abilities associated with safe driving. The purpose of this study was to improve upon existing self-screening instruments by focusing entirely on “health concerns” that affect driving - that is, the symptoms that people experience due to medical conditions and the medications used to treat them - rather than the medical conditions or medications themselves.

Method

A computer-based, easy-to-use self-screening instrument for older drivers was developed to provide individualized feedback intended to increase self-awareness about declines in driving-related abilities, as well as suggestions for behavioral changes or safety tips to maintain safe driving, further evaluation from a physician/health professional, and vehicle modifications to help compensate for driving-related declines.

Results

This paper describes the development of the self-screening instrument and summarizes findings relative to increasing self-awareness among older drivers. This research represents an important first step in improving self-awareness among older drivers through self-screening.     

Using a driving simulator to identify older drivers at inflated risk of motor vehicle crashes

PROBLEM: To develop appropriate assessment criteria to measure the performance of older drivers using an interactive PC-based driving simulator, and to determine which measures were associated with the occurrence of motor-vehicle crash. METHOD: One hundred and twenty-nine older drivers residing in a metropolitan city volunteered to participate in this retrospective cohort study. Using the driving simulator, appropriate driving tasks were devised to test the older drivers, whose performances were assessed by 10 reliable assessment criteria. Logistic regression analysis was then undertaken to determine those criteria that influence the self-reported crash outcome. RESULTS: As expected, driving skill of older drivers was found to decline with age. Over 60% of the sample participants reported having at least one motor-vehicle crash during the past year. Adjusting for age in a logistic regression analysis, the cognitive abilities associated with the crash occurrence were working memory, decision making under pressure of time, and confidence in driving at high speed. SUMMARY: The findings of this retrospective study indicated those individuals at inflated risk of vehicle crashes could be identified using the PC-based interactive driving simulator. Prospective studies need to be undertaken to determine whether the driving simulator can predict future crash events. IMPACT ON INDUSTRY: This study demonstrated an economical driving simulator approach to screen out problematic or unsafe older drivers before a more detailed but expensive road test is considered.     

Sleepy driving and risk of obstructive sleep apnea among truck drivers in Saudi Arabia

Objective: Truck drivers represent a group at a particularly higher risk of motor vehicle accidents (MVAs). Sleepy driving and obstructive sleep apnea (OSA) among truck drivers are major risk factors for MVAs. No study has assessed the prevalence of sleepy driving and risk of OSA among truck drivers in Saudi Arabia. Therefore, this study aimed to assess sleepy driving and risk of OSA among these truck drivers.

Methods: This study included 338 male truck drivers working in Saudi Arabia. A validated questionnaire regarding sleepy driving and OSA was used. The questionnaire included sociodemographic assessment, the Epworth Sleepiness Scale (ESS), the Berlin Questionnaire (BQ), and driving-related items.

Results: The drivers had a mean age of 42.9?±?9.7 years. The majority (94.7%) drove more than 5?h a day. A history of MVAs during the last 6 months was reported by 6.5%. Approximately 95% of the participants reported that they had accidentally fallen asleep at least once while driving over the past 6 months, and 49.7% stated that this had happened more than 5 times during the last 6 months. Based on the BQ score, a high risk of OSA was detected in 29% of the drivers. “Not getting good-quality sleep” (odds ratio [OR]?=?2.89; 95% confidence interval [CI], 1.08–7.75; P = .014) and driving experience from 6 to 10 years (OR = 3.37; 95% CI, 1.28–8.91; P = .034) were the only independent predictors of MVAs in the past 6 months.

Conclusions: Sleepy driving and a high risk of OSA was prevalent among the study population of male truck drivers in Saudi Arabia. Not getting good-quality sleep and driving experience from 6 to 10 years contributes to the accident risk among these truck drivers.     

Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study

Problem: Previous research have focused extensively on crashes, however near crashes provide additional data on driver errors leading to critical events as well as evasive maneuvers employed to avoid crashes. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive data on real world driving and offers a reliable methodology to study near crashes. The current study utilized the SHRP2 database to compare the rate and characteristics associated with near crashes among risky drivers. Methods: A subset from the SHRP2 database consisting of 4,818 near crashes for teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers was used. Near crashes were classified into seven incident types: rear-end, road departure, intersection, head-on, side-swipe, pedestrian/cyclist, and animal. Near crash rates, incident type, secondary tasks, and evasive maneuvers were compared across age groups. For rear-end near crashes, near crash severity, max deceleration, and time-to-collision at braking were compared across age. Results: Near crash rates significantly decreased with increasing age (p < 0.05). Young drivers exhibited greater rear-end (p < 0.05) and road departure (p < 0.05) near crashes compared to adult and older drivers. Intersection near crashes were the most common incident type among older drivers. Evasive maneuver type did not significantly vary across age groups. Near crashes exhibited a longer time-to-collision at braking (p < 0.01) compared to crashes. Summary: These data demonstrate increased total near crash rates among young drivers relative to adult and older drivers. Prevalence of specific near crash types also differed across age groups. Timely execution of evasive maneuvers was a distinguishing factor between crashes or near crashes. Practical Applications: These data can be used to develop more targeted driver training programs and help OEMs optimize ADAS to address the most common errors exhibited by risky drivers.     

Classifying and predicting risky driving among novice drivers: A group-based trajectory approach

Introduction: Classifying risky driving among new teenage drivers is important for efficiently targeting driving interventions. We thoroughly investigated whether novice drivers can be clustered by their driving outcome profiles over time. Methods: A sample of 51 newly licensed teen drivers was recruited and followed over a period of 20 weeks. An in-vehicle video recording system was used to gather data on dangerous driving events referred to as DDEs (elevated g-force, near-crash, and crash events), risky driving behaviors referred to as RDBs (e.g., running stop signs, cell phone use while driving), and miles traveled. The DDE and RDB weekly rates rate were determined by dividing the number of DDEs and RDBs in a week by the number of miles traveled in that week, respectively. Group-based trajectory modeling was used to map the clustering of DDE rate and RDB rate patterns over time and their associated covariates. Results: Two distinct DDE rate patterns were found. The first group (69.1% of the study population) had a lower DDE rate which was consistent over time. The second had a higher DDE rate pattern (30.9%) and characterized by a rising trend in DDE rate followed by a steady decrease (inverted U-shaped pattern). Two RDB rate patterns were also identified: a lower RDB rate pattern (83.4% of the study population) and a higher RDB rate pattern (16.6%). RDB and DDE rate patterns were positively related, and therefore, co-occurred. The results also showed that males were more likely than females to be in the higher DDE and RDB rate patterns. Conclusion: The groups identified by trajectory models may be useful for targeting driving interventions to teens that would mostly benefit as the different trajectories may represent different crash risk levels. Practical applications: Parents using feedback devices to monitor the driving performance of their teens can use the initial weeks of independent driving to classify their teens as low or high-risk drivers. Teens making a very few DDEs during their early weeks of independent driving are likely to remain in the lower risk group over time and can be spared from monitoring and interventions. However, teens making many DDEs during their initial weeks of unsupervised driving are likely to continue to make even more DDEs and would require careful monitoring and targeted interventions.     

Identifying risky drivers with simulated driving

Objective: The present study aimed to examine whether high-risk drivers differ from low-risk drivers in driving behavior in a simulated environment.

Method: The 2 risk groups including 36 drivers (18 males and 18 females) performed driving tasks in a simulated environment. The simulated driving behaviors are compared between the 2 risk groups.

Results: The high-risk drivers drove much faster and exhibited larger offsets of the steering wheel than did the low-risk drivers in events without incidents. Additionally, the high-risk drivers used turn signals and horns less frequently than the low-risk drivers.

Conclusions: The present study revealed that the high-risk group differed from the low-risk group in driving behavior in a simulated environment. These results also suggest that simulated driving tasks might be useful tools for the evaluation of drivers’ potential risks.     

Changes in the sources of distracted driving among Northern Virginia drivers in 2014 and 2018: A comparison of results from two roadside observation surveys

Introduction: An increase in distracted driving has been suggested as a factor contributing to the 15% increase in fatal crashes from 2014 to 2016, but objective information about the prevalence of distracted driving in recent years is incomplete or lacking. The current study replicated a 2014 observation study conducted in Northern Virginia to examine whether the prevalence of distracted driving overall and of individual secondary behaviors has changed. Method: Drivers of moving or stopped vehicles were observed at 12 locations across 4 Northern Virginia communities during the daytime. The presence of 12 different secondary behaviors was recorded. Results: In 2018, about 23% of drivers were engaged in at least one secondary behavior, which was not significantly different from 2014. Overall phone use was not significantly different between 2014 and 2018. However, the likelihood of holding a cellphone significantly decreased while the likelihood of manipulating a cellphone significantly increased in 2018 relative to 2014. About 14% of drivers were engaged in noncellphone secondary behaviors in 2014 and 2018, which exceeded the proportion using phones in both years. Conclusions: There was no evidence that distracted driving has become more common in recent years, but the prevalence of some secondary behaviors has changed. Most concerning was the 57% increase in the likelihood of cellphone manipulation in 2018 relative to 2014, a behavior that has been consistently linked to increased crash risk; however, because the behavior is uncommon overall, the increased prevalence would be expected to only slightly increase crash rates. Practical applications: Although cellphone use was frequently observed in 2014 and 2018, collectively, other noncellphone secondary behaviors were more prevalent. Practitioners and policymakers should continue targeting cellphone use, but also must target other common secondary behaviors to fully address distracted driving.     

Distracting behaviors among teenagers and young,middle-aged,and older adult drivers when driving without and with warnings from an integrated vehicle safety system

IntroductionNegative reinforcement from crash warnings may reduce the likelihood that drivers engage in distracted driving. Alternatively, drivers may compensate for the perceived safety benefit of crash warnings by engaging in distractions more frequently, especially at higher speeds. The purpose of this study was to examine whether warning feedback from an integrated vehicle-based safety system affected the likelihood that various secondary behaviors were present among drivers ages 16–17, 20–30, 40–50, and 60–70.MethodParticipants drove an instrumented sedan with various collision warning systems for an extended period. Ten 5-second video clips were randomly sampled from driving periods at speeds above 25 mph and below 5 mph each week for each driver and coded for the presence of 11 secondary behaviors.ResultsAt least one secondary behavior was present in 46% of video clips; conversing with a passenger (17%), personal grooming (9%), and cellphone conversation (6%) were the most common. The likelihood that at least one secondary behavior was present was not significantly different during periods when drivers received warnings relative to periods without warnings. At least one secondary behavior was 21% more likely to be present at speeds below 5 mph relative to speeds above 25 mph; however, the effect of vehicle speed was not significantly affected by warning presence. Separate models for each of the five most common secondary behaviors also indicated that warnings had no significant effect on the likelihood that each behavior was present.ConclusionsCollision warnings were not associated with significant increases or decreases in the overall likelihood that teen and adult drivers engaged in secondary behaviors or the likelihood of the behaviors at speeds above 25 mph or below 5 mph.Practical applicationsThere was no evidence that forward collision warning and other technologies like those in this study will increase or decrease distracted driving.     

Automated vehicles and older drivers in Canada

Introduction: As seniors represent a growing proportion of the driving population, research about how automated vehicles can help improve older driver safety and mobility is highly relevant. This paper examines the knowledge, attitudes and perceptions of older drivers towards limited self-driving vehicles (LSDVs), and how these variables can influence the likelihood that they will rely on this technology. Method: The study includes data from a previous national survey (N = 2662) about automated vehicle technology, with new analyses to test hypothetical models using structural equation modeling. Results of the first model were confirmed and built upon with a second more complex model that incorporated the construct “behavioral adaptation.” Focus groups with older drivers were also conducted (N = 38) to help reveal nuances in older drivers' knowledge, attitudes, perceptions, and behaviors regarding this technology. Results: Survey results demonstrated that feelings of safety and knowledge about LSDVs are positively related to perceived ease of use and adoption of the technology. The positive association between safety and perceived ease of use was further highlighted when comparing responses of older drivers to those of younger age groups, as older drivers were significantly less likely to agree that LSDVs were easy to use and were significantly less agreeable about feeling safe using them. Focus groups results confirmed that safety and knowledge of LSDVs are essential to the likelihood of adopting this technology, and revealed a high receptivity among older drivers to educational strategies and tools to increase their knowledge of LSDVs. Implications for educational strategies and safety benefits for older drivers are discussed. Practical applications: Results provide insight into strategies to encourage the early adoption of automated vehicles by older drivers and facilitate a safer transition towards automated vehicles that is lead by a cohort of safety-conscious drivers.     

Views of US drivers about driving safety

Problem: To assess how drivers view dangers on the highway, what motivates them to drive safely, how they say they reduce their crash and injury risk, and how they rate their own driving skills. Results: Most drivers rated their skills as better than average. The biggest motivating factor for safe driving was concern for safety of others in their vehicle, followed by negative outcomes such as being in a crash, increased insurance costs, and fines. The greatest threats to their safety were thought to be other drivers' actions that increase crash risk such as alcohol impairment or running red lights. In terms of reducing crashes and injuries, drivers tended to focus on actions they could take such as driving defensively or using seat belts. There was less recognition of the role of vehicles and vehicle features in crash or injury prevention. Impact on research, practice, and policy: Knowing how drivers view themselves and others, their concerns, and their motivations and techniques for staying out of trouble on the roads provides insight into the difficulty of changing driving practices.     

Identifying compensatory driving behavior among older adults using the situational avoidance questionnaire

IntroductionDriving self-regulation is considered a means through which older drivers can compensate for perceived declines in driving skill or more general feelings of discomfort on the road. One form of driving self-regulation is situational avoidance, the purposeful avoidance of situations perceived as challenging or potentially hazardous. This study aimed to validate the Situational Avoidance Questionnaire (SAQ, Davis, Conlon, Ownsworth, & Morrissey, 2016) and identify the point on the scale at which drivers practicing compensatory avoidance behavior could be distinguished from those whose driving is unrestricted, or who are avoiding situations for other, non-compensatory reasons (e.g., time or convenience).MethodSeventy-nine Australian drivers (M_age = 71.48, SD = 7.16, range: 55 to 86 years) completed the SAQ and were classified as a compensatory-restricted or a non-restricted driver based on a semi-structured interview designed to assess the motivations underlying avoidance behavior reported on the SAQ.ResultsUsing receiver-operator characteristic (ROC) analysis, the SAQ was found to have high diagnostic accuracy (sensitivity: 85%, specificity: 82%) in correctly classifying the driver groups. Group comparisons confirmed that compensatory-restricted drivers were self-regulating their driving behavior to reduce the perceived demands of the driving task. This group had, on average, slower hazard perception reaction times, and reported greater difficulty with driving, more discomfort when driving due to difficulty with hazard perception skills, and greater changes in cognition over the past five years.ConclusionsThe SAQ is a psychometrically sound measure of situational avoidance for drivers in baby boomer and older adult generations.Practical applicationsUse of validated measures of driving self-regulation that distinguish between compensatory and non-compensatory behavior, such as the SAQ, will advance our understanding of the driving self-regulation construct and its potential safety benefits for older road users.     

Personality,attitudes and risk perception as predictors of risky driving behaviour among young drivers

Within psychology, different research traditions have attempted to explain individual differences in risky driving behaviour and traffic accident involvement. The present study attempts to integrate two of these research traditions, the personality trait approach and the social cognition approach, in order to understand the mechanisms underlying young drivers' risk-taking behaviour in traffic. The study was based on a self-completion questionnaire survey carried out among 1932 adolescents in Norway. The questionnaire included measures of risk perception, attitudes towards traffic safety and self-reported risk-taking in traffic. Personality measures included aggression, altruism, anxiety and normlessness. The results of a structural equation model suggested that the relation between the personality traits and risky driving behaviour was mediated through attitudes. On this basis it was concluded that personality primarily influences risky driving behaviour indirectly through affecting the attitudinal determinants of the behaviour. Practical implications for traffic safety campaigns are also discussed.