Individual difference factors in risky driving: the roles of anger/hostility, conscientiousness, and sensation-seeking

Motor vehicle crashes claim the lives of more Americans than any other cause of injury. One factor long recognized as relevant to predicting dangerous driver behavior is the driver's personality. This study examines the independent and combined roles of three personality traits--sensation-seeking, conscientiousness, and anger/hostility--in predicting risky driving behavior. Seventy-three participants completed personality and driving history questionnaires, and also engaged in a virtual environment (VE) task designed to assess risk-taking driving behavior. Each facet of personality was correlated to risky driving behavior in independent univariate analyses. In multivariate analyses, sensation-seeking emerged as the best predictor of self-reported driving violations. Anger/hostility and the interactive effect of anger/hostility by sensation-seeking also emerged in a multivariate analysis predicting one measure of self-reported driving violations. No personality trait predicted risky driving in the VE in multivariate analyses. Results are discussed with respect to previous work in the field, challenges involved in measuring the constructs of interest, and implications to prevention.     

Development of a short form of the driving anger expression inventory

The present study developed a revised version of the driving anger expression inventory (25-items) and a short (15-item) version using data from 551 drivers. Split half factor analyses on both versions confirmed the original four factors; personal physical aggressive expression, use of a vehicle to express anger, verbal aggressive expression and adaptive/constructive expression. The two DAX versions were strongly correlated, demonstrating the suitability of both forms of the scale and the aggressive forms of expression were higher for drivers who reported initiating road rage interactions. Total aggressive expression was also higher for drivers who reported recent crash-related conditions, such as: loss of concentration, losing control of their vehicle, moving violations, near-misses and major crashes. The revised DAX and DAX-short provide shorter versions of the 49-item DAX that can more easily be combined with other questionnaires and require smaller sample sizes to analyse. Further research is required to validate these tools among different samples and populations.     

Forgiveness and consideration of future consequences in aggressive driving

Most research on aggressive driving has focused on identifying aspects of driver personality which will exacerbate it (e.g., sensation seeking, impulsiveness, driving anger, etc.). The present study was designed to examine two theoretically relevant but previously unexplored personality factors predicted to reduce the risk of aggressive driving: trait forgiveness and consideration of future consequences. The utility of these variables in predicting aggressive driving and driving anger expression was evaluated among 316 college student volunteers. Hierarchical multiple regressions permitted an analysis of the incremental validity of these constructs beyond respondent gender, age, miles driven per week, and driving anger. Both forgiveness and consideration of future consequences contributed to the prediction of aggressive driving and driving anger expression, independent of driving anger. Research on aggressive driving may be enhanced by greater attention to adaptive, potentially risk-reducing traits. Moreover, forgiveness and consideration of future consequences may have implications for accident prevention.     

Driving anger,emotional and instrumental aggressiveness,and impulsiveness in the prediction of aggressive and transgressive driving

The present study investigates the potential contribution of three predictors of aggressive and transgressive behaviors on the road: driving anger, impulsiveness and aggressiveness. A total of 455 participants (laypersons), of all age and gender, filled self-reported measures evaluating driving anger, impulsiveness, two forms of aggressiveness (instrumental and emotional forms), driving behaviors and aggressive and transgressive behaviors. Main results indicate: (1) a significant gender effect for almost all variables; (2) gender was involved in the prediction of Lapses and Errors; (3) driving anger, impulsiveness and aggressiveness were involved in a complementary manner in the prediction of aggressive and transgressive driving; (4) Aggressiveness and Impeded Progress were the best predictors of violations and aggressive violations. Results support that transgressive driving behaviors are relevant indicators of aggressive driving.     

Forgivingness,anger, and hostility in aggressive driving

This study was aimed at investigating the relationship between trait forgivingness, general anger, hostility, driving anger, and self-reported aggressive driving committed by the driver him/herself (“self” scale) and perceiving him/herself as an object of other drivers’ aggressive acts (“other” scale). The Slovak version of questionnaires was administrated to a sample of 612 Slovak and Czech drivers. First, the factor structure of the Driver Anger Indicators Scale (DAIS) was investigated. Factor analyses of the self and other parts of the DAIS resulted in two factors, which were named as aggressive warnings and hostile aggression and revenge. Next, the results showed that from all dependent variables (scales of the DAIS), self-reported aggressive warnings (self) on the road were predicted best by chosen person-related factors. The path model for aggressive warnings (self) suggested that trait forgivingness and general anger were fully mediated by driving anger whereas hostility proved to be a unique predictor of aggressive behavior in traffic. Driving anger was found to be the best predictor of perceptions that other drivers behave aggressively.     

On-the-road driving records of licensed race drivers

On-the-road driving records of Sports Car Club of America national competition license holders from three states were compared with the records of other drivers of the same age and sex. In each state the race drivers had a greater number of crashes per driver, and a greater number of speeding violations, other moving violations, and non-moving violations per driver, than the drivers comprising the matched comparison group. The results cast considerable doubt as to the validity of the Master Driver's License concept, which has been given serious consideration by the National Highway Traffic Safety Administration. The results also suggest a need for caution regarding the assumption that advanced driver education crash-avoidance techniques can he translated into reduced crash experience.     

Risky driving related to driver and vehicle characteristics

This investigation was performed to determine relationships between driver and vehicle characteristics and risk taking in everyday driving, as measured by close following in freeway traffic. A total of 12,000 observations were made of headways (time intervals between successive vehicles) in high flow freely moving freeway traffic at two sites, one in Michigan, the other in Toronto, Canada. The headways were measured using a photographic technique which allowed vehicle and occupant characteristics to be recorded, including type of vehicle and sex and seat belt use. For the Michigan observations, the vehicle license number was recorded, from which additional information on the vehicle and owner (usually the driver) was obtained from state files, including the vehicle mass and model year and the owner's age. sex and history of recorded accidents and traffic violations. Shorter headways, corresponding to higher risk, were found for drivers with prior accidents or violations, young drivers, male drivers, drivers with no passengers. and drivers who did not wear a seat belt. Vehicles associated with shorter headways included newer cars and cars of intermediate mass (1600–1900 kg).     

Personality and attitudes as predictors of risky driving among older drivers

Although there are several studies on the effects of personality and attitudes on risky driving among young drivers, related research in older drivers is scarce. The present study assessed a model of personality-attitudes-risky driving in a large sample of active older drivers. A cross-sectional design was used, and structured and anonymous questionnaires were completed by 485 older Italian drivers (Mean age = 68.1, SD = 6.2, 61.2% males). The measures included personality traits, attitudes toward traffic safety, risky driving (errors, lapses, and traffic violations), and self-reported crash involvement and number of issued traffic tickets in the last 12 months. Structural equation modeling showed that personality traits predicted both directly and indirectly traffic violations, errors, and lapses. More positive attitudes toward traffic safety negatively predicted risky driving. In turn, risky driving was positively related to self-reported crash involvement and higher number of issued traffic tickets. Our findings suggest that theoretical models developed to account for risky driving of younger drivers may also apply in the older drivers, and accordingly be used to inform safe driving interventions for this age group.     

An instrumented vehicle assessment of problem behavior and driving style: do younger males really take more risks?

An instrumented vehicle was used to obtain behavioral data from 61 drivers ranging in age from 18 to 82. Each driver completed a personality questionnaire and participated in a study described as an evaluation of cognitive mapping and way-finding abilities. An evaluation of relationships between age, personality and driving style revealed that driver age and type A personality characteristics were significant predictors of vehicle speed and following distance, P < 0.05. However, contrary to the earlier research, which relies heavily on a self-reported driving criterion, no significant gender differences were obtained. A factor analysis of several at-risk driving behaviors identified a cluster of correlated driving behaviors that appeared to share a common characteristic identified as aggressive/impatient driving. It is suggested that the correlated cluster of driving behavior provide objective support for the assumptions of response generalization and problem behavior theory. Results are discussed with regard to implications for safe driving interventions and a problem behavior syndrome.     

Statistical implications of using moving violations to determine crash responsibility in young driver crashes

Traditional methods for determining crash responsibility – most commonly moving violation citations – may not accurately characterize at-fault status among crash-involved drivers given that: (1) issuance may vary by factors that are independent of fault (e.g., driver age, gender), and (2) these methods do not capture driver behaviors that are not illegal but still indicative of fault. We examined the statistical implications of using moving violations to determine crash responsibility in young driver crashes by comparing it with a method based on crash-contributing driver actions. We selected all drivers in police-reported passenger-vehicle crashes (2010–2011) that involved a New Jersey driver <21 years old (79,485 drivers < age 21, 61,355 drivers ≥ age 21). For each driver, crash responsibility was determined from the crash report using two alternative methods: (1) issuance of a moving violation citation; and (2) presence of a driver action (e.g., failure to yield, inattention). Overall, 18% of crash-involved drivers were issued a moving violation while 50% had a driver action. Only 32.2% of drivers with a driver action were cited for a moving violation. Further, the likelihood of being cited given the presence of a driver action was higher among certain driver subgroups—younger drivers, male drivers, and drivers in single-vehicle and more severe crashes. Specifically among young drivers, those driving at night, carrying peer passengers, and having a suspended or no license were more often cited. Conversely, fatally-injured drivers were almost never cited. We also demonstrated that using citation data may lead to statistical bias in the characterization of at-fault drivers and of quasi-induced exposure measures. Studies seeking to accurately determine crash responsibility should thoughtfully consider the potential sources of bias that may result from using legal culpability methods. For many studies, determining driver responsibility via the identification of driver actions may yield more accurate characterizations of at-fault drivers.     

Prevalence and characteristics of red light running crashes in the United States

About 40% of motor vehicle crashes occur at intersections. In recent years, the number of crashes at traffic signals has increased considerably. A major cause of such crashes is drivers disregarding traffic signals. Despite concerns about the frequent occurrence of red light violations and the significant crash consequences, relatively little is known about the overall prevalence and characteristics of red light running crashes. The present study examines the prevalence of red light running crashes on a national basis and identifies the characteristics of such crashes and the drivers involved. Cities with especially high rates of fatal red light running crashes are identified. Countermeasures to reduce red light running crashes based on collision patterns and characteristics of drivers involved are discussed. It was estimated that about 260 000 red light running crashes occur annually in the United States, of which approximately 750 result in fatalities. Comparisons were made between red light running drivers and drivers deemed not to have run red lights in these same crashes. As a group, red light runners were more likely than other drivers to be younger than age 30, male, have prior moving violations and convictions for driving while intoxicated, have invalid driver’s licenses, and have consumed alcohol prior to the crash. Comparisons also were made between characteristics of red light runners involved in daytime and nighttime crashes. Nighttime red light runners were more likely than daytime runners to be young, male, and have more deviant characteristics, 53% having high blood alcohol concentrations.     

The effect of stress and personality on dangerous driving behavior among Chinese drivers

The relationship between stress and road safety has been studied for many years, but the effect of global stress and its joint effect with personality on driving behavior have received little attention in previous studies. This study aimed to elucidate the impact of global stress and various personality traits on driving behavior. 242 drivers completed the Perceived Stress Scale-10 (PSS-10), the Dula Dangerous Driving Index (DDDI), and several personality trait scales related to anger, sensation seeking, and altruism. The results showed that perceived stress and sensation seeking were significantly correlated with the four subcategories of dangerous driving behavior, namely, negative cognitive/emotional driving (NCED), aggressive driving (AD), risky driving (RD), and drunk driving (DD). Moreover, anger was positively correlated with negative cognitive/emotional driving, aggressive driving, and risky driving, and altruism was negatively correlated with aggressive driving and drunk driving. Hierarchical multiple regressions were applied to analyze the mediating effect of personality traits, and the results showed that anger mediated the relationship between stress and dangerous driving behavior and that this mediating role was especially strong for negative cognitive/emotional driving and aggressive driving. Collectively, the results showed that stress is an important factor that can affect people's driving behavior but that personality traits mediate the effect of stress on driving behavior. The findings from this study regarding the relationship among stress, anger, and dangerous driving behavior could be applied in the development of intervention programs for stress and anger management in order to improve drivers’ ability to manage emotional thoughts and adjust their behavior on the road.     

Evaluating a model linking assessed parent factors to four domains of youth risky driving

Motor vehicle collisions are the leading cause of death in youth aged 15–19. Research has consistently shown that driver education programs do not result in safer youth driving. Indeed, the biggest predictor of collisions involving youth is parental history of collisions. The current study examined how parental modeling of and teaching about risky driving behaviors related to youth practices within four domains of risky driving (aggressive, substance use, distracted, moving violations), and evaluated whether the Prototype-Willingness Model explains links from parent to teen driving practices. Participants (N = 432) were undergraduate students (mean age 18 years, age range 17–22 years) who had obtained their G2 driver's license within the past year; the G2 driver's license allows youth to drive alone on all municipal roads, with some restrictions on their blood alcohol level and the number of passengers they can carry. Results revealed that parental modeling was more predictive than parental teaching for all domains of risky driving examined. Youth whose parents modeled risky driving behaviors were found to be more likely to have engaged in those risky driving behaviors in the past, as well as to be more willing to engage in the behaviors in the future. The Prototype-Willingness Model was not a good fit to explain these relations. Findings from this study highlight the role parents play in the development of youth risky driving practices.     

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.     

Modeling young driver motor vehicle crashes: data with extra zeros

Much of the data collected on motor vehicle crashes is count data. The standard Poisson regression approach used to model this type of data does not take into account the fact there are few crash events and hence, many observed zeros. In this paper, we applied the zero-inflated Poisson (ZIP) model (which adjusts for the many observed zeros) and the negative binomial (NB) model to analyze young driver motor vehicle crashes. The results of the ZIP regression model are comparable to those from fitting a NB regression model for general over-dispersion. The findings highlight that driver confidence/adventurousness and the frequency of driving prior to licensing are significant predictors of crash outcome in the first 12 months of driving. We encourage researchers, when analyzing motor vehicle crash data, to consider the empirical frequency distribution first and to apply the ZIP and NB models in the presence of extra zeros due, for example, to under-reporting.     

Young novice driver subtypes: Relationship to driving violations, errors and lapses

The present study aimed to identify, in a large Italian sample of young, novice drivers, specific subtypes of drivers on the basis of combinations of self-reported personality traits (i.e., driving anger, anxiety, angry hostility, excitement-seeking, altruism, normlessness and driving locus of control) and to evaluate their high-risk driving behaviors not only in terms of traffic rule violations and risk-taking behaviors, but also in terms of driving errors and lapses as measured by the Manchester Driver Behavior Questionnaire. Participants were 1008 high school students between the ages of 18 and 23 years, with valid driver's licenses. On the basis of a cluster analysis of the personality variables, three easily interpretable driver subgroups were identified (risky drivers, worried drivers and careful drivers) that differed on self-reported accident involvement, attitudes toward traffic safety and risk perception, as well as on driving violations, errors, and lapses. The inclusion of internal and external driving locus of control, variables not previously considered in similar cluster studies, provided a relevant contribution to the final cluster solution. Further, the use of the Driving Behavior Questionnaire permitted the differentiation between deliberate deviations from safe driving practices and mistakes due to misjudgments or lapses in attention. This distinction was critical for understanding the behavior of each of the three identified subgroups of drivers, and for planning interventions to promote safe driving.     

High school driver education: further evaluation of the DeKalb County study

The DeKalb County, Georgia, study is the largest experimental evaluation of high school driver education to date. Detailed reanalyses of data from that study reveal that students assigned to an enhanced driver education program (Safe Performance Curriculum) were more likely to obtain drivers licenses, to be in car crashes and to have traffic violations than control students not assigned to driver education. Students assigned to a minimal program of high school driver education (Pre-Driver Licensing Curriculum) were also more likely than control students to obtain licenses, but the difference was smaller than for the enhanced program and they were not significantly more likely to be in crashes or to have violations. Students in the more limited course were less likely than those in the enhanced course to complete the course in a timely manner, which suggests that exposure differences may explain differences between the two groups. The results confirm that greater availability of driver education causes students to become licensed sooner, although the DeKalb study probably underestimates the effect. Because of this greater exposure, crashes and violations are incurred at an earlier age.     

Investigating driver injury severity patterns in rollover crashes using support vector machine models

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.     

The expression of anger amongst Turkish taxi drivers

The present study tested the four factor structure of the DAX on a sample of Turkish taxi drivers and the relationship these factors had with a number of other variables. Confirmatory Factor Analysis found that the data broadly fit the four factor solution of the DAX. These factors included three aggressive expressions: Verbal Aggressive Expression; Personal Physical Aggressive Expression; Use of a Vehicle to Express anger, and one Adaptive/Constructive factor. Driving experience was negatively related to the three types of aggressive expression. The Total Aggressive Expression was positively related to annual mileage and preferred driving speed, but negatively related to age and experience. The present research also found that the three aggressive types of anger expression were significantly related to potentially crash related conditions, such as losing control of the vehicle, loss of concentration and near-misses. However, none of the DAX factors was significantly related to either minor or major crashes.     

A review of the literature evaluating the defensive driving course

The Defensive Driving Course (DDC), like other post-licensure driver training programs, is intended to reduce the rate of motor vehicle crashes among those who take the course. A review of the literature revealed 14 controlled studies of the effects of DDC. About a third of these studies provided methodologically strong tests of DDC, but the remainder had design flaws that made their findings questionable or inadequate as tests of DDC. Only among the flawed tests were there large, positive effects of DDC. In the methodologically strong tests, DDC had no consistent effect on crashes but did decrease the frequency of traffic violations by about 10%. The failure of violation reductions to be translated into crash reductions may indicate that the violation reduction is an artifact of traffic record procedures or that the changes in driver behavior, if real, were insufficient to modify individual crash likelihoods. In either event, the best available evidence does not support the hypothesis that DDC decreases the likelihood of motor vehicle crashes.