Understanding Aggressive Driving: The Role of Personality and Individual Differences

The goal of the present study was to examine the role of personality and individual differences on aggressive driving. It was hypothesized that personality and individual differences would be significantly related to aggressive driving behavior. A sample of n = 252 participants from a southeastern university and surrounding community were required to complete a series of driving questionnaires; the ADBQ, DBQ, and CFQ-D; and a series of personality questionnaires; the IPIP-NEO-PIR and BFI. Our results indicated that personality factors and individual differences significantly predicted aggressive driving outcomes. These results provided a preliminary personality based characteristic profile of the aggressive driver. These results also support the use of trait anger and trait cooperation independently from the subscales they are derived from (Neuroticism and Agreeableness) to predict aggressive driving behaviors. Theoretical and practical implications are discussed.

THE PROFILE OF THE ROMANIAN AGGRESSIVE DRIVER

Various studies have documented that aggressive driving is indeed a real problem. In each country there are various aspects of dangerous driving of empirical and practical concern and there are also individual differences to be explored. The present study aims at profiling the Romanian aggressive driver and questioning whether there are differences according to demographic variables such as: gender, age, area of living, marital status, religion, socio-economic status and level of instruction. An educational purpose may be nevertheless included. If psychologist may be provided with the profile of psychological driver and the predisposition of some to risky drivind according to age, marital status, religion, area of living and other demographic variables, they may shorten the time spent for evaluation and recommend counseling sessions for anger management for those identified with risky driving behavior. Nevetheless, other sound measures of dangerous driving are needed to understand differences and commonalties between aggression, negative cognitive/emotional driving, and risky driving. The study presents the DDDI results that might help psychologist in evaluating some variables that are part of the profile of the aggressive driver in Romania; we used it as a psychometric screening tool to select individuals who are prone to dangerous driving styles and who could benefit from sketching a cognitive-behaviour therapy (CBT)-type therapeutic intervention, at least in Romania. The educational implication of this study are that such types of interventions as cognitive-behavioral interventions (e.g., relaxation, cognitive restructuring, and behavioral skill building) may be suggested after testing the drivers in order to reduce and maintain reductions of driving anger, aggressive anger expression, aggression, risky behavior, and general anger

Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting

Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. First, a surrogate measurement of collision risk, called Average Crash Risk (ACR), is proposed to calculate a vehicle’s crash risk. Second, the driver’s driving aggressiveness is determined by his/her ACR with three anomaly detection methods. Third, we train classification models to identify aggressive drivers using partial trajectory data. Three imbalanced class boosting algorithms, SMOTEBoost, RUSBoost, and CUSBoost, are compared with cost-sensitive AdaBoost and cost-sensitive XGBoost. Additionally, we try two resampling techniques with AdaBoost and XGBoost. Among all algorithms tested, CUSBoost achieves the highest or the second-highest Area Under Precision-Recall Curve (AUPRC) in most datasets. We find the discrete Fourier coefficients of gap as the key feature to identify aggressive drivers.

Experimental Derivation of Models of Human Drivers Executing Emergency Steering Maneuvers

Tests with human drivers in a driving simulator are used for developing dynamic models of drivers during emergency highway steering maneuvers. The maneuvers were required to avoid colliding with suddenly emerging simulated lane-blocking vehicles and objects. The driver models are intended for forming vehicle-driver simulation models that are used in analysis of highway accidents caused by lane obstructions. Four standard driver models are derived based on the test results, ranging from a cautious driver to an aggressive driver. Test data is provided to allow derivation of additional driver models.

Bicycle Backlash: Qualitative Examination of Aggressive Driver–Bicyclist Interactions

This research investigated aggressive driver–bicyclist interactions. Individuals who identified themselves as both a driver and a bicyclist were asked about their behavior when they encountered a bicyclist on the road while they were driving a car. Open-ended survey responses were analyzed from individuals who reported a propensity for driving too closely to a bicyclist who they felt was not staying to the side of the road. The data were drawn from a snowball-sampled, online survey specifically targeted to elicit responses about rare (i.e., deviant or illegal) behaviors. Little research exists on why individuals would choose to intimidate a bicyclist while they were driving. Applicable theories from sociology and behavioral economics (i.e., theories of crime as social control and as altruistic punishment) were drawn on in this study to help understand why individuals might do so. This paper argues that aggressive driving behavior directed at bicyclists in the sample population could be characterized with two general themes: “teaching them a lesson” and “they had it coming.” In both cases, individuals deflected the blame for their aggressive behavior away from themselves. Instead, they cast themselves as serving a social good by teaching bicyclists how they should behave or by punishing bicyclists for behaving in ways with which the drivers disagreed. The study reported here was an initial step in an effort to identify testable hypotheses through qualitative methods to explain such behaviors and eventually to mitigate them. The intent is to inform actionable directions to address dangerous on-street interactions that act as barriers to a safe transportation system that accommodates all users.