Psycho-social Correlates of Driving Behavior: A Masculinity Perspective

Young male taxi drivers and private car users on driving simulator for their self-reported driving skills and behaviors

The present study examined the predictive utility of constructs specified by the theory of planned behaviour (TPB) and prototype willingness model (PWM) for young and older male drivers' willingness to drive while intoxicated. A cross-sectional questionnaire was employed. Two hundred male drivers, recruited via a street survey, voluntarily completed measures of attitude, subjective norm, perceived behavioural control, prototype perceptions, and willingness. Findings showed that the TPB and PWM variables explained 65% of the variance in young male drivers' willingness and 47% of the variance in older male drivers' willingness, with the interaction between prototype favourability and similarity contributing 7% to the variance explained in older males' willingness to drive while intoxicated. The findings possess implications for theory, research, and anti-drink driving campaigns.

Young male drivers’ risky driving 15 months after licensure – The role of intervention, attitudes towards accompanied driving, and parents’ risk

Lately, a significant rise in traffic fatalities linked to aggressive driving behaviors has been noted, accentuating the imperative need for research in this domain. Hence, the detection of aggressive driving is increasingly advocated as a strategy not only to alert drivers about their perilous behaviors but also to potentially diminish the incidence of accidents. In parallel, the widespread adoption of driving simulators in the automotive sector has unveiled their profound advantages, especially in terms of repeatability in a controlled environment, helping researchers significantly reduce time and cost of development. Furthermore, driving simulators also provide a safe platform for testing new technologies and evaluating driver behavior in various scenarios. The article presents a method for identifying aggressive driving by analyzing vehicle dynamics data (such as speed, acceleration, and steering angle) collected from simulations in an urban setting using the software SCANeR™Studio. The algorithm employs Iterative Density-based spatial clustering of applications with noise, an unsupervised learning technique, to cluster aggressive driving maneuvers with sub-classification in terms of comfort and safety. Further, this labeled data is used to train a Bayesian optimization-based long short-term memory neural network, a pattern recognition model for the detection of driving behavior. The research findings confirm the model’s notable ability to recognize aggressive driving behaviors, as indicated by the confusion matrix and a F-score of 0.869, showing great potential to enhance road safety.

Little is known about the nature and scope of aggressive driving in developing countries. The objective of this study is to specifically examine the sociodemographic factors associated with aggressive driving behavior among 3-wheeler taxi drivers in Sri Lanka. Convenience samples of 3-wheeler taxi drivers from Rathnapura, Ahaliyagoda, Sri Lanka were surveyed from June to August 2006. Analyses included bivariate and multivariate logistic regression. Drivers with less than high school education were 3.5 times more likely to drive aggressively (odds ratio [OR] = 3.46; 95% confidence interval [CI] = 1.08, 11.1). Single drivers were 9 times more likely to run red lights (OR = 8.74; 95% CI = 2.18, 35.0), and being single was a major risk factor for drunk driving (OR = 4.80; 95% CI = 1.23, 18.7). Furthermore, high school completers were 4 times more likely to bribe a policeman (OR = 4.27; 95% CI = 1.23, 14.9) when caught violating the road rules. Aggressive driving and risk-taking behavior are amenable to policy initiatives, and preventive programs targeted at key groups could be used to improve road safety in Sri Lanka. This study demonstrates that aggressive driving behavior is associated with sociodemographic factors, including the level of education, marital status, and other socioeconomic factors. Hence, economic factors should be addressed to find solutions to traffic-related issues. It will be the government's and policy makers' responsibility to try and understand the economic factors behind risky road behavior and bribe-taking behavior prior to legislating or enforcing new laws.

Effect of ADHD traits in young drivers on self-reported deviant driving behaviours: An exploratory study in the Arab gulf region

Based on the velocity effect (VE) model, a cellular automaton (CA) model is proposed with the consideration of timid and aggressive driving behaviors. Numerical simulations are performed to investigate the characteristics of traffic flow. The flux-density curves are plotted. The results show that the maximum flux and the corresponding density decrease as the proportion of timid drivers increases, while they increase as the proportion of aggressive drivers increases. In addition, road capacity is also investigated with the consideration of various driving behaviors. The results show that only a smaller proportion of timid drivers can play an essential role on the road capacity, while it needs much more aggressive drivers to promote the road capacity.

Aggressive driving behavior or “road rage” is often viewed as a dysfunctional pattern of social behavior that poses a serious risk to public safety. In a previous study, we conducted a factor analytic study of 79 items derived from five different driving behavior scales to produce a new scale, which can be correlated to driver anger. The goal of the present research was to empirically test this scale in a controlled laboratory environment. It was hypothesized that the newly developed Aggressive Driving Behavior Questionnaire (ADBQ) would predict driving performance of aggressive drivers. The results showed a significant effect of traffic condition on lane deviations and that the composite scores co-varied significantly with lane deviations and collisions. In addition, several dependent measures were significantly correlated with the composite scores. These variables included lane deviations and collisions. This suggests that the ADBQ is a valid predictor of aggressive driving behavior in a simulated environment.

Aggressive driving behavior is regarded as a social dysfunction that poses a major risk to traffic safety. Previous research has indicated trait mindfulness as a protective factor for maladaptive thoughts and driver anger. The current study was designed to examine the mediating role of trait driver aggressiveness on the relationship between trait mindfulness and propensity for aggressive driving behavior. A sample of 122 drivers responded to a series of online questionnaires that assessed trait mindfulness, trait driver aggressiveness, and propensity for aggressive driving behavior. Results indicated that trait mindfulness was a significant predictor of trait driver aggressiveness. Similarly, trait mindfulness had also a moderating effect on the relationship between propensity for aggressive driving and trait driver aggressiveness. This indicated that those with higher trait mindfulness scores and lower trait driver aggressiveness scores were less likely to engage in aggressive driving behavior. Both theoretical and practical implications are discussed, and directions for future research are presented.

There were 338 road fatalities on Irish roads in 2007. Research in 2007 by the Road Safety Authority in Ireland states that young male drivers (17 – 25 years) are seven times more likely to be killed on Irish roads than other road users. The car driver fatality rate was found to be approximately 10 times higher for young male drivers than for female drivers in 2000. Young male drivers in particular demonstrate a high proclivity for risky driving behaviours. These risky behaviours include drink driving, speeding, drug-driving and engaging in aggressive driving. Speed is the single largest contributing factor to road deaths in Ireland. Approximately 40% of fatal accidents are caused by excessive or inappropriate speed. This study focuses on how dangerous driving behaviours may be addressed through social marketing. This study analyses the appropriate level of fear that needs to be induced in order to change young male driving behaviour.

In-vehicle technology is increasingly being implemented to assist drivers. These technologies could improve driver safety or, conversely, introduce distractions that reduce safety. The purpose of this study was to evaluate the effectiveness of a newly developed, commercially available road safety device (Otto Driving Companion, Persen Technologies Inc.) that provides drivers with auditory alerts based on position and velocity data acquired via the Global Positioning System. Auditory alarms warn of situations such as speeding, crosswalks, and red light cameras. To study emergency braking situations, simulated driving was used (STISIM Driving Simulator). Younger male drivers (30 to 50 years old, n = 16) and older male drivers (70+, n = 14) participated in two sessions. In the first session, they were tested for underlying driving abilities, and they practiced driving on the simulator. In the second session, they were refamiliarized with the simulator and then drove two blocks of 10 trials, with and without auditory alerts. Braking events in each trial were either expected (e.g., person crossing at a crosswalk) or unexpected (e.g., person jaywalking). The presentation of auditory alerts resulted in faster brake response times with expected events for both groups (p < .01), and they resulted in even faster response times for the older subjects for unexpected events (p < .05). The auditory alerts also reduced the proportion of events with crashes for the older subjects (from 25.3 ± 21.7% to 10.8 ± 11.9%, p < .05). In conclusion, this simulator study demonstrated that there may be road safety benefits associated with auditory alerts.

Accuracy of young male drivers’ self-assessments of driving skill

We examined the effects of trait driving anger, aggressive stimuli, and anonymity on aggressive driving behavior in a driving simulation task. High and low driving anger participants were randomly assigned to one of four conditions: (a) anonymous vs. identifiable driver; and (b) exposure to aggressive stimuli versus nonaggressive stimuli. Participants drove more aggressively when they were anonymous (d = .28) and exposed to aggressive stimuli (d = .05). Males drove more aggressively than did females (d = .06). No main or interaction effects were found for trait driving anger. Results suggest that situational factors affecting other forms of aggression are also important in aggressive driving.

Driving simulators serve as valuable instruments for traffic safety research because they enable the creation of various scenarios that are hard to replicate in the real world. Eye tracker devices have proven to be immensely beneficial in studying eye movements. In this particular study, the objective was to examine potential variations in the adaptability of young male Chinese and Pakistani student drivers to left-hand traffic (LHT) and right-hand traffic (RHT) infrastructures when navigating under unfamiliar driving rules and environments. To achieve this, twenty-one Pakistani and twenty Chinese young male drivers were recruited to participate in different simulated driving scenarios (LHT and RHT). The factors tested were: (1) hazard perception; (2) time to collision (TTC); and (3) intersectional and lane-changing behavior. Using data collected from the driving simulator and eye tracker, differences in adaptability between both pools of drivers were compared using the ANOVA technique. The results showed that young male Chinese drivers were more vigilant and had a higher adaptability to unfamiliar infrastructure (3), they also had a better hazard perception (1) and time to collision (1 and 2). Young male Pakistani drivers had poorer hazard perception (2) and consequently had the shortest brake response time in the RHT scenario (2).

With the fast development of autonomous vehicle (AV) technology and possible popularity of AVs in the near future, a mixed-vehicle type driving environment where both AVs and their surrounding human-driving vehicles drive on the same road will exist and last for a long time. An AV measures its driving environments in real time and make control decisions to ensure driving safety. However, surrounding human-driving vehicles may conduct aggressive driving behaviors (e.g., sudden deceleration, sudden acceleration, sudden left or right lane change) in practice, which requires an AV to make correct control decisions to eliminate the effect of aggressive driving behaviors on its driving safety. In this paper, we propose a reinforcement learning based decision-making system (ReDS) which considers aggressive driving behaviors of surrounding human-driving vehicles during the decision making process. In ReDS, we firstly build a mixture density network based aggressive driving behavior detection method to detect possible aggressive driving behaviors among surrounding vehicles of an AV. We then build a reward function based on aggressive driving behavior detection results and incorporate the reward function into a reinforcement learning model to make optimal control decisions considering aggressive driving behaviors. We use a real-world traffic dataset from the United States Department of Transportation Federal Highway Administration to evaluate optimal control decision determination performance of ReDS in comparison with the state-of-the-art methods. The comparison results show that ReDS can improve optimal control decision success rate by 43% compared with existing methods, which demonstrates that ReDS has good optimal control decision determination performance.