Predictive Validity of the Aggressive Driver Behavior Questionnaire (ADBQ) in a Simulated Environment

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.

The goal of the current study was to examine the relationship between identity traits, manliness, and aggressive driving in young male drivers. Manliness was hypothesized to mediate the relationship between identity traits and aggressive driving. It is generally agreed that there will likely be a relationship between identity features, manliness, and forceful driving in young male drivers, and that manliness will probably operate as an intermediary in that relationship. It is a cross-sectional co-relational research study. A total of 150 young male drivers made up the sample. Purposive sampling was used to gather the sample from various Lahore institutions. The Doing Masculinity Composite Scale (DMCS), the Ten-Item Personality Inventory (TIPI), and the Aggressive Driving Behavior Questionnaire (ADBQ) are among the measures. Structural Equation Modelling (SEM) was used to examine the mediating effect of masculinity between aggressive driving behavior and personality factors. Pearson product moment correlation was utilized to evaluate the relationship between the study variables. The goal of this study is to identify any potential psycho-social causes of aggressive driving in order to develop a strategy to curb this behavior while improving men's social perception.

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.

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.

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.

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.

Based on NS model, a mixed traffic flow on single lane is simulated in the paper, which includes two-classification vehicles owing the different max-velocity. Take the one move 5 grids for one time as quick-vehicle, which is the results of aggressive behavior, such as overtaking and closely following behavior. And take the one which moves 3 grids for one time as slow-vehicle, which is on behalf of normally driving. The relationship among discrete of traffic flow, traffic density and chaos is researched, in the hope of preparing the solid theoretic basis for eliminating the bad chaos in traffic flow. And based on the distance headway condition and velocity condition, Rear-end accident risk assessment coefficient is advanced to research the influence of aggressive behavior on traffic safety lever on the urban road and on the freeway. The results show that aggressive behavior would emphasis the chaos of traffic flow and the happen opportunity is more, the chaos character in traffic flow is more. And aggressive driving behavior would decrease the safety character of traffic flow. The happen opportunity is more, the value of Rear-end accidents is bigger.

What is the role of narcissism in the relationship between impulsivity and driving anger expression?

This study aims to identify the relationship of playing violent video game addiction and aggressive behavior of elementary school students. This research uses analytic survey design with cross sectional approach. The populations in this study were all elementary students in Unklab Elementary School Airmadidi amounted to 158 people. Sample selection using purposive sampling technique amounted to 87 people, with the criteria such as male and female, grade III and IV, love to play a violent video game, plays video games more than 2-3 hours every day from last one year. Data collection using a video game use behavior questionnaire and aggressive behavior questionnaire adapted from Siregar (2014). Univariate analysis uses frequency distribution and bivariate analysis using Spearmen Correlation and Partial Correlation. The results of the study showed that of 87 respondents, most of which were included in the category of high violent video game playing addiction that is 39 respondents (44.8%), mostly included in the category of aggressive behavior is very high that is 41 respondents (47.1%). The result of Spearman Correlation statistical test between violent video games use addiction and aggressive behavior obtained p value = 0.000 <0.05 it means H01 rejected. The results of Partial Correlation between the violent video games playing addiction and aggressive behavior by gender obtained p value = 0.000 <0.05 it means H02 rejected. Gender respondents who aggressively behaved with high category were male which amounted to 35 respondents (71.4%) and female were 6 respondents (15.8%), therefor men are more aggressive than women. For parents to be able to control and guide children to control and limit the use of electronic devices as entertainment that became one of the factors causing children to use the violent video games.

In order to increase traffic safety on our roads, certain forms of behavior and personality traits of passenger car drivers were studied. As an attempt to understand the potential contribution of drivers’ impulsiveness and aggressiveness in traffic accidents in Sulaimaniyah City, this study was conducted. The correlation between drivers’ impulsiveness and aggressiveness were also explored. Participants, who filled Barratt Impulsiveness Scale (BIS-11) and Aggressive Driving Behavior Questionnaire (ADBQ), were 244 drivers. The male drivers who answered the questionnaires were 143, while female drivers were 101. The results of the statistical analyses showed that male drivers are driving more aggressively than female drivers; as a result, male drivers faced higher numbers of traffic accidents than female drivers. There were no significant differences between male and female drivers regarding drivers’ impulsivity. Speeding as a measuring scale of the aggressive driving is significantly correlated with second-order impulsiveness subscales. The attentional and motor impulsiveness subscales are more correlated with the total score of the driving aggressiveness than non-planning subscale. There was moderate correlation between the impulsiveness total score and the overall aggressiveness score. The impulsiveness of the drivers was negatively correlated with the drivers’ ages and positively correlated with number of crashes; while the driving aggressiveness was significantly correlated with number of crashes and negatively with gender and age of the drivers. The traffic police in Sulaimaniyah City can benefit from the results of this paper during permitting driving license and enforcement processes.

&lt;div class="htmlview paragraph"&gt;The driver's ability to control the lateral position of an automobile is dependent on his perception of the command path (roadway) to be followed. This perception is affected by both the configuration of road markings and other features, and the visibility of these elements. As visibility decreases, the driver's preview of the commanded path is reduced.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;Theory indicates that driver performance should degrade with reduced preview and configurational parameters which characterize the intermittent nature of delineation (e.g., dashed lines). This paper describes a simulation experiment in which driver behavior and driver/vehicle system performance were measured over a range of visibility and configuration parameter variations. Driver dynamic response and noise (remnant) were reliably affected by variations in visibility and configuration. These effects were also reflected in system performance measures such as lane deviations. The results suggest minimum perceptual (visibility) requirements for the driver to maintain adequate steering control of a car under reduced visibility conditions.&lt;/div&gt;

Traffic plays a key role in evaluating a country's development. Urbanization, road construction, and vehicle numbers have increased worldwide, adding to the risk of accidents. Traffic accidents on freeways tend to cause more severe damage than accidents on other types of roads. Identifying collision hotspots on freeways is the first step to preventing crashes. In addition, governments need to allocate funding for safety legislation and supervision. Intelligent methods, such as smartphones, big data, and cloud computing, can be used for traffic safety, identifying hotspots by investigating traffic exposure, road attributes, and weather conditions. This paper analyzes the risk from aggressive driving behavior (ADB), including sudden accelerating, braking, and turning. Data is acquired from a Chinese smartphone application, Amap, owned by AutoNavi Software Company. A big amount of data is used by virtue of cloud computing technology. Results show the reliability of such algorithms to identify traffic accident hotspots on freeways.

Ways of identifying aggressive events in the educational environment

The objective of this study is the assessment of the real-world environmental performance, and the comparison with laboratory measurements, of two Euro 6 passenger cars. The first is equipped with a common-rail diesel engine, Lean NOx Trap (LNT) and Diesel Particulate Filter (DPF), and the second is a bi-fuel gasoline/CNG (Compressed Natural Gas) one, equipped with Three Way Catalyst (TWC). The experimental campaign consisted of on-road and chassis dynamometer measurements. In the former test set, two driving routes were followed, one complying with the Real Driving Emissions (RDE) regulation, and another characterized by more dynamic driving. The aim of the latter route was to go beyond the regulatory limits and cover a wider range of real-world conditions and engine operating area. In the laboratory, the WLTC (Worldwide harmonized Light vehicles Test Cycle) was tested, applying the real-world road load of the vehicles. Both cars underwent the same tests, and these were repeated for the primary (CNG) and the secondary (gasoline) fuel of the bi-fuel vehicle. In all the tests, CO2 and NOx emissions were measured with a Portable Emissions Measurement System (PEMS). The results were analyzed on two levels, the aggregated and the instantaneous, in order to highlight the different emissions attributes under varying driving conditions. The application of the realistic road load in the WLTC limited its difference with the RDE compliant route, in terms of CO2 emissions. However, the aggressive driver behavior and the uphill roads of the Dynamic driving schedule resulted in around double CO2 emissions for both cars. The potential of natural gas to reduce CO2 emissions was also highlighted. Concerning the diesel car NOx emissions, the real-world results were significantly higher than the respective WLTC levels. On the other hand, the bi-fuel car exhibited very low NOx emissions with both fuels. Natural gas resulted in increased NOx emissions, compared to gasoline, always remaining below the Euro 6 limit, with only exception the Dynamic driving schedule. Finally, it was found that the overall cycle dynamics are not sufficient for the complete assessment of transient emissions and the instantaneous engine and aftertreatment behavior can reveal additional details.