The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

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The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

The mediating effect of driver characteristics on risky driving behaviors moderated by gender, and the classification model of driver's driving risk.

Crashes are occurred frequently at freeway off-ramps due to improper lane-changing (LC) behaviours. The LC behaviour is the main cause of freeway off-ramp crashes. It is important to warn the LC to reduce potential crashes. The uncertainty of LC behaviour increases the difficulties of predicting in advance. The off-ramps at Xi’an Raocheng freeway were chosen for investigation. The datasets were collected by the UAV. There was a total of 637 LC images extracted from the 200 minutes’ video stream. All LC behaviours were divided into twelve categories according to the changing direction and the influence of other vehicles in the target-lane or ego-lane. The machine learning technology is efficient in the image recognition. Thus, the vision technology was applied to devised a lane-changing recognition (LCR) model with the two-level convolutional neural network. A novel convolutional neural network based on the AlexNet was also proposed to compare with the LCR model. All samples were divided into a training dataset and a testing dataset for two models. The performance of two machines networks was compared. The training average accuracy was above 94.6% with the LCR model. The LCR model outperformed the model based on the AlexNet which was only 73.97% on average.

Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps

Attributions of social interactions: Driving among self-driving vs. conventional vehicles

The purpose of this study was to systematically investigate the factors that influence driver aggression by using a combination of attribution theory, planned behavior theory, and general aggression theory. Using the principle of convenience to select our sample size, we asked 308 Chinese drivers from Dalian to fill out a questionnaire. They ranged in age from 21 to 65 years (n = 297). Inspired by the Propensity for Angry Driving Scale (PADS) and the Driving Anger Scale (DAS), we formulated six scenarios involving collision risk and six scenarios involving obstacles. We also evaluated subjects’ cognitive assessments, experienced anger, and tendency toward aggressive driving behavior for each scenario. Subjects also completed the Aggression Questionnaire and the Ethical Position Questionnaire (EPQ). The EPQ divided participants into four categories: situationists (with high idealism and high relativism), absolutists (with high idealism and low relativism), subjectivists (with low idealism and high relativism), and exceptionists (with low idealism and low relativism). A path analysis of the structural equation model showed that attributive tendency, along with responsibility inference, and anger as mediator variables, collectively affected aggressive driving behavior. An analysis of variance showed that drivers were more likely to attribute an internal cause to the infringing behavior of instigating drivers and the level of responsibility inference was higher for dangerous situations than obstructive situations. Within the context of Chinese culture, subjectivists have a stronger tendency towards internal attribution and responsibility inference, and exceptionists, at the other extreme, have the lowest tendency towards internal attribution and responsibility inference. Drivers who held exceptionist ethical positions had a higher level of aggression. The results contribute to a comprehensive understanding of the psychological mechanisms behind aggressive behavior and suggest that road safety can be promoted by upholding road fairness through strict and just laws, which encourage drivers to suppress their feelings of anger and aggressive tendencies using rational cognition methods.

The exceptionists of Chinese roads: The effect of road situations and ethical positions on driver aggression

The aim of this study was to examine the interactions between the road situational risk and angry driving style on lane-changing decisions in drivers and the allocation of visual attention of angry-driving-style drivers based on video clips of driving. We employed the Tobii eye tracker to collect the eye movement markers and reaction time data of 35 drivers. First, the road type and distances of neighboring cars were used to classify situations in the video clips into high- and low-risk situations. The revised Chinese version of the Multidimensional Driving Style Inventory (MDSI-C) was used to measure the driving style of drivers. The eye movement heatmap of drivers was used to classify the areas of interest into the front view and side mirror. We found that the angry driving style and the total fixation duration on the front view of drivers in high-risk situations were predictors of the relative lane-changing decision time. Angry-driving-style drivers’ relative lane-changing decision time is shorter than that of other drivers. In high-risk driving situations, the longer the total fixation duration on the front view, the shorter the relative lane-changing decision time. We also found that the angry driving style and eye movement areas of interest interacted with the mean search durations and total fixation durations of the drivers. Angry-driving-style drivers were more focused on the front view. Our study has significance for the improvement of the improvement of the allocation of visual attention of angry-driving-style drivers and the development of advanced driver assistance systems (ADAS) and connected and autonomous vehicles (CAVs).

Lane-changing-decision characteristics and the allocation of visual attention of drivers with an angry driving style

Primary school children are vulnerable road users. At this age, children's self-control ability is not mature and still developing, and thus they need more parental companionship and education. However, as China's social and economic development accelerates, many parents living in townlets have chosen to work in big cities. Currently, the effects of the absence of parental education on children's pedestrian safety, or what mechanisms lie behind these effects, are unknown. Therefore, this study aimed to explore the relationship between parental absence and the pedestrian behaviors of primary school children and proposed a mediating model to examine the mediating effect of self-control ability. In this study, a questionnaire survey was conducted with 197 elementary school students in grades four to six in a town in Shandong Province, China. A student self-control scale and pedestrian behaviors scale were used in the research. The results were as follows: First, children with absent fathers had low planning ability. Second, as children's planning ability increased, they showed more positive traffic behaviors. Third, planning played a mediating role in the relationship between father absence and the pedestrian behaviors of primary school children; that is, father absence indirectly affected children's positive pedestrian behaviors through planning.

The influences of father absence on primary school children's pedestrian behaviors: The mediating effect of self-control

Does sensation seeking—as a personality trait that is closely related to risky driving—affect a driver's hazard perception, and if so, under what circumstances? Using hazard locations and vehicle signals as cues, this study classifies traffic situations into four types: danger ahead with cues, danger ahead without cues, danger behind with cues, and danger behind without cues. Based on these classifications, we examine the characteristics of hazard perception, decision-making criteria, sensitivity, and visual attention patterns of drivers with different sensation-seeking tendencies. Fifty-two experienced drivers were recruited for experiments in Dalian, China. Using computer, participants were shown videos taken from the driver's perspective and were asked to respond to hazards by pressing keys on the left mouse button. Their eye movements were recorded, and a multi-dimensional driving-style scale was used to divide participants into higher and lower sensation-seeking groups. The results showed that the higher sensation-seeking drivers had a lower hazard detection rate, fewer fixation counts, and shorter glances when there were cues, and they responded more slowly when there was danger ahead. For dangers ahead with cues and dangers behind without cues, the more attention the higher sensation-seeking drivers paid to the danger, the slower their response and the lower their hazard detection rate. The results indicated that higher sensation-seeking drivers have poor hazard perception in situations when there are vehicle signals as prompting clues and dangers ahead. In particular, for a danger with clues, the visual agility of the higher sensation-seeking drivers was worse than that of the lower sensation-seeking drivers. The more attention they paid to the danger the worse their hazard perception. The results of this study can provide guidance to teach drivers hazard-perception skills.

Ruosong Chang

Papers

The exceptionists of Chinese roads: The effect of road situations and ethical positions on driver aggression

Lane-changing-decision characteristics and the allocation of visual attention of drivers with an angry driving style

The influences of father absence on primary school children's pedestrian behaviors: The mediating effect of self-control

The interactive effect of vehicle signals and sensation-seeking on driver hazard perception