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

Journal of Advanced Transportation

Liu

Feng

2019

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new tra c safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers' mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we nished nonparametric test of the drivers' psychological load and road test. e results show that in compassion with the daytime scenario, drivers should pay much e ort to driving skills and risk identi cation in the nighttime scenario. us, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions.

Section: Measurement Methodsmentioning

confidence: 99%

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

Journal of Advanced Transportation

Liu

Feng

2019

“…Weng and Meng [24] discussed risky driving behavior of vehicles at work zones and similar results were found in our study. Ma et al [7] also found irregular driving behavior such as aggressive driving is related to traffic accident deaths. Therefore, it is necessary to legislate some irregular driving behaviors into illegal driving behaviors to make drivers realize that their irregular driving behaviors can be illegal and reduce irregular driving behavior consequences.…”

Section: Common Points Of Passenger and Freight Vehicle Accidentsmentioning

confidence: 97%

“…Factors influencing the injury severity of traffic accidents have been discussed by many scholars. Previous studies indicated that factors affecting the severity of road traffic accidents include the age of the motor vehicle driver [6], aggressive driving behavior [7], driving experience [8], drunk driving [9], weather [10], road infrastructure [11], hazardous materials transportation [12], accident circumstances [13], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Factors Influencing Injury Severity of Vehicle At-Fault Accidents: A Comparative Analysis of Passenger and Freight Vehicles

Sun

et al. 2020

IJERPH

The objective of this study is to find factors influencing the injury severity of vehicle at-fault accidents in Shenyang (China), and discuss the commonalities and differences between passenger and freight vehicle accidents. We analyzed 1647 traffic accidents from 2015 to 2017, in which motor vehicles were fully or mainly responsible, including 1164 traffic accidents caused by passenger vehicles and 483 traffic accidents caused by freight vehicles. Twenty influencing factors from the aspects of accident, driver, time, space and environmental attributes are analyzed to find their statistical connection with injury severity using the binary logistic regression model. For passenger vehicles, five influencing factors (side collision; illegal act while driving; hit-and-run; season and administrative division), showed statistically significant correlations with the injury severity. For freight vehicles, three influencing factors (illegal act while driving; season and administrative division), showed statistically significant correlations with the injury severity. Illegal act while driving is the only common influencing factor for the injury severity of both passenger and freight vehicle accidents. Side collision and hit-and-run are significant influencing factors for the injury severity of passenger vehicle accidents, but not for freight vehicle accidents. Season and administrative division present different results on influencing passenger and freight vehicle accidents. Based on these results, measures including driver education and road infrastructure improvement could be implemented to reduce the injury severity of accidents in passenger and freight vehicles.

“…[23][24][25][26][27]. As such, aberrant behaviors [28,29] related to tra c incidents could be detected. However, it is o en di cult to be utilized in practice, limited by high computational costs and unsatisfactory tracking performance in congested tra c environment [30].…”

Section: Literature Reviewmentioning

confidence: 99%

A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition

Journal of Advanced Transportation

Dai

Zhou

et al. 2020

In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.