Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

Abstract

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

Analysis of Key Injury-Causing Factors of Object Strike Incident in Construction Industry Based on Data Mining Method

“…A Bayesian network (BN) represents causal features as a network (weighted graph) by means of a directed graph and then predicts the likelihood of complex and uncertain events by performing probabilistic reasoning. Based on the information accumulated so far, the probability of occurrence of each scenario can be calculated and based on the path of occurrence, thus quantitatively representing the probability of occurrence where causality exists due to complex paths [38,39].…”

Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles

“…The number of rear-end collisions and side-swipe collisions accounts for about 4% of road traffic accidents in Queensland, Australia [3]; 4.9% of all 2015 road traffic accidents were caused by overtaking and improper lane changing in China [4]; and nearly 5% of all road traffic accidents and 7% of all fatalities in such accidents are caused by improper merging or lane-changing operations in the United States [5]. During the process of lane changing, the poor perception and judgment ability or improper operation of the driver may lead to rear-end collisions and side-swipe collisions more easily [6,7]. Studies have shown that nearly half of drivers fail to use turn signals when changing lanes [8,9].…”