Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model

Quan Yuan (), Xianguo Zhai, Wei Ji (), Tiantong Yang, Yang Yu and Shengnan Yu
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Quan Yuan: State Key Laboratory of Automotive Safety and Energy, School of Vehicle & Mobility, Tsinghua University, Beijing 100084, China
Xianguo Zhai: Department of Road Traffic Management, Beijing Police College, Beijing 102202, China
Wei Ji: Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China
Tiantong Yang: Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China
Yang Yu: Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China
Shengnan Yu: Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China

Sustainability, 2022, vol. 14, issue 23, 1-11

Abstract: Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should strengthen safety consciousness and compliance with regulations to prevent accident and reduce injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may provide ideas for intelligent vehicles to avoid collisions with risky VRUs.

Keywords: traffic accident; vulnerable road user; Bayesian general ordinal logit model; risky traffic behavior; contributing factor statistics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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