Causal Analysis of E-bike Traffic Accident Severity
Considering Built Environment

doi:10.16097/j.cnki.1009-6744.2024.01.018

Journal of Transportation Systems Engineering and Information Technology ›› 2024, Vol. 24 ›› Issue (1): 179-187.DOI: 10.16097/j.cnki.1009-6744.2024.01.018

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Causal Analysis of E-bike Traffic Accident Severity Considering Built Environment

WANG Jing¹, DONG Chunjiao^*1, LI Penghui¹, JIANG Wenlong², SHAO Chunfu^1,3

1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. School of Traffic Management, People's Public Security University of China, Beijing 100038, China; 3. School of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China

Received:2023-10-13 Revised:2023-11-23 Accepted:2023-12-19 Online:2024-02-25 Published:2024-02-12
Supported by:
National Natural Science Foundation of China (72371017)；The Project of Public Security Science First-class Subject Elite-oriented Training Action and the Establish of the Public Security Behavioral Science Laboratory at the People's Public Security University of China (2023ZB02)

考虑建成环境的电动自行车事故严重程度致因分析

王菁¹，董春娇^*1，李鹏辉¹，姜文龙²，邵春福^1,3

1. 北京交通大学，综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044；2. 中国人民公安大学，治安与交通管理学院，北京 100038；3. 新疆大学，交通运输工程学院，乌鲁木齐 830017

作者简介:王菁(1995- )，女，重庆荣昌人，博士生
基金资助:
国家自然科学基金(72371017)；中国人民公安大学公安学一流学科培优行动及公共安全行为科学实验室建设项目 (2023ZB02)

Abstract

Abstract: The study aimed to investigate the influential factors contributing to e-bike traffic accident severity, particularly considering the impact of the built environment. First, 18 potential influencing variables were identified, based on accident attributes, cyclist characteristics, attributes of the involved vehicles and drivers, road conditions, and elements of the built environment. A random parameter Logit model was then developed, considering heterogeneity in means and variances. Marginal effects were employed to quantify the influence of the significant variables on accident severity. Sampled data from e-bike accidents in Beijing, China, over the past five years were utilized for analysis. The results showed that factors such as accidents occurring between 19:00 and 7:00 of the next day, cyclists aged over 40 years, presence of heavy (large) trucks, increased distance to the nearest hospital, and adverse weather conditions would increase the severity of e-bike accidents. Among the built environment factors, the parameter of the distance to the nearest hospital exhibits a stochastic nature, following a normal distribution in cases of fatal accidents. Adverse weather conditions and road sections amplify the mean value of the distance to the nearest hospital, while the age group of drivers between 40 and 60 increases its variance heterogeneity. In addition, the parameter of general urban roads in injury accidents adheres to a random parameter with a normal distribution, and road sections increase its mean heterogeneity. These findings provide a theoretical underpinning for reducing the severity of e-bike accidents.

Key words: traffic engineering, accident injury severity, random parameter Logit model, e-bike, built environment

摘要： 为探究考虑建成环境影响下，电动自行车交通事故严重程度的影响因素，本文从事故属性、骑行者属性、对象车辆及驾驶员属性、道路属性及建成环境属性这5个方面，选取18个影响电动自行车交通事故严重性的潜在变量。在此基础上，构建考虑均值及方差异质性的随机参数Logit模型，利用边际效应量化显著变量对事故严重程度的影响差异。基于北京市近5年电动自行车事故抽样数据进行实证研究，结果表明：事故时段19:00-次日7:00、骑行者年龄大于40岁、重(大)型货车、到最近医院的距离增大及恶劣天气等因素会增加电动自行车事故严重程度。建成环境属性中，到最近医院的距离在死亡事故中的参数为服从正态分布的随机参数，路段及恶劣天气会增大其均值异质性，驾驶员年龄为(40, 60]岁会增大其方差异质性；其他属性中，一般城市道路在受伤事故中的参数为服从正态分布的随机参数，路段会增大其均值异质性。研究结果可以为降低电动自行车事故严重程度提供理论支撑。

关键词: 交通工程, 事故严重程度, 随机参数Logit模型, 电动自行车, 建成环境

CLC Number:

U491.31

WANG Jing, DONG Chunjiao, LI Penghui, JIANG Wenlong, SHAO Chunfu. Causal Analysis of E-bike Traffic Accident Severity Considering Built Environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 179-187.

王菁, 董春娇, 李鹏辉, 姜文龙, 邵春福. 考虑建成环境的电动自行车事故严重程度致因分析[J]. 交通运输系统工程与信息, 2024, 24(1): 179-187.

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Causal Analysis of E-bike Traffic Accident Severity Considering Built Environment

考虑建成环境的电动自行车事故严重程度致因分析

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