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。
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.
作者
王菁
董春娇
李鹏辉
姜文龙
邵春福
WANG Jing;DONG Chunjiao;LI Penghui;JIANG Wenlong;SHAO Chunfu(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China;School of Traffic Management,People's Public Security University of China,Beijing 100038,China;School of Traffic and Transportation Engineering,Xinjiang University,Urumqi 830017,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2024年第1期179-187,共9页
Journal of Transportation Systems Engineering and Information Technology
基金
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)。
