电动汽车与行人碰撞事故时空特征与严重程度建模  

Spatiotemporal characteristics and severity modelling of electric vehicle-pedestrian collision accidents

作　　者：庄焱 董春娇[1] 李鹏辉 郑睿 ZHUANG Yan;DONG Chun-jiao;LI Peng-hui;ZHENG Rui(School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学交通运输学院,北京100044

出　　处：《交通运输工程学报》2024年第6期299-310,共12页Journal of Traffic and Transportation Engineering

基　　金：国家自然科学基金项目(72371017,52302425)。

摘　　要：为了探索电动汽车与行人碰撞事故的时空分布模式,在传统核密度估计法中引入聚类强度和事故严重度指标,建立了交通事故黑点时空核密度估计方法;考虑到事故变量中可能存在的随机变量和异质性,基于均值异质性的随机参数Logit模型,建立了电动汽车与行人碰撞事故严重程度模型,识别影响电动汽车与行人碰撞事故严重程度的关键因素,并通过计算显著变量的边际效用定量确定了关键因素的影响程度。研究结果表明:电动汽车和燃油汽车与行人碰撞事故均主要分布在城市中心区域,电动汽车与行人碰撞事故分布相对更为集中,且工作日和白天时电动汽车的事故数更多,严重程度更高;电动汽车驾驶人年龄在(40,60)岁和事故发生时间段在18:00至次日6:00为随机变量,驾驶人年龄的效用值服从标准差为-0.582、方差为0.926~2的正态分布,且在道路功能等级(主干路)和车辆类型(货车)变量中具有均值异质性,事故发生时间段的效用值服从标准差为-0.313、方差为0.518~2的正态分布,且在天气(雾霾)变量中具有均值异质性,此外,男性驾驶人、中青年驾驶人和工作日出行等因素会导致电动汽车与行人碰撞事故严重程度的不同增加;考虑均值异质性的随机参数Logit模型的赤池信息准则比随机参数Logit模型减少了26,对数似然值增加了94.272,可见,引入均值异质性后,构建的基于随机参数Logit模型的电动汽车与行人碰撞事故严重程度模型拟合优度有所提升,且能更科学全面地解释电动汽车与行人碰撞事故的致因原理。To investigate the spatiotemporal distribution pattern of electric vehicle-pedestrian collision accidents,the indicators of clustering strength and accident severity were incorporated into the traditional kernel density estimation method.A spatiotemporal kernel density estimation approach for traffic accident-prone locations was built.The random parameter Logit model based on mean heterogeneity was developed to model the severity of electric vehicle-pedestrian collision accident by considering the potential random variables and heterogeneity in accident variables.Key factors affecting the severity of electric vehicle-pedestrian collision accidents were identified,and the marginal utility of each significant variable was calculated to quantify its impact on the severity of electric vehicle-pedestrian collision accidents.Research results show that the electric/fuel vehicle-pedestrian collision accidents are predominantly located in urban center areas,whereas the distribution of electric vehicle-pedestrian collision accidents is relatively more concentrated.Moreover,the number and severity of electric vehicle-pedestrian collision accidents are higher on weekdays and daytime.Electric vehicle driver age ranging from 40 to 60 years old and accident occurring time between 18:00 and 6:00 the next day are identified as random variables.The utility value of driver age follows a normal distribution with standard deviation of-0.582 and variance of 0.9262,and has mean heterogeneity in variables of road function level(trunk road)and vehicle type(truck).The utility value of accident occurring time follows a normal distribution with standard deviation of-0.313 and variance of 0.5182,and there is mean heterogeneity in variable of weather(haze).Additionally,the factors such as male drivers,middle-aged and young drivers,and weekday travel are found to contribute to the varying degrees of severity in electric vehicle-pedestrain collision accidents.The Akaike information criterion of the random parameter Logit model considering mean

关 键 词：交通安全 电动汽车 随机参数Logit模型 事故时空特征 事故严重度 边际效应 

分 类 号：U491.31[交通运输工程—交通运输规划与管理]