Factor comparison of passenger-vehicle to vulnerable road user crashes in Beijing, China

被引:22
|
作者
Yuan, Quan [1 ,2 ]
Chen, Hongyun [2 ]
机构
[1] Tsinghua Univ, Dept Automot Engn, State Key Lab Automot Safety & Energy, Beijing, Peoples R China
[2] Minist Transport, Key Lab Rd Safety Technol, Beijing, Peoples R China
关键词
Crash severity; passenger-vehicle; pedestrian; bicycle; electric-bicycle; logic regression model; ELECTRIC BIKE RIDERS; PEDESTRIAN ACCIDENTS; BICYCLIST;
D O I
10.1080/13588265.2016.1248226
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Vehicle to vulnerable road user (VRU) crash is a large portion of traffic crashes in China Crash data from Beijing, China from the year 2009 to 2012 are used to identify the factors associated with the likelihood of vehicle to VRU crashes. 180 passenger-vehicles to VRU crashes are collected including 60 vehicle to pedestrian, 60 vehicle to bicycle and 60 vehicle to electric-bicycle cases. Then the statistics of the crash data are carried out, and the variables of human, vehicle, road, environment are investigated. Further, a logic regression model is established to analyse the significance of main contributing factors of these crashes. This paper describes the sample data, which includes time of incident, road user's age and gender, impact speed, crash pattern and VRU's head impact position. Also a comparative research among the three crash types is performed. According to the results, some characteristics of three crash types are different, such as the occurrence time, road position, impact speed and the impact position of VRU's head on the passenger-car. Moreover, chi-square test reveals that night-time travelling, crash type involving pedestrian and speeding of vehicle are significant related to non-fatal/fatal crashes. The logic regression model shows that night-time, intersection, older age of VRU and higher speed of vehicle increased the crash severity.
引用
收藏
页码:260 / 270
页数:11
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共 47 条
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