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汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (4): 643-650.DOI: 10.3969/j.issn.1674-8484.2022.04.005

• 汽车安全 • 上一篇    下一篇

考虑因素交互作用的车—车事故严重程度分析

张道文1,2,3(), 王朝健1(), 蒋骏1, 黎华惠4   

  1. 1.西华大学 汽车与交通学院,成都 610039,中国
    2.汽车测控与安全四川省重点实验室,成都 610039,中国
    3.四川省新能源汽车智能控制与仿真测试技术工程研究中心,成都 610039,中国
    4.成都工业职业技术学院,成都 610218,中国
  • 收稿日期:2022-05-09 修回日期:2022-07-18 出版日期:2022-12-31 发布日期:2023-01-01
  • 作者简介:张道文(1968—),男(汉),四川,教授。E-mail: 0119910025@mail.xhu.edu.cn
    王朝健(1997—),男(汉),四川,硕士研究生。E-mail:549786670@qq.com
  • 基金资助:
    四川省重点实验室课题(QCCK2021-011);国家市场监督管理总局项目(202289)

Analysis of the severity of vehicle to vehicle accidents considering the interaction of factors

ZHANG Daowen1,2,3(), WANG Chaojian1(), JIANG Jun1, LI Huawei4   

  1. 1. School of Automobile and Transportation, Xihua University, Chengdu 610039, China
    2. Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Chengdu 610039, China
    3. Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Chengdu 610039, China
    4. Chengdu Industrial Vocational Technical College, Chengdu 610218, China
  • Received:2022-05-09 Revised:2022-07-18 Online:2022-12-31 Published:2023-01-01

摘要:

研究了车对车(V2V)事故严重程度的关键影响因素、因素交互作用下对致死事故率的影响。以(中国)国家车辆事故深度调查体系(NAIS)数据库中583例V2V事故为样本,运用Bayes网络(BN),建立了考虑风险因素交互作用的V2V事故严重程度模型。结合关联规则方法,挖掘了高频率和高耦合规则集。结果表明:过失方车型、过失方状态、受害方车型、发生时段、事故地点、交通信号灯等因素对致死事故率影响显著;因素交互作用下,因素的联合效应显著,并且影响高于各自边际效应和;在十字路口两辆乘用车发生事故的频率较高,但致死事故率比致死事故率的先验概率低18.4%;大型汽车在无交通信号灯的普通路段的致死事故率比致死事故率的先验概率会高42.6%。

关键词: 交通安全, 车对车(V2V)事故, 事故严重程度, Bayes网络, 关联规则, 交互作用

Abstract:

This paper investigated the key factors affecting the severity of vehicle to vehicle (V2V) accidents and the influence of the interaction of key factors on fatal- accident rate. Taking the 583 V2V accidents selected from the National Automobile Accident In-Depth Investigation System (NAIS) database (China) as samples, an analytical model of the V2V accident severity was established by the Bayesian network (BN). The association rule method was integrated to mine the rules with high frequency and strong coupling degree. The results showed that the factors, such as the at-fault party vehicle type, the at-fault party state, the injured party vehicle type, the accident time period, the accident place, and the traffic lights, have significant effects on fatal-accidents rate. The joint effect of factors was significant under the interaction of factors, and the effect was higher than the respective marginal effects. Road junctions had a higher incidence of accidents involving two passenger-cars, but the fatal accident rate is 18.4% lower than the a priori probability of the fatal accident rate. The fatal accident rate of a large car on the common roadway without traffic signals would be 42.6% higher than the a priori probability of the fatal accident rate.

Key words: traffic safety, vehicle to vehicle (V2V) accident, accident severity, Bayesian networks, association rules, interaction

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