中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊
重庆交通大学学报（自然科学版）

重庆交通大学学报（自然科学版） ›› 2026, Vol. 45 ›› Issue (3): 98-110.DOI: 10.3969/j.issn.1674-0696.2026.03.12

• 交通运输+人工智能 • 上一篇    

基于自动机器学习的骑行者碾压致因及损伤严重程度研究

赵云飞1，谢世婷1，王长帅2，王鹏3，朱彤1   

  1. (1. 长安大学 运输工程学院，陕西 西安 710064； 2. 东南大学 交通学院，江苏 南京 211189； 3. 中国汽车技术研究中心有限公司，天津 300300)
  • 收稿日期:2025-08-19 修回日期:2025-10-17 发布日期:2026-03-24
  • 作者简介:赵云飞（2000—），男，四川绵阳人，博士研究生，主要从事交通安全方面的研究。E-mail:13252002559@163.com 通信作者：朱彤（1977—），男，浙江诸暨人, 副教授，博士，主要从事交通安全与驾驶行为方面的研究。E-mail:zhutong@chd.edu.cn
  • 基金资助:
    国家重点研发计划项目(2019YFE0108000)

Causes of Rider Run-Over Crashes and Injury Severity Based on Automated Machine Learning

ZHAO Yunfei1, XIE Shiting1, WANG Changshuai3, WANG Peng2, ZHU Tong1   

  1. (1. School of Transportation Engineering, Chang’an University, Xi’an 710064, Shaanxi, China; 2. School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China; 3. China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China)
  • Received:2025-08-19 Revised:2025-10-17 Published:2026-03-24

PDF

摘要： 为挖掘机动车-二轮车事故特征，揭示碾压事故的形成原因与骑行者损伤致因路径，以中国道路交通事故深度调查数据库的2 281起机动车-二轮车事故为数据基础，将驾驶人、骑行者、二轮车、机动车、道路、环境方面的相关因素作为背景变量，引入AutoGluon自动机器学习方法进行路径分析，提取背景因素、是否被碾压、损伤严重程度之间的影响关系。首先使用AutoGluon建立碾压事故模型与损伤严重程度模型，量化背景因素对碾压和损伤严重程度的边际效应；然后，采用路径分析方法识别背景因素通过是否被碾压影响损伤严重程度的关键路径。结果发现：骑行者被碾压导致死亡概率提升16.75%，重伤概率提升4.38%，其损伤分布与未碾压事故存在显著差异；驾驶人年龄、骑行者年龄、载货车辆、碰撞类型、机动车长度均是导致碾压事故概率增加的主要原因；损伤致因存在3类路径：间接效应主导型（施工区通过增加碾压风险间接提升损伤）、直接效应主导型（儿童骑行者直接效应增加1.25%）、复合作用型（老年骑行者同时存在直接与间接效应）。为了更细致地刻画出背景因素、是否被碾压与损伤严重程度之间的关系，进一步揭示了碾压事故的形成机理，证实碾压事故需独立分析，为实现差异化交通安全治理提供了理论依据。

关键词: 交通工程；骑行者损伤严重程度；自动机器学习；碾压；路径分析；二轮车

Abstract: To explore the characteristics of motor vehicle-two-wheeler accidents and reveal the causes of run-over accidents and the injury causation pathways of riders, 2,281 motor vehicle-two-wheeler accidents in China In-Depth Accident Study Database (CIDAS) were taken as the data basis. Relevant factors related to drivers, riders, two-wheeler, motor vehicles, roads, and the environment were taken as background variables, AutoGluon automated machine learning method was employed for path analysis to extract the influence relationships among background variables, whether or not to be run over and injury serverity. Firstly, AutoGluon was utilized to establish models of run-over accidents and injury severity, quantifying the marginal effects of background factors on both outcomes. Then, the path analysis method was employed to identify the key pathways that background variables influenced injury severity by being run over whether or not. The results show that the probability of death for riders who are run over increases by 16.75%, the probability of serious injury increases by 4.38%, and the distribution of their injuries is significantly different from that in non-run-over accidents. Driver age, cyclist age, loaded vehicle, collision type, and length of motor vehicle are all the main reasons for the increase in the probability of run-over accidents. There are three types of paths to injury causation: indirect effect-dominated type (work zones indirectly enhancing injury through increasing the risk of run-over), direct effect-dominated type (1.25% increase in direct effect for child riders), and composite effect type (both direct and indirect effects for elderly riders). In order to more precisely depict the relationship between background factors, whether or not to be run over and injury severity, the formation mechanism of run-over accidents was further revealed, confirming that run-over accidents need to be analyzed independently, which provides a theoretical basis for the realization of differentiated traffic safety management.

Key words: traffic engineering; injury severity of rider; automated machine learning; run-over; path analysis; two-wheeler

中图分类号: