交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 360-370.DOI: 10.16097/j.cnki.1009-6744.2026.01.033

• 工程应用与案例分析 • 上一篇    

基于链式因果推断的国省干线人因交通事故异质性分析

姚亮1，张文贵2，吴利3，刘尊青*1，陈贻乐2   

  1. 1. 新疆农业大学，交通与物流工程学院，乌鲁木齐830052；2.贵州省公路工程集团有限公司，贵阳550008；3. 新疆哈密市公安局，交通警察支队，新疆哈密839000
  • 收稿日期:2025-11-16 修回日期:2025-12-08 接受日期:2025-12-18 出版日期:2026-02-25 发布日期:2026-02-17
  • 作者简介:姚亮（1995—），男，新疆乌鲁木齐人，助教。
  • 基金资助:
    新疆哈密市公安局科技专项项目(2524HXKT2)；贵州省公路工程集团科技项目(XHGJ-5-JSFW-005)。

Heterogeneity Analysis of Human-Factor Traffic Accidents on National and Provincial Arterial Roads Considering Chain-based Causal Inference

YAO Liang1, ZHANG Wengui2, WU Li3, LIU Zunqing*1, CHEN Yile2   

  1. 1. College of Transportation & Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China; 2. Guizhou Provincial Highway Engineering Group Co Ltd, Guiyang 550008, China; 3. Traffic Police Detachment, Public Security Bureau of Hami, Hami 839000, Xinjiang, China
  • Received:2025-11-16 Revised:2025-12-08 Accepted:2025-12-18 Online:2026-02-25 Published:2026-02-17
  • Supported by:
    Science and Technology Special Project of Hami Public Security Bureau, Xinjiang(2524HXKT2)；Science and Technology Project of Guizhou Provincial Highway Engineering Group (XHGJ-5-JSFW-005)。

PDF

摘要： 针对国省干线人因交通事故的异质性特点，提出一种事故链式因果推断方法，实现交通事故因果机制辨识与交互效应量化的同步解析。首先，采集新疆11条国省干线近年交通事故信息构建数据集，利用K-prototype 聚类算法将事故严重程度分为3类；其次，基于结构因果模型、因果森林及SHAP（SHapley Additive exPlanation）算法构建链式因果推断模型，推理出事故关键致因链、多维诱因交互效应及事故类别概率预测值，解析“场景组合-人为要素-事故类别”的链式传导特征及异质性特点；最后，基于SHAP值分析多维要素贡献度，并结合因果效应强度划分行为致因类别，辨识出事故关键人为致因，提出具有针对性的事故防控策略。结果表明：本文所提方法的加权平均F1得分与宏平均AUC（Area Under Curve）值分别为0.86与0.82，相对高于常用的机器学习算法，且克服了传统关联模型难以实现多因素交互作用机制刻画与效应量化的局限，适宜人因事故异质机理解构分析；由致因链分析可知，人为要素为事故主要致因，天气和时段等环境要素对事故后果均有显著影响，恶劣环境与危险行为的综合作用对事故程度升级具有非线性影响作用；超速行驶、疲劳驾驶、跟车过近与观察不周为核心行为致因，营运类车辆出现上述行为后事故严重程度相对较高，严重型事故占比超过50%，应重点进行事故监测与防控。

关键词: 公路运输, 事故异质性特征, 链式因果推断, 人因事故, 国省干线公路

Abstract: To investigate the heterogeneity characteristics of human-caused traffic accidents on national and provincial arterial roads, this paper proposes a chain causal inference method with the synchronous analysis to analyze the causal mechanism of traffic accidents and quantify the interaction effects. First, the recent traffic accident information was collected from 11 arterial roads in Xinjiang province, and the severity of the accidents was classified into three categories using the K-prototype clustering algorithm. Based on the structural causal model, causal forest, and the SHAP (SHapley Additive exPlanation) algorithm, the study proposes a chain causal inference model to infer the key causal chains of the accidents, the interaction effects of multi-dimensional causes, and the predicted values of the accident categories. The chain transmission characteristics and heterogeneity of "scene combination-human factors-accident category" were also analyzed. Then, the contribution of multi-dimensional factors was evaluated based on the SHAP value. The behavioral causes were classified according to the strength of the causal effect to identify the key human causes of the accidents and propose targeted accident prevention and control strategies. The results show that:(1) the weighted average F1 score and macro average AUC (Area Under Curve) value of the proposed method are 0.86 and 0.82, which are relatively higher than those of commonly used machine learning algorithms. The proposed method overcomes the limitations of traditional association models in characterizing and quantifying the interaction mechanism of multiple factors, making it suitable for the analysis of the heterogeneous mechanism of human-caused accidents. (2) From the analysis of the causal chain, it can be seen that human factors are the main causes of accidents, and environmental factors such as weather and time periods have significant impacts on the consequences of accidents. The combined effect of adverse environments and dangerous behaviors has a nonlinear impact on the escalation of accident severity. (3) Speeding, fatigued driving, following too closely, and poor observation are the critical behavioral causes. For commercial vehicles, these driving behaviors normally cause higher severity of the accidents(i.e., the proportion of severe accidents exceeding 50%), which indicates an important area for accident monitoring and prevention.

Key words: highway transportation, accident heterogeneous characteristics, chain-based causal inference, human-factor accidents; national and provincial arterial roads

中图分类号: