交通信息与安全2023，Vol.41Issue(5)：24-34,11.DOI:10.3963/j.jssn.1674-4861.2023.05.003
基于可解释机器学习框架的高速公路安全风险及影响要素识别
Identification of Safety Risk in Freeway and Impact Factors Based on an Interpretable Machine Learning Framework
摘要
Abstract
Traffic accidents,being random events with low-probability,pose challenges for traffic safety analysis in the comprehensively temporal and spatial perspective,which hinders the proactive effective prevention and control strategies before accidents occur.To this end,this paper aims to identify the safety risk and underlying mechanism under various factors.Specifically,data about aggressive driving behavior and speed variation coefficients are used to calculate traffic order index(TOI)to further form accident proxies.TOI are classified into three traffic safety risk levels by K-means clustering algorithm.The correlations of traffic flow characteristics,weather conditions,road conditions,and other factors with traffic safety risk are established using the Catboost algorithm.Based on the feature importance of Gini coefficient,elements contributing to safety risk of highway traffic are identified.Next,the partial dependency plots algorithm is utilized to analyze the dependency relationship and marginal effect be-tween risk factors and traffic safety risk.The results indicate that:①The Catboost algorithm exhibits high model fitness in identifying risk levels with accuracy,precision,and recall rates equaling 85.95%,88.56%,and 86.75%,re-spectively,which confirms the robust correlation of TOI with external risk factors.②Traffic flow and congestion can significantly influence risk identification,displaying a nonlinear relationship with traffic safety risk levels.No-tably,when traffic flow exceeds 450 veh/h or the congestion index surpasses 1.5,traffic safety risk would substan-tially increase by 16.9%and 29.5%,respectively.③When there are 1 or 2 traffic signs within 1km of consecutive roadway,with a 38.1%likelihood of being identified as high-risk areas.Additionally,ramp entrances,exits,and roads inside the tunnel are identified as locations with the highest traffic safety risk.④The impact of lateral wind on traffic safety risk is relatively minor.However,as the wind level increases from 0 to 5,traffic safety risk increas-es by 4.99%.关键词
交通安全/高速公路/风险识别与影响要素挖掘/部分依赖图/机器学习模型Key words
traffic safety/highways/risk identification and impact factor mining/partial dependence plot/machine learning models分类
交通工程引用本文复制引用
杜渐,杨海益,李洋,郭淼,亓航,魏金强,马浩,胡丹丹,李志宇..基于可解释机器学习框架的高速公路安全风险及影响要素识别[J].交通信息与安全,2023,41(5):24-34,11.基金项目
国家重点研发计划项目(2019YFB1600500)资助 （2019YFB1600500）
