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

由于交通事故是小概率随机事件,难以在全时空域上开展交通安全分析,也无法基于此制定事故发生前的交通安全风险主动防控策略。为辨识混杂因素干扰下安全风险及其诱发本质,使用激进驾驶行为数据与速度变异系数计算交通秩序指数(traffic order index,TOI),形成事故替代指标,并通过K-means聚类算法将TOI划分为3种交通安全风险等级。在此基础上,利用Catboost算法构建交通流特征、天气条件、道路条件等因素与交通安全风险等级间的关联关系,并基于基尼系数的特征重要性确定高速公路交通安全风险要素。使用部分依赖图算法解析风险要素与交通安全风险的依赖关系,获取风险要素对交通安全风险的边际效应。结果表明:(1)Catboost算法对风险等级识别的准确率、精确率、召回率依次为85.95%、88.56%、86.75%,证明交通秩序指数与外部风险要素具有较强相关性;(2)交通流量、拥堵指数对风险识别有较大影响,且与交通安全风险等级呈现非线性关系,交通流量>450 veh/h或拥堵指数>1.5时,交通安全风险均会显著增长,交通安全风险分别上升16.9%、29.5%;(3)当连续1 km道路内设有1~2个交通标志时,交通安全风险最高,路段识别为高风险的概率为38.1%;匝道出入口和隧道内部道路的交通安全风险最高;(4)侧风作用会小幅度影响高速公路交通安全风险,当风力等级由0级增至5级时,交通安全风险上升4.99%。

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 between 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%,respectively,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.Notably,when traffic flow exceeds 450 veh/h or the congestion index surpasses 1.5,traffic safety risk would substantially 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 increases by 4.99%.