交通运输工程与信息学报2026，Vol.24Issue(3)：102-113,12.DOI:10.19961/j.cnki.1672-4747.2025.09.029
基于可解释机器学习的信号交叉口左转冲突风险建模与量化分析
Modeling and quantifying left-turn traffic conflict at signalized intersections based on explainable machine learning
摘要
Abstract
[Background]At signalized intersections,left-turn-related traffic conflicts exhibit dynam-ic response characteristics and have a significant impact on the safe operation of intelligent transpor-tation systems.Typical left-turn traffic conflicts are mainly categorized into those between pedestri-ans and left-turning vehicles,and opposing through-traffic and left-turning vehicles.[Objective]To explore potential factors that influence left-turn traffic conflicts and to clarify the underlying mecha-nisms through which these factors affect both types of conflicts.[Data]Observational data collected from September 13～19,2019,at six representative signalized intersections in Bellevue,Washington,USA.[Method]Multiple machine-learning models were employed to model the left-turn traffic con-flict risk(conflict frequency),and Bayesian Optimization was applied to determine the optimal hy-perparameters of each model.Among these,the Extreme Gradient Boosting(XGBoost)model dem-onstrated superior performance in risk modeling both types of left-turn traffic conflicts.Shapley Ad-ditive Explanations(SHAP)and Accumulated Local Effects(ALE)were used to interpret XGBoost outputs,systematically quantifying the mechanisms through which traffic-flow characteristics,signal-control features,and other potential factors influence left-turn traffic conflicts.[Conclusion]Larger traffic volumes at signalized intersections are associated with a higher left-turn traffic conflict risk;the effect of the traffic volume is highly nonlinear.Different signal phase-control strategies play markedly distinct roles in moderating the conflict intensity;among these,protected phase-control mechanisms can effectively suppress the occurrence of conflicts.Moreover,when the interval of the Flashing Don't Walk(FDW)signal exceeds 20 s,it can result in higher pedestrian volumes—signifi-cantly amplifying the risk of left-turn traffic conflicts.[Application]These findings can provide a reference for optimizing signal-control strategies and geometric intersection designs to enhance the safety of urban signalized intersections.关键词
智能交通/交通安全/信号控制交叉口/左转冲突/可解释机器学习Key words
intelligent transportation/traffic safety/signalized intersections/left-turn traffic con-flicts/explainable machine learning分类
交通工程引用本文复制引用
徐嗣轩,何治平,周威,章天然,王晨..基于可解释机器学习的信号交叉口左转冲突风险建模与量化分析[J].交通运输工程与信息学报,2026,24(3):102-113,12.基金项目
国家重点研发计划项目(2023YFE0106800) （2023YFE0106800）
江苏省杰出青年基金项目(BK20231531) （BK20231531）
江苏省前沿技术研发计划项目(BF2024019) （BF2024019）
江苏省科技成果转化专项基金项目(BA2023010) （BA2023010）
江苏省研究生科研与实践创新计划项目(sjcx240100) （sjcx240100）
