基于贝叶斯推理与XGBoost的城市交叉口车道风险综合评价模型  

A Comprehensive Evaluation Model of Lane-based Risk for Urban Intersections Based on Bayesian Inference and XGBoost

作　　者：汪勇杰 徐玥莹 苏倩 李琼[1] 尤欣赏 WANG Yongjie;XU Yueying;SU Qian;LI Qiong;YOU Xinshang(School of Transportation Engineering,Chang'an University,Xi'an 710064,China;Xi'an Xixian New District Natural Resources and Planning Bureau(Jinghe Office),Jinghe New City Management Committee,Xi'an 713700,China;School of Economics and Management,Hebei University of Science and Technology,Shijiazhuang 050018,China)

机构地区：[1]长安大学运输工程学院,西安710064 [2]泾河新城管理委员会西咸新区自然资源和规划局(泾河)工作部,西安713700 [3]河北科技大学经济管理学院,石家庄050018

出　　处：《交通信息与安全》2024年第4期12-20,共9页Journal of Transport Information and Safety

基　　金：国家自然科学基金项目(71801020);陕西省社会科学基金项目(2022R053);陕西省自然科学基金项目(2023-JC-QN-0795);河北省自然科学基金项目(G2020208002)资助。

摘　　要：针对城市信号控制交叉口车道风险研究不足及复杂交互带来的不确定性等问题,建立了基于贝叶斯推理与XGBoost的车道风险综合评价模型。基于西安市吉祥村、明光路和青松路这3个交叉口的交通视频数据,从时间逼近和空间逼近这2个维度构建了2个新的风险评价集,选取后侵入时间、最大速度、距离差和速度差作为核心指标,用以捕捉交叉口内的动态风险特征;并引入贝叶斯推理构建概率性评价方法,以解决复杂交互中的不确定性问题。随后进行XGBoost模型的SHAP值理论和Logistic回归,探究影响车道风险等级的特征重要程度和显著性。结果表明:①建立的车道风险综合评价模型在评估非机动车-汽车、行人-汽车与行人-非机动车等3类交互冲突时,识别中等和高风险的性能优于基准模型,特别是在极度危险交互的判定上更为合理,避免了基准模型的高估问题。②在常见的非机动车-汽车交互、行人-汽车交互和行人-非机动车交互中,仅有少部分为极度危险情况,但仍存在较多的中等风险,总占比分别为29.7%,20.8%,34.3%。③交叉口不同车道的风险存在显著差异,第1车道相较于第2、3、4车道更容易发生交通冲突。④在3类交互中,车道风险主要受速度、加速度和流量影响。非机动车-汽车交互在第1车道和隔离带宽度较小的路段风险最高,尤其是早高峰和右转车道。行人-汽车交互的车道风险因素集中于速度和流量,且第1车道风险较大。行人-非机动车交互中,较窄的非机动车道增加了冲突风险。This study addresses the challenges of inadequate researches on lane-scale risk evaluation and the uncertainties in complex interactions at urban signal-controlled intersections.To this end,a comprehensive risk evaluation model is developed based on Bayesian inference and XGBoost.Specifically,this study is based on traffic video data from three intersections in Xi'an:Jixiang Village,Mingguang Road,and Qingsong Road.Two innovative risk-eval-uation sets are constructed from the dimensions of temporal and spatial proximities,in which key indicators,including post-entrainment time,maximum speed,distance difference and speed difference are selected to capture dynamic risk characteristics of intersections.Further,Bayesian inference is used to develop a probabilistic evaluation method to address uncertainties in complex interactions at intersections.Next,SHAP value theory of the XGBoost model and Logistic regression are applied to analyze the significance and importance of factors influencing lane risk levels.The results show that:①The proposed model outperforms baseline models in identifying medium and high-risk interactions.It also more accurately assesses extreme danger interactions,avoiding the overestimation observed in baseline models.②Among the typical interactions,that between motor vehicle-bicycle,pedestrian-motor vehicle,and pedestrian-bicycle,only a small portion are classified as extreme risk,though medium-risk interactions account for 29.7%,20.8%,and 34.3%,respectively.③There are significant differences regarding the risk level across different lanes,with the first lane being more prone to traffic conflicts compared to the second,third,and fourth lanes.④For all three interaction types,lane risk is mainly influenced by speed,acceleration,and traffic volume.In motor vehicle-bicycle interactions,the highest risk occurs in the first lane and on roads with narrow buffer zones,particularly during morning rush hours and on right-turn lanes.Pedestrian-motor vehicle interactions are primarily influenced by s

关 键 词：交通安全 车道风险 安全性分析 贝叶斯推理 XGBoost 

分 类 号：X951[环境科学与工程—安全科学]