高速公路实时事故风险研判模型及可移植性  

Real-Time Crash Risk Prediction Models and Transferability Analysis on Freeways

作　　者：游锦明 方守恩[1] 张兰芳[1] 折欣[1] YOU Jinming;FANG Shouen;ZHANG Lanfang;SHEXin(Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

机构地区：[1]同济大学道路与交通工程教育部重点实验室,上海201804

出　　处：《同济大学学报（自然科学版）》2019年第3期347-352,共6页Journal of Tongji University:Natural Science

基　　金：国家重点研发计划(2016YFC0802701)

摘　　要：通过G15沈海高速公路南通段上布设的高清卡口过车数据对路段上发生的实时事故风险进行研究.采用配对案例对照方法,结合基于随机森林的参数选取方法对3个子路段上的事故分别建立了支持向量机模型.结果表明,基于高清卡口采集的高分辨率过车数据构建的支持向量机模型相对既有研究中的模型而言其性能较优;对3个子路段分别构建的支持向量机模型进行可移植性分析发现各支持向量机模型均具有一定的可移植性,经过参数重新标定后可直接应用至邻近道路对其实时事故风险状态进行研判,并有着相对较高的预测精度.The paper aims to investigate the real-time crash risk based on the High Definition Monitoring System data on G15 Freeway in Nantong, China. Matched case-control method and parameter filtering method based on random forest were utilized to build SVM(support vector machine) models for the crashes on three sub-segments respectively. Results show that the SVM models based on high definition data collected by High Definition Monitoring System show better performance than those in existing studies. The transferability research was also conducted to verify the transferability of the proposed SVMs and results indicate that the models can be transferred to a certain extent. They could be applied in real-time crash prediction process on road segments nearby after the calibration of the parameters in the models and the transferred models have relatively higher prediction accuracy.

关 键 词：高速公路 实时研判 事故风险 支持向量机(SVM) 可移植性 

分 类 号：U491[交通运输工程—交通运输规划与管理]