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
投稿时间:2018-04-27  修订日期:2018-12-24
DOI:10.11908/j.issn.0253-374x.2019.03.007     稿件编号:    中图分类号:U491
 
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中文摘要
      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.
英文摘要
      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.
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