基于支持向量机的高速公路实时事故风险研判

Support Vector Machines Approach for Predicting Real-time Rear-end Crash Risk on Freeways

采用G60高速公路(上海段)上布设的单组线圈检测器检测的车道级交通流数据对该路段上发生追尾事故可能性进行研究.通过配对案例对照的方法,分别对事故前5~10min,10~15min和15~20min的交通流数据建立了追尾事故实时预测支持向量机模型.结论表明基于事故前5~10min的交通流数据构建的支持向量机分类器能够有效的对事故进行实时预测,总体事故预测精度为84.85%,误报率为0.33%,该支持向量机分类器具有较高的实用价值,同时也表明了基于单流量检测器的交通流数据对事故进行实时预测的可靠性.

The paper aims to study the relationship between rear-end crash potential and lane-level traffic data collected by single pair of loop detectors located on the G60 Freeway in Shanghai, China. The matched case-control method with support vector machines was applied to modelling the traffic data of different time slices before the crashes respectively, 5 -10minutes, 10-15 minutes and 15-20 minutes before the crash. Results indicate that support vector machines classifiers based on the traffic data of 5-10 minutes before the crashes have the highest crash prediction accuracy of 84. 85% and a false alarming rate 0.33%. The model proves to be valid to predict the real-time crash risk, which is helpful in freeway traffic management.