考虑货车因素的高速公路短期交通流风险预测

基于G15上海段的交通流数据和交通事故数据,研究货车比例较高且货车事故率较高的高速公路短期交通流风险预测模型.分别选取整体交通流参数、货车交通流参数和综合参数作为特征变量,通过支持向量机进行建模,运用遗传算法对模型参数进行寻优,建立不同时间段、不同风险特征变量的分类模型并对比分析.结果表明事故发生前5~10min的模型预测精度最高.当加入货车因素时,总体的预测精度提高7.1%,事故预测精度提高6.1%,误报率降低4.7%.采用平均影响值法进行货车因素对预测结果的影响程度分析,表明货车因素对于预测模型有较大影响.该研究模型可用来开发交通安全预警系统,为高速公路货车安全管理提供理论依据.

Short-term traffic flow risk prediction on freeways based on truck factors

Based on the traffic data and crash data collected on G15, this paper studied short-term traffic flow risk prediction model on freeways with high proportion of trucks and high proportion of truck crashes. The overall traffic flow parameters, the truck traffic flow parameters and the comprehensive parameters were selected as the risk characteristic variables. The support vector machine was adopted for the modeling and genetic algorithm was used to optimize the parameters. Classification models of different time periods, different risk characteristics variables were got and compared. The results show that the model using the data within 5 to 10 minutes before the accident performs the best. When considering truck factors,the overall prediction accuracy improves 7.1% , the crash rate prediction accuracy improves 6.6% and the false alarm rate is 7.7% lower. Finally, the different importance of characteristic variables was obtained through mean impact value. The results show that truck factors have larger effects on the prediction model. The model in this research can be used to developSearly warningSsystem of traffic security and provide theoretical basis of truck safety management on freeways.