快速路交通流运行安全关键参数识别与评估

Key Variables Identification and Proactive Assessment of Real time Traffic Flow Accident Risk on Urban Expressway

摘要:

基于上海市两条快速路采集的事故数据和相应检测器数据，应用随机森林模型对事故发生前5~10 min内的交通流数据进行重要变量筛选.利用基于高斯混合模型和最大期望算法的贝叶斯网络(BN)模型对快速路实时交通流事故风险进行建模分析，并对建立的BN模型进行了可转移性测试.结果表明：选取重要变量后建立的BN模型效果优于使用直接检测数据建立的模型，事故预测准确率达到82.78%；可转移性测试中BN模型的事故预测准确率虽有所下降，但整体预测精度和事故预测精度仍都优于利用直接检测数据建立的模型.

Abstract:

Based on accident data and detector data collected on two expressways in Shanghai, important variables for model construction were selected from the data of traffic flow within 5~10 min before the accident with random forest model. Then, the Bayesian network (BN) model based on the Gaussian mixture model and expected maximum algorithm was established for the analysis of real time traffic flow state and accident risk. Meanwhile, the transferability of BN model was also assessed. The results show that BN model built with selected important variables is better than that with direct detection data, with the accident prediction accuracy rate of 82.78%. The results of the transferability show that the improved BN model is still better than the traditional model, though the accident prediction accuracy of BN model decreases.