城市快速路实时交通流运行安全主动风险评估

基于上海市快速路系统采集的线圈检测器数据和事故数据,应用贝叶斯网络(BN)模型对快速路实时交通流参数与事故风险进行建模分析,并利用可有效应对缺失数据的高斯混合模型和最大期望算法分别对BN模型输入和参数进行估计,进而主动评估快速路实时交通流运行安全风险,并对事故状态提前做出预警.分别对可能事故点前后2组检测器和4个时间段的8组交通流数据进行了建模,结果表明使用事故发生地点上下游各一个检测器在事故发生前5~10min内的交通流数据建立的BN模型效果最好,事故预测准确率为76.94%.最后不仅与朴素贝叶斯分类、K近邻、反向传播(BP)神经网络等经典事故风险估计算法进行了对比分析,还与现有的实时风险评估成果进行了对比,结果表明BN模型预测效果最好.

Proactive Assessment of Real time Traffic Flow Accident Risk on Urban Expressway

Based on dual loop detector data and accident data collected on Shanghai expressway, the Bayesian networks (BN) model was adopted for the modeling and analysis of real time traffic flow parameters and accident risk on expressways. Gaussian mixture model and the expectation maximization algorithm which could effectively deal with the missing data were also used in the parameters estimation of BN model. Then real time traffic safety risk was evaluated, and accident warning could be carried out in advance. Different combinations of dual loop detector data and time segments before accidents were used to develop the optimal accident risk estimation model by BN. The results show that the BN model adopting the nearest detector data upstream and downstream of the accident site within 5 to 10 minutes before the accident performs the best and the accident prediction accuracy is up to 76.94%. At last, a comparative study was made of the classical accident risk estimation algorithms including naive Bayes classifier, K nearest neighbor and back propagation (BP) neural network as well as the existing real time risk assessment studies. And the results show that the BN model obtains the best predictive results.