基于卡尔曼滤波的城市快速路交通密度估计与拥堵识别

针对城市快速路网中只有部分路段检测器可用的情况,为准确地估计交通密度并基于此快速识别路网所有路段的交通拥堵情况,研究了基于宏观交通流模型的卡尔曼滤波器设计方法.结合动态图混杂自动机(DGHA)与元胞传输模型(CTM)对快速路网建模,在此基础上推导出分段仿射线性系统(PWALS)模型.基于所得到的模型设计出切换型卡尔曼滤波器进行交通密度估计,并通过将路段密度估计值与临界拥堵密度进行对比来对快速路网的拥堵进行识别.以京通快速路为例进行实验,结果表明,真实值与估计值的平均绝对误差为MAE=0.625 988,显示了所提方法的有效性. 

A Traffic Density Estimation and Congestion Identification of Urban Freeways Based on Kalman Filter

For the situation where only a part of traffic detectors are available to obtain traffic information in urban freeway networks,a Kalman filter based ona macroscopic traffic flow model is studied in order to accurately estimate traffic density,and moreover,to quickly identify traffic congestion of all road sections.A macroscopic traffic flow model of urban freeway networks is developed by combining Dynamic Graph Hybrid Automata (DGHA) with Cell Transmission Model (CTM),and a Piecewise Affine Linear System (PWALS) model is deduced.Traffic density is estimated in the switched Kalman filter designed by this model,and congestion of urban freeway networks can be identified by comparing the road density estimation with the critical congestion density.The experiment takes Jingtong freeway in Beijing as a case study,and the Mean Absolute Error (MAE) which is generated by estimated value and actual value is 0.625 988.The resuits indicate the effectiveness of the proposed method.