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.