Real-time flux and density estimation of freeway traffic with decentralized speed data

Big GPS data from the moving vehicles allow understanding the transportation systems in more details and at a vehicle level. While only depending on the trajectory and speed data from probe vehicles cannot estimate the traffic states (vehicle densities etc.) in real-time with the classical filtering methods, such as particular filtering (PF) and extended Kalman filtering (EKF), because the boundary fluxes of the considered road network are not available in advance. In this paper, we firstly build the traffic flow dynamics of freeway traffic as the discrete-time state-space models including unknown input variables (boundary fluxes). Then a novel simultaneous state and input estimation technique is developed by using the decentralized heterogeneous data of vehicle speed as measurement. A freeway link of Interstate 80 East (I-80E) in Emeryville, Northern California, is chosen to investigate the performance of the developed filtering approach. Traffic data are obtained from the Performance Measurement System (PeMS) and the Mobile Century experiment.