Vehicle trajectory reconstruction for signalized intersections: A hybrid approach integrating Kalman Filtering and variational theory
Peng Chen, Lei Wei, Fangfang Meng, Nan Zheng
Abstract
Trajectory data provide a rich source of information on spatial and temporal speed fluctuations of individual vehicles. To assemble a complete picture of traffic operations, obtaining vehicle trajectories of the entire traffic flow is essential. The current techniques cannot fulfill this need sufficiently. To this end, we developed a hybrid approach for reconstructing vehicle trajectories at signalized intersections that accounts for queue dynamics. Specifically, an extended variational solution network was established and Kalman Filtering was applied to capture and reproduce the stochastic properties of queue boundary curves. The proposed method was demonstrated using both NGSIM data and simulation data. Results showed that the hybrid approach reproduces accurately the shockwave boundaries and trajectory reconstruction. By fusing different sources of traffic data, the proposed method does not rely on the common assumption of uniform arrival between adjacent probe vehicles, which is the main drive for estimation improvement.