Real-Time Queue Length Estimation With Trajectory Reconstruction Using Surveillance Data

This paper presents a new method to estimate real-time queue lengths at a signalized intersection by utilizing limited data extracted from surveillance videos. This method focuses on reconstructing vehicles' trajectories on an entire road segment. The real-time queue length can be derived from these reconstructed trajectories. In order to improve the accuracy of the trajectory reconstruction, a built-up car-following model is proposed to reconstruct the trajectories of vehicles joining and leaving the queue, respectively, which are further corrected by a fusion algorithm. The proposed method was validated in the Next Generation Simulation dataset, and the queue length for each signal cycle can be estimated with a high accuracy. The results show that the proposed method has a higher precision compared to three baseline models.