Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Sampled Trajectory Data

Abstract

Queue length estimation is an important component of intersection performance measurement. Different approaches based on different data sources have been presented. With the latest developments in vehicle detection technologies, especially probe vehicle technologies, use of vehicle trajectory data has become possible. In this paper, an improved method for queue length estimation for signalized intersections is proposed. This method is able to provide cycle-by-cycle queue length estimation for signalized intersections with sampled vehicle trajectories as the only input. The keystone of the entire approach is the concept of the critical point (CP), which represents the changing vehicle dynamics. A CP extraction algorithm is introduced to identify CPs from raw trajectories. Using the CPs related to queue formation and dissipation, the authors propose an improved queue length estimation method based on shock waves. The performance of this approach is evaluated with several data sets under different flow and signal timing scenarios, including a recently collected data set from a Global Positioning System logger. The results indicate that this trajectory-based approach is promising.