Queue Length Estimation on Urban Signalized Intersection Combining Automatic Vehicle Identification and Vehicle Trajectory Data

Abstract

Queue length is one of the indicators of the state of traffic and is often used to measure the operational state of signalized intersections. Many studies have proposed estimating queue length from vehicle trajectory data (e.g., floating car GPS data); however, its sparse spatio-temporal distribution and low sampling frequency present substantial challenges in practice. In some jurisdictions, the widespread deployment of automatic vehicle identification (AVI) technologies presents the opportunity to improve queue length estimation at signalized intersections by combining AVI and trajectory data from floating (probe) vehicles. The method proposed in this paper is applicable for both under and oversaturated traffic conditions, is evaluated using field data [Next Generation Simulation (NGSIM) data set] and simulation data, and is compared to ground truth and the method proposed by the author Tan. The results from the field data evaluation indicate that the method provides a good estimation of the queue size (mean average error less than three vehicles for a floating vehicle penetration rate of 5% and a GPS sampling interval of 10 s). The simulation data evaluation indicated that the proposed method performs better than the Tan’s method.