Real-Time Queue Length Estimation for Signalized Intersections Using Single-Channel Advance Detector Data

Abstract

Queue length is one of the most important metrics required for the performance assessment of signalized intersections. However, the current methodology of estimating queue length in the literature suffers from several drawbacks, including unstable estimation and the requirement for multiple data sources. Moreover, for single-channel advance detection, which is a common detection configuration for signal control in many U.S. cities, manual parameter calibration is required. To bridge these gaps, this study proposes a cycle-based maximum queue length estimation method based on: (a) the empirical observation of breakpoints in the time gap between successive actuations; and (b) the identification of queue status for all detector actuations in a cycle. Maximum queue length for cycles with long queues is estimated based on the saturation flow rate and the trajectory of the last vehicle in the queue. The proposed methodology was implemented on two study intersections in Tucson, Arizona. Results showed that using the proposed method, queue length can be estimated with mean absolute percentage errors of 14.77% and 15.1% and mean absolute errors of 25 ft and 42.5 ft. The results showed significant improvement in queue length estimation from single-channel detection data when compared with similar methods in the literature. The proposed method can help transportation agencies accurately estimate queue length at intersections with single-channel advance detection without the need for manual field data collection and without installing lane-by-lane detection.