Real-Time Prediction of Lane-Based Queue Lengths for Signalized Intersections

Abstract

Queue length is one of the most important traffic evaluation indexes for traffic signal control at signalized intersections. Most previous studies have focused on estimating queue length, which cannot be predicted effectively. In this paper, we applied the Lighthill–Whitham–Richards shockwave theory and Robertson’s platoon dispersion model to predict the arrival of vehicles in advance at intervals of 5 seconds. This approach fully described the relationship between disparate upstream traffic arrivals (as a result of vehicles making different turns) and the variation of incremental queue accumulation. It also addressed the shortcomings of the uniform arrival assumption in previous research. In addition, to predict the queue length of multiple lanes at the same time, we integrated the prediction of the traffic volume proportions in each lane using the Kalman filter. We tested this model in a field experiment, and the results showed that the model had satisfactory accuracy. We also discussed the limitations of the proposed model in this paper.