A Bayesian approach for estimating vehicle queue lengths at signalized intersections using probe vehicle data

Abstract

A novel Bayesian approach is proposed for estimating the maximum queue lengths of vehicles at signalized intersections using high-frequency trajectory data of probe vehicles. The queue length estimates are obtained from a distribution estimated over several neighboring cycles via a maximum a posteriori method. An expectation maximum algorithm is proposed for efficiently solving the estimation problem. Through a battery of simulation experiments and a real-world case study, the proposed approach is shown to produce more accurate and robust estimates than two benchmark estimation methods. Fairly good accuracy is achieved even when the probe vehicle penetration rate is 2%.