A Bayesian approach for estimating vehicle queue lengths at signalized intersections using probe vehicle data. (December 2019)

• Record Type: Journal Article
• Authors: Mei, Yu; Gu, Weihua; Chung, Edward C.S.; Li, Fuliang; Tang, Keshuang
• Is Part Of: Transportation research. Volume 109(2019)
• Page Start: 233
• Page End: 249
• Publication Date: 2019-12
• Subjects: Queue length estimation -- Probe vehicles -- Bayesian approach -- Expectation maximum algorithm; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.10.006
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A novel Bayesian approach is proposed for estimating queue lengths at signalized intersections. High-frequency probe vehicle trajectory data are used. Both the queue length and the discharging shockwave speed are modelled stochastically. An efficient expectation maximum algorithm is developed. Estimates are accurate and robust even under low penetration of probe vehicles. 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%.