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

Yu Mei, Weihua Gu, Edward C.S. Chung, Fuliang Li, Keshuang Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

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%.

Original languageEnglish
Pages (from-to)233-249
Number of pages17
JournalTransportation Research Part C: Emerging Technologies
Volume109
DOIs
Publication statusPublished - Dec 2019

Keywords

  • Bayesian approach
  • Expectation maximum algorithm
  • Probe vehicles
  • Queue length estimation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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