A hidden Markov model for the estimation of correlated queues in probe vehicle environments
Queue length estimation is critical for traffic signal control and performance measures. With the development of connected vehicle technologies and the popularization of ride-hailing services, probe vehicle data are now being collected on a large scale. Some studies have shown that queue lengths can be estimated using only probe vehicle data. The relevant literature usually assumes the queue lengths in different traffic signal cycles are independent and identically distributed or treats the queues independently. However, in the real world, the queue lengths in different cycles might be correlated. For instance, when there exists an overflow queue, the queue length in the following cycle is correlated with the queue length in the previous cycle. In fact, the correlation of different cycles can provide additional information and thus improve the queue length estimation accuracy. In this paper, the authors model such queueing processes in probe vehicle environments using a hidden Markov model (HMM), where the queue length in each cycle is a hidden state, and the observed pattern of probe vehicles is an observation. Based on the HMM, the authors propose two novel cycle-by-cycle queue length estimation methods. In the case where the parameters of the HMM are unknown, the authors also provide an algorithm that can estimate the parameters from historical probe vehicle data. Validation results show that the proposed cycle-by-cycle queue length estimation methods outperform the existing methods, and the parameter learning algorithm can estimate the parameters adequately.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhao, Yan
- Shen, Shengyin
- Liu, Henry X
- Publication Date: 2021-7
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 103128
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 128
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Algorithms; Length; Markov chains; Mathematical models; Maximum likelihood method; Probe vehicles; Queuing; Traffic queuing
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01778479
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 30 2021 12:36PM