DC FieldValueLanguage
dc.contributorDepartment of Electrical Engineering-
dc.creatorMei, Y-
dc.creatorGu, W-
dc.creatorChung, ECS-
dc.creatorLi, F-
dc.creatorTang, K-
dc.date.accessioned2021-05-13T08:32:49Z-
dc.date.available2021-05-13T08:32:49Z-
dc.identifier.issn0968-090X-
dc.identifier.urihttp://hdl.handle.net/10397/89935-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectBayesian approachen_US
dc.subjectExpectation maximum algorithmen_US
dc.subjectProbe vehiclesen_US
dc.subjectQueue length estimationen_US
dc.titleA Bayesian approach for estimating vehicle queue lengths at signalized intersections using probe vehicle dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage233-
dc.identifier.epage249-
dc.identifier.volume109-
dc.identifier.doi10.1016/j.trc.2019.10.006-
dcterms.abstractA 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%.-
dcterms.accessRightsembargoed access-
dcterms.bibliographicCitationTransportation research. Part C, Emerging technologies, Dec. 2019, v. 109, p. 233-249-
dcterms.isPartOfTransportation research. Part C, Emerging technologies-
dcterms.issued2019-12-
dc.identifier.scopus2-s2.0-85074636459-
dc.description.validate202105 bcvc-
dc.description.oaNot applicable-
dc.identifier.FolderNumbera0783-n09-
dc.identifier.SubFormID1708-
dc.description.fundingSourceRGC-
dc.description.fundingSourceOthers-
dc.description.fundingTextRGC: General Research Funds 15217415,General Research Fund 15224317-
dc.description.fundingTextOthers: P0001008-
dc.description.pubStatusPublished-
dc.date.embargo2021.12.31en_US
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