Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/118257
DC FieldValueLanguage
dc.contributorDepartment of Electrical and Electronic Engineering-
dc.creatorWu, H-
dc.creatorYao, J-
dc.creatorKang, P-
dc.creatorTan, C-
dc.creatorCai, Y-
dc.creatorZhou, J-
dc.creatorChung, E-
dc.creatorTang, K-
dc.date.accessioned2026-03-26T08:18:15Z-
dc.date.available2026-03-26T08:18:15Z-
dc.identifier.issn2168-0566-
dc.identifier.urihttp://hdl.handle.net/10397/118257-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectArrival flow profileen_US
dc.subjectLicense plate recognition (LPR) dataen_US
dc.subjectPlatoon dispersionen_US
dc.subjectShockwave reconstructionen_US
dc.subjectSignalized arterialsen_US
dc.titleArrival flow profile estimation and prediction for urban arterials using license plate recognition dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume13-
dc.identifier.issue1-
dc.identifier.doi10.1080/21680566.2025.2585060-
dcterms.abstractArrival flow profiles enable precise assessment of urban arterial dynamics and support signal control optimization. License plate recognition (LPR) data, with comprehensive coverage and event-based detection, are promising for reconstructing arrival flow profiles. This paper presents an arrival flow profile estimation and prediction method for urban arterials using LPR data. Unlike conventional methods assuming traffic homogeneity and ignoring wave features and signal timing impacts, our approach employs a time partition algorithm and platoon dispersion model to compute arrival flow using only boundary data. Shockwave theory defines the piecewise relation between arrival flow and profile. We further derive the link between arrival flow profiles and traffic dissipation at downstream intersections, enabling recursive estimation across all intersections. The method also predicts arrival flow profiles under various signal timing schemes. Validation through simulation and empirical cases demonstrates its robustness and reliable performance.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransportmetrica. B, Transport dynamics, 2025, v. 13, no. 1, 2585060-
dcterms.isPartOfTransportmetrica. B, Transport dynamics-
dcterms.issued2025-
dc.identifier.scopus2-s2.0-105023197597-
dc.identifier.eissn2168-0582-
dc.identifier.artn2585060-
dc.description.validate202603 bcjz-
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG001315/2026-02en_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis study was strongly supported by the Innovation and Technology Fund of HKSAR (Grant No. MHP/038/23), the National Key Research & Development Program of China (Grant No. 2023YFE0209300) and the National Natural Science Foundation of China Project (Grant No. 52372319). The authors would like to thank Prof. Hideki Nakamura from Nagoya University for providing the video analysis software.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2026-11-25en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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