Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88227
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorMa, Wen_US
dc.creatorPi, Xen_US
dc.creatorQian, Sen_US
dc.date.accessioned2020-09-28T01:46:52Z-
dc.date.available2020-09-28T01:46:52Z-
dc.identifier.issn0968-090Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/88227-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsThe following publication Ma, W., Pi, X., & Qian, S. (2020). Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs. Transportation Research Part C: Emerging Technologies, 119, 102747 is available at https://doi.org/10.1016/j.trc.2020.102747en_US
dc.subjectDynamic networksen_US
dc.subjectMachine learningen_US
dc.subjectMulti-source dataen_US
dc.subjectNeural networken_US
dc.subjectO-D estimationen_US
dc.titleEstimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage30en_US
dc.identifier.volume119en_US
dc.identifier.doi10.1016/j.trc.2020.102747en_US
dcterms.abstractTransportation networks are unprecedentedly complex with heterogeneous vehicular flow. Conventionally, vehicles are classified by size, the number of axles or engine types, e.g., standard passenger cars versus trucks. However, vehicle flow heterogeneity stems from many other aspects in general, e.g., ride-sourcing vehicles versus personal vehicles, human driven vehicles versus connected and automated vehicles. Provided with some observations of vehicular flow for each class in a large-scale transportation network, how to estimate the multi-class spatio-temporal vehicular flow, in terms of time-varying Origin-Destination (OD) demand and path/link flow, remains a big challenge. This paper presents a solution framework for multi-class dynamic OD demand estimation (MCDODE) in large-scale networks that work for any vehicular data in general. The proposed framework cast the standard OD estimation methods into a computational graph with tensor representations of spatio-temporal flow and all intermediate features involved in the MCDODE formulation. A forward-backward algorithm is proposed to efficiently solve the MCDODE formulation on computational graphs. In addition, we propose a novel concept of tree-based cumulative curves to compute the exact multi-class Dynamic Assignment Ratio (DAR) matrix. A Growing Tree algorithm is developed to construct tree-based cumulative curves. The proposed framework is examined on a small network, a mid-size network as well as a real-world large-scale network. The experiment results indicate that the proposed framework is compelling, satisfactory and computationally plausible.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part C, Emerging technologies, Oct 2020, v. 119, 102747, p. 1-30en_US
dcterms.isPartOfTransportation research. Part C, Emerging technologiesen_US
dcterms.issued2020-10-
dc.identifier.scopus2-s2.0-85089847740-
dc.identifier.artn102747en_US
dc.description.validate202009 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0988-n01, OA_Othersen_US
dc.identifier.SubFormID2363-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
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