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Article: Pedestrian volume prediction with high spatiotemporal granularity in urban areas by the enhanced learning model

TitlePedestrian volume prediction with high spatiotemporal granularity in urban areas by the enhanced learning model
Authors
Jiang, FMa, JLI, Z
Issue Date2022
Citation
Sustainable Cities and Society, 2022, v. 79, p. 103653 How to Cite?
DOI: http://dx.doi.org/10.1016/j.scs.2021.103653
Persistent Identifierhttp://hdl.handle.net/10722/311822
ISI Accession Number ID
WOS:000781384900004

 

DC FieldValueLanguage
dc.contributor.authorJiang, F-
dc.contributor.authorMa, J-
dc.contributor.authorLI, Z-
dc.date.accessioned2022-04-01T09:13:39Z-
dc.date.available2022-04-01T09:13:39Z-
dc.date.issued2022-
dc.identifier.citationSustainable Cities and Society, 2022, v. 79, p. 103653-
dc.identifier.urihttp://hdl.handle.net/10722/311822-
dc.languageeng-
dc.relation.ispartofSustainable Cities and Society-
dc.titlePedestrian volume prediction with high spatiotemporal granularity in urban areas by the enhanced learning model-
dc.typeArticle-
dc.identifier.emailJiang, F: ffjiang@hku.hk-
dc.identifier.emailMa, J: junma@hku.hk-
dc.identifier.authorityMa, J=rp02719-
dc.identifier.doi10.1016/j.scs.2021.103653-
dc.identifier.hkuros332273-
dc.identifier.volume79-
dc.identifier.spage103653-
dc.identifier.epage103653-
dc.identifier.isiWOS:000781384900004-

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