DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.contributorResearch Institute for Sustainable Urban Development-
dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorLiu, Xen_US
dc.creatorWu, Jen_US
dc.creatorHuang, Jen_US
dc.creatorZhang, Jen_US
dc.creatorChen, BYen_US
dc.creatorChen, Aen_US
dc.date.accessioned2021-04-13T06:08:05Z-
dc.date.available2021-04-13T06:08:05Z-
dc.identifier.issn0966-6923en_US
dc.identifier.urihttp://hdl.handle.net/10397/89571-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectGeographically and temporally weighted regressionen_US
dc.subjectPublic transporten_US
dc.subjectSmart card dataen_US
dc.subjectSpatiotemporal heterogeneityen_US
dc.titleSpatial-interaction network analysis of built environmental influence on daily public transport demanden_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage13en_US
dc.identifier.volume92en_US
dc.identifier.doi10.1016/j.jtrangeo.2021.102991en_US
dcterms.abstractMany studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services.-
dcterms.accessRightsembargoed access-
dcterms.bibliographicCitationJournal of transport geography, Apr. 2021, v. 92, 102991, p. 1-13, https://doi.org/10.1016/j.jtrangeo.2021.102991en_US
dcterms.isPartOfJournal of transport geographyen_US
dcterms.issued2021-04-
dc.identifier.scopus2-s2.0-85101829528-
dc.identifier.artn102991en_US
dc.description.validate202104 bcvc-
dc.description.oaNot applicable-
dc.identifier.FolderNumbera0698-n04-
dc.identifier.SubFormID1057-
dc.description.fundingSourceRGC-
dc.description.fundingSourceOthers-
dc.description.fundingText1-PP5Q from Research Grants Council (RGC) Hong Kong-
dc.description.fundingText1-BBWD from the Research Institute for Sustainable Urban Development, the Hong Kong Polytechnic University, 1-BBWF from the Research Institute for Sustainable Urban Development, the Hong Kong Polytechnic University.-
dc.description.pubStatusEarly release-
dc.date.embargo2023.04.30en_US
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