Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

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datacite.relatedItem.firstPage243
datacite.relatedItem.issue6
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2220-9964
datacite.relatedItem.relationTypeIsPublishedIn
datacite.relatedItem.titleIJGI
datacite.relatedItem.volume8
dc.contributor.authorYong Han
dc.contributor.authorShukang Wang
dc.contributor.authorYibin Ren
dc.contributor.authorCheng Wang
dc.contributor.authorPeng Gao
dc.contributor.authorGe Chen
dc.date.accessioned2025-05-14T15:17:47Z
dc.date.available2025-05-14T15:17:47Z
dc.date.issued2019
dc.identifier.doihttps://doi.org/10.3390/ijgi8060243
dc.identifier.otherjz000156-0241
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/28252
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titlePredicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks
dc.typeArticle
dcat.distribution.pdfhttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/28253
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