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dc.contributor.authorZhuang, Dingyi
dc.contributor.authorWang, Shenhao
dc.contributor.authorKoutsopoulos, Haris
dc.contributor.authorZhao, Jinhua
dc.date.accessioned2022-11-09T18:38:27Z
dc.date.available2022-11-09T18:38:27Z
dc.date.issued2022-08-14
dc.identifier.isbn978-1-4503-9385-0
dc.identifier.urihttps://hdl.handle.net/1721.1/146261
dc.publisherACM|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining USBen_US
dc.relation.isversionofhttps://doi.org/10.1145/3534678.3539093en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceACM|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining USBen_US
dc.titleUncertainty Quantification of Sparse Trip Demand Prediction with Spatial-Temporal Graph Neural Networksen_US
dc.typeArticleen_US
dc.identifier.citationZhuang, Dingyi, Wang, Shenhao, Koutsopoulos, Haris and Zhao, Jinhua. 2022. "Uncertainty Quantification of Sparse Trip Demand Prediction with Spatial-Temporal Graph Neural Networks."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Human Dynamics Group
dc.contributor.departmentMassachusetts Institute of Technology. School of Architecture and Planning
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-11-03T01:05:58Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2022-11-03T01:05:59Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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