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dc.contributor.authorJiang, Xinke
dc.contributor.authorZhuang, Dingyi
dc.contributor.authorZhang, Xianghui
dc.contributor.authorChen, Hao
dc.contributor.authorLuo, Jiayuan
dc.contributor.authorGao, Xiaowei
dc.date.accessioned2023-11-02T18:53:35Z
dc.date.available2023-11-02T18:53:35Z
dc.date.issued2023-10-21
dc.identifier.isbn979-8-4007-0124-5
dc.identifier.urihttps://hdl.handle.net/1721.1/152629
dc.publisherACM|Proceedings of the 32nd ACM International Conference on Information and Knowledge Managementen_US
dc.relation.isversionofhttps://doi.org/10.1145/3583780.3615215en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleUncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Predictionen_US
dc.typeArticleen_US
dc.identifier.citationJiang, Xinke, Zhuang, Dingyi, Zhang, Xianghui, Chen, Hao, Luo, Jiayuan et al. 2023. "Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning
dc.identifier.mitlicensePUBLISHER_CC
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.updated2023-11-01T07:48:16Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2023-11-01T07:48:17Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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