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Article: Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns

TitleMemory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns
Authors
LIANG, YZhao, ZSun, L
Issue Date2022
Citation
Transportation Research Part C: Emerging Technologies, 2022, v. 143, p. 103826 How to Cite?
DOI: http://dx.doi.org/10.1016/j.trc.2022.103826
Persistent Identifierhttp://hdl.handle.net/10722/315866
ISI Accession Number ID
WOS:000841196300002

 

DC FieldValueLanguage
dc.contributor.authorLIANG, Y-
dc.contributor.authorZhao, Z-
dc.contributor.authorSun, L-
dc.date.accessioned2022-08-19T09:05:52Z-
dc.date.available2022-08-19T09:05:52Z-
dc.date.issued2022-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2022, v. 143, p. 103826-
dc.identifier.urihttp://hdl.handle.net/10722/315866-
dc.languageeng-
dc.relation.ispartofTransportation Research Part C: Emerging Technologies-
dc.titleMemory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns-
dc.typeArticle-
dc.identifier.emailZhao, Z: zhanzhao@hku.hk-
dc.identifier.authorityZhao, Z=rp02712-
dc.identifier.doi10.1016/j.trc.2022.103826-
dc.identifier.hkuros335431-
dc.identifier.volume143-
dc.identifier.spage103826-
dc.identifier.epage103826-
dc.identifier.isiWOS:000841196300002-

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