Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

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datacite.relatedItem.firstPage88
datacite.relatedItem.issue2
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datacite.relatedItem.relatedItemIdentifier2220-9964
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datacite.relatedItem.titleIJGI
datacite.relatedItem.volume11
dc.contributor.authorChi Zhang
dc.contributor.authorHong-Yu Zhou
dc.contributor.authorQiang Qiu
dc.contributor.authorZhichun Jian
dc.contributor.authorDaoye Zhu
dc.contributor.authorChengqi Cheng
dc.contributor.authorLiesong He
dc.contributor.authorGuoping Liu
dc.contributor.authorXiang Wen
dc.contributor.authorRunbo Hu
dc.date.accessioned2025-05-14T13:32:16Z
dc.date.available2025-05-14T13:32:16Z
dc.date.issued2022
dc.identifier.doihttps://doi.org/10.3390/ijgi11020088
dc.identifier.otherjz000147-0085
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/23528
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titleAugmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting
dc.typeArticle
dcat.distribution.pdfhttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/23529
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