Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network

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dc.authorid0000-0003-3062-4140
dc.authorid0000-0002-7201-6963
dc.authorid0000-0003-1840-9958
dc.contributor.authorZhang, Dongran
dc.contributor.authorYan, Jiangnan
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorLi, Jun
dc.date.accessioned2024-09-25T20:00:08Z
dc.date.available2024-09-25T20:00:08Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü en_US
dc.description.abstractTraffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance.en_US
dc.description.sponsorshipScience and Technology Planning Project of Guangdong Province, China [2023B1212060029]en_US
dc.description.sponsorshipThis work was supported financially by the Science and Technology Planning Project of Guangdong Province, China, grant number 2023B1212060029.en_US
dc.identifier.doi10.1016/j.aei.2024.102533
dc.identifier.issn1474-0346
dc.identifier.issn1873-5320
dc.identifier.scopus2-s2.0-85190735017en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.aei.2024.102533
dc.identifier.urihttps://hdl.handle.net/20.500.12491/14088
dc.identifier.volume62en_US
dc.identifier.wosWOS:001271984200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofAdvanced Engineering Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectTraffic Flow Predictionen_US
dc.subjectMultimodal Joint Predictionen_US
dc.subjectSparse Attention Mechanismen_US
dc.subjectBidirectional Temporal Convolutionalen_US
dc.subjectModel Consistently
dc.subjectTop-U
dc.titleMultimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional networken_US
dc.typeArticleen_US

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