ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Junho | - |
dc.contributor.author | Son, Jiwon | - |
dc.contributor.author | Seo, Dong-hyuk | - |
dc.contributor.author | Han, Kyungsik | - |
dc.contributor.author | Kim, Namhyuk | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2023-11-24T04:44:19Z | - |
dc.date.available | 2023-11-24T04:44:19Z | - |
dc.date.created | 2023-11-14 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192912 | - |
dc.description.abstract | Spatio-temporal models, which combine GNNs (Graph Neural Networks) and RNNs (Recurrent Neural Networks), have shown state-of-the-art accuracy in traffic speed prediction. However, we find that they consider the spatial and temporal dependencies between speeds separately in the two (i.e., space and time) dimensions, thereby unable to exploit the joint-dependencies of speeds in space and time. In this paper, with the evidence via preliminary analysis, we point out the importance of considering individual dependencies between two speeds from all possible points in space and time for accurate traffic speed prediction. Then, we propose an Individual Spatio-Temporal graph (IST-graph) that represents the Individual Spatio-Temporal dependencies (IST-dependencies) very effectively and a Spatio-Temporal Graph ATtention network (ST-GAT), a novel model to predict the future traffic speeds based on the IST-graph and the attention mechanism. The results from our extensive evaluation with five real-world datasets demonstrate (1) the effectiveness of the IST-graph in modeling traffic speed data, (2) the superiority of ST-GAT over 5 state-of-the-art models (i.e., 2-33% gains) in prediction accuracy, and (3) the robustness of our ST-GAT even in abnormal traffic situations. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Han, Kyungsik | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3511808.3557705 | - |
dc.identifier.scopusid | 2-s2.0-85140847210 | - |
dc.identifier.wosid | 001074639604106 | - |
dc.identifier.bibliographicCitation | PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022, pp.4500 - 4504 | - |
dc.relation.isPartOf | PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022 | - |
dc.citation.title | PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022 | - |
dc.citation.startPage | 4500 | - |
dc.citation.endPage | 4504 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | Attention network | - |
dc.subject.keywordPlus | Graph neural networks | - |
dc.subject.keywordPlus | Space and time | - |
dc.subject.keywordPlus | Spatio-temporal data | - |
dc.subject.keywordPlus | Spatio-temporal graphs | - |
dc.subject.keywordPlus | Spatio-temporal models | - |
dc.subject.keywordPlus | Speed prediction | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Traffic speed | - |
dc.subject.keywordPlus | Traffic speed prediction | - |
dc.subject.keywordAuthor | Traffic speed prediction | - |
dc.subject.keywordAuthor | Attention network | - |
dc.subject.keywordAuthor | Spatio-temporal data | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3511808.3557705 | - |
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