{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:58:45Z","timestamp":1774630725184,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602354"],"award-info":[{"award-number":["61602354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876138"],"award-info":[{"award-number":["61876138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2019JM-227"],"award-info":[{"award-number":["2019JM-227"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Spatial\u2013temporal graph neural network has drawn more and more attention in recent years and is widely used to various real-world applications. However, learning the spatial\u2013temporal graph neural network structure presents unique challenges including: (i) the dynamic spatial correlation; (ii) the dynamic temporal correlation. Even the existing methods take into account the spatial correlation, they still learn the static road network structure information, which cannot reflect the dynamic of road relations. Some of the works has focused on modeling the long-term time series, but the improvements have been limited tightly. To overcome these challenges, we proposed a novel approach called Multi-View Spatial\u2013Temporal Graph Neural Network. Differ from the existing research, we designed a multi-view temporal transformer module to extract dynamic temporal correlation and enhance the expression of medium and long-term temporal features. We propose a multi-view spatial structure and a corresponding multi-view graph convolutional module, which are capable of simultaneously combining the features of static road network structure and dynamic changes. Compared with 11 baselines, our proposed model has achieved significant improvement in the accuracy of prediction.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac086","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T14:06:52Z","timestamp":1657116412000},"page":"2393-2408","source":"Crossref","is-referenced-by-count":11,"title":["Multi-View Spatial\u2013Temporal Graph Neural Network for Traffic Prediction"],"prefix":"10.1093","volume":"66","author":[{"given":"He","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology , Xidian University, Xi\u2019an, China"}]},{"given":"Duo","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , Xidian University, Xi\u2019an, China"}]},{"given":"XueJiao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , Xidian University, Xi\u2019an, China"}]},{"given":"HongJie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , Xidian University, Xi\u2019an, China"}]},{"given":"JinPeng","family":"Yun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , Xidian University, Xi\u2019an, China"}]},{"given":"LongJi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , Xidian University, Xi\u2019an, China"}]}],"member":"286","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"2023101421211798100_ref1","volume-title":"Proceedings of the Sixth International Conference on Learning Representations","author":"Li","year":"2018"},{"key":"2023101421211798100_ref2","first-page":"1907","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","author":"Zonghan","year":"2019"},{"key":"2023101421211798100_ref3","first-page":"3634","volume-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","author":"Bing","year":"2018"},{"key":"2023101421211798100_ref4","first-page":"339","volume-title":"Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence","author":"Zhang","year":"2018"},{"key":"2023101421211798100_ref5","volume-title":"The 9th ISCA Speech Synthesis Workshop","author":"Oord","year":"2016"},{"key":"2023101421211798100_ref6","first-page":"753","volume-title":"The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wu","year":"2020"},{"key":"2023101421211798100_ref7","first-page":"3529","volume-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence","author":"Chen","year":"2020"},{"key":"2023101421211798100_ref8","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1145\/3292500.3330884","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery Data Mining","author":"Pan","year":"2019"},{"key":"2023101421211798100_ref9","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TITS.2013.2247040","article-title":"Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning","volume":"14","author":"Lippi","year":"2013","journal-title":"IEEE Trans. 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