{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:59:31Z","timestamp":1778345971271,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62206204"],"award-info":[{"award-number":["62206204"]}]},{"name":"National Natural Science Foundation of China","award":["2019CFB571"],"award-info":[{"award-number":["2019CFB571"]}]},{"name":"Natural Science Foundation of Hubei Province","award":["62206204"],"award-info":[{"award-number":["62206204"]}]},{"name":"Natural Science Foundation of Hubei Province","award":["2019CFB571"],"award-info":[{"award-number":["2019CFB571"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Considering the spatial and temporal correlation of traffic flow data is essential to improve the accuracy of traffic flow prediction. This paper proposes a traffic flow prediction model named Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU). In particular, the GRU is embedded with the DSC unit to enable the model to synchronously capture the spatiotemporal dependence. When considering spatial correlation, current prediction models consider only nearest-neighbor spatial features and ignore or simply overlay global spatial features. The DSC unit models the adjacent spatial dependence by the traditional static graph and the global spatial dependence through a novel dependency graph, which is generated by calculating the correlation between nodes based on the correlation coefficient. More than that, the DSC unit quantifies the different contributions of the adjacent and global spatial correlation with a modified gated mechanism. Experimental results based on two real-world datasets show that the DSC-GRU model can effectively capture the spatiotemporal dependence of traffic data. The prediction precision is better than the baseline and state-of-the-art models.<\/jats:p>","DOI":"10.3390\/ijgi12090366","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T09:59:31Z","timestamp":1693907971000},"page":"366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Gated Recurrent Unit Embedded with Dual Spatial Convolution for Long-Term Traffic Flow Prediction"],"prefix":"10.3390","volume":"12","author":[{"given":"Qingyong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5537-7504","authenticated-orcid":false,"given":"Huiwen","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingrong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","article-title":"Privacy-preserving traffic flow prediction: A federated learning approach","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3442","DOI":"10.1109\/TVT.2007.906878","article-title":"GSIS: A secure and privacy-preserving protocol for vehicular communications","volume":"56","author":"Lin","year":"2007","journal-title":"IEEE Trans. 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