{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T20:53:27Z","timestamp":1779310407394,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hubei Province","award":["2019CFB571"],"award-info":[{"award-number":["2019CFB571"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial\u2013temporal relationships. Although the existing methods have researched spatial\u2013temporal relationships, they neglect the long periodic aspects of traffic flow data, and thus cannot attain a satisfactory result. In this paper, we propose a novel model Attention-Based Spatial\u2013Temporal Convolution Gated Recurrent Unit (ASTCG) to solve the traffic flow forecasting problem. ASTCG has two core components: the multi-input module and the STA-ConvGru module. Based on the cyclical nature of traffic flow data, the data input to the multi-input module are divided into three parts, near-neighbor data, daily-periodic data, and weekly-periodic data, thus enabling the model to better capture the time dependence. The STA-ConvGru module, formed by CNN, GRU, and attention mechanism, can capture both temporal and spatial dependencies of traffic flow. We evaluate our proposed model using real-world datasets and experiments show that the ASTCG model outperforms the state-of-the-art model.<\/jats:p>","DOI":"10.3390\/e25060938","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T02:28:56Z","timestamp":1686796136000},"page":"938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Attention-Based Spatial\u2013Temporal Convolution Gated Recurrent Unit for Traffic Flow Forecasting"],"prefix":"10.3390","volume":"25","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"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8853-6841","authenticated-orcid":false,"given":"Wanfeng","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Conghui","family":"Yin","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\/0000-0001-8240-8293","authenticated-orcid":false,"given":"Peng","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meifang","family":"Tan","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":[[2023,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TITS.2011.2158001","article-title":"Data-Driven Intelligent Transportation Systems: A Survey","volume":"12","author":"Zhang","year":"2011","journal-title":"IEEE Trans. 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