{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:02:06Z","timestamp":1769709726465,"version":"3.49.0"},"reference-count":23,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p>The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics.<\/jats:p>","DOI":"10.3233\/jifs-224285","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T11:40:30Z","timestamp":1680867630000},"page":"10437-10450","source":"Crossref","is-referenced-by-count":3,"title":["Spatial-temporal gated graph convolutional network: a new deep learning framework for long-term traffic speed forecasting"],"prefix":"10.1177","volume":"44","author":[{"given":"Dongping","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, China Jiliang University, Hangzhou, China"}]},{"given":"Hao","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Information Engineering, China Jiliang University, Hangzhou, China"}]},{"given":"Zhennan","family":"Ma","sequence":"additional","affiliation":[{"name":"Books and Information Center, Zhejiang Institute of Communications, Hangzhou, China"}]},{"given":"Zhixiong","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiaxing Sudoku Bridge Technology Co., Ltd., Jiaxing, China"}]},{"given":"Xin","family":"Wu","sequence":"additional","affiliation":[{"name":"China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou, China"}]},{"given":"Xiaoling","family":"Huang","sequence":"additional","affiliation":[{"name":"Library, Zhejiang University of Finance and Economics, Hangzhou, China"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-224285_ref1","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.3233\/JIFS-179916","article-title":"Predicting short-term traffic flow in urban based on multivariate linear regression model","volume":"39","author":"Li","year":"2020","journal-title":"Journal of 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