{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:20:47Z","timestamp":1785856847773,"version":"3.56.0"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.<\/jats:p>","DOI":"10.3390\/s23062897","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T02:08:14Z","timestamp":1678241294000},"page":"2897","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction"],"prefix":"10.3390","volume":"23","author":[{"given":"Junhua","family":"Gu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1941-9269","authenticated-orcid":false,"given":"Zhihao","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taotao","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba 4350, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Song","sequence":"additional","affiliation":[{"name":"School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3526-9037","authenticated-orcid":false,"given":"Adnan","family":"Mahmood","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fang, X., Huang, J., Wang, F., Zeng, L., Liang, H., and Wang, H. 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