{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:26:56Z","timestamp":1781015216768,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62062033"],"award-info":[{"award-number":["62062033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The spatial\u2013temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial\u2013temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.<\/jats:p>","DOI":"10.3390\/s23020841","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T05:26:31Z","timestamp":1673414791000},"page":"841","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaohui","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Information Engineering, East China Jiaotong University, Nanchang 330000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, East China Jiaotong University, Nanchang 330000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanchun","family":"Lan","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, East China Jiaotong University, Nanchang 330000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaojie","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, East China Jiaotong University, Nanchang 330000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhua","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, East China Jiaotong University, Nanchang 330000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s41019-020-00151-z","article-title":"A survey of traffic prediction: From spatio-temporal data to intelligent transportation","volume":"6","author":"Yuan","year":"2021","journal-title":"Data Sci. 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