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Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial\u2013temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.<\/jats:p>","DOI":"10.1007\/s40747-023-01324-9","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:02:35Z","timestamp":1706551355000},"page":"3305-3317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9681-8119","authenticated-orcid":false,"given":"Jiahao","family":"Ling","sequence":"first","affiliation":[]},{"given":"Yuanchun","family":"Lan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-4484","authenticated-orcid":false,"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaofei","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"1324_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2020.01.002","volume":"59","author":"P Xie","year":"2020","unstructured":"Xie P, Li T, Liu J, Du S, Yang X, Zhang J (2020) Urban flow prediction from spatiotemporal data using machine learning: a survey. 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