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It is a challenging task due to the complicated spatial\u2010temporal dependency. The latest studies mainly focus on capturing temporal and spatial dependencies with spatially dense traffic data. However, when traffic data become spatially sparse, existing methods cannot capture sufficient spatial correlation information and thus fail to learn the temporal periodicity sufficiently. To address these issues, we propose a novel deep learning framework, Multi\u2010component Spatial\u2010Temporal Graph Attention Convolutional Networks (MSTGACN), for traffic prediction, and we successfully apply it to predicting traffic flow and speed with spatially sparse data. MSTGACN mainly consists of three independent components to model three types of periodic information. Each component in MSTGACN combines dilated causal convolution, graph convolution layer, and the weight\u2010shared graph attention layer. Experimental results on three real\u2010world traffic datasets, METR\u2010LA, PeMS\u2010BAY, and PeMSD7\u2010sparse, demonstrate the superior performance of our method in the case of spatially sparse data.<\/jats:p>","DOI":"10.1155\/2021\/9134942","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T23:20:38Z","timestamp":1640301638000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multicomponent Spatial\u2010Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9374-0192","authenticated-orcid":false,"given":"Shaohua","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1055-4325","authenticated-orcid":false,"given":"Shijun","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1032-2957","authenticated-orcid":false,"given":"Jingkai","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2537-9873","authenticated-orcid":false,"given":"Tianlu","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8950-4157","authenticated-orcid":false,"given":"Junsuo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0871-5839","authenticated-orcid":false,"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2011.2158001"},{"key":"e_1_2_13_2_2","doi-asserted-by":"crossref","unstructured":"LiuW. 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