{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:35:06Z","timestamp":1780054506331,"version":"3.54.0"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"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":["62076251"],"award-info":[{"award-number":["62076251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder\u2013decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics.<\/jats:p>","DOI":"10.3390\/ijgi12080346","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T09:13:44Z","timestamp":1692350024000},"page":"346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network for Traffic Prediction"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1733-7956","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"Command and Control Engineering College, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Zhan","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronic Engineering, Nanjing 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Command and Control Engineering College, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8746-1106","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy of Military Science, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhisong","family":"Pan","sequence":"additional","affiliation":[{"name":"Command and Control Engineering College, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TITS.2010.2060218","article-title":"Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications","volume":"11","author":"Wang","year":"2010","journal-title":"IEEE Trans. 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