{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T17:07:11Z","timestamp":1793120831614,"version":"4.5.0"},"reference-count":234,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.<\/jats:p>","DOI":"10.3390\/ijgi12030100","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T03:02:32Z","timestamp":1678071752000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":201,"title":["Graph Neural Network for Traffic Forecasting: The Research Progress"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-5047","authenticated-orcid":false,"given":"Weiwei","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4151-6682","authenticated-orcid":false,"given":"Jiayun","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering & China-Singapore International Joint Research Institute, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"He","sequence":"additional","affiliation":[{"name":"Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixi","family":"Gu","sequence":"additional","affiliation":[{"name":"China Academy of Industrial Internet, Beijing 100102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zlatanova, S., Yan, J., Wang, Y., Diakit\u00e9, A., Isikdag, U., Sithole, G., and Barton, J. 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