{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T04:52:01Z","timestamp":1776142321364,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Funding of Sichuan Provincial Key Laboratory of Philosophy and Social Sciences for Mountain Tourism Safety","award":["24SDLYAQZZ001"],"award-info":[{"award-number":["24SDLYAQZZ001"]}]},{"name":"Open Funding of Observation and Research Station of Land Ecology and Land Use in Chengdu Plain, Ministry of Natural Resources, China","award":["CDORS-2023-04"],"award-info":[{"award-number":["CDORS-2023-04"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Project","doi-asserted-by":"crossref","award":["42371418"],"award-info":[{"award-number":["42371418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the rapid development of Intelligent Transportation Systems (ITSs), traffic prediction, a crucial component of ITSs, has garnered growing scholarly attention. The appli-cation of deep learning into traffic prediction has emerged as a prominent research direction, especially amid the rapid advancement of pretrained large language models (LLMs), which offer substantial benefits in time-series analysis through cross-modal knowledge transfer. In response to this advancement, this study introduces an innovative model for traffic flow prediction, designated as WGLLM. To capture spatiotemporal characteristics inherent in traffic flow data, this model incorporates a sequence embedding layer constructed on the stationary wavelet transform (SWT) and long short-term memory (LSTM), in conjunction with a spatial embedding layer founded on graph convolutional networks (GCNs). Additionally, a fully connected layer is utilized to integrate embeddings into the LLMs for comprehensive global dependency analysis. To verify the effectiveness of the proposed approach, experiments were carried out on two real traffic flow datasets. The experimental results demonstrate that WGLLM achieves superior predictive performance compared to multiple mainstream baseline models, accompanied by a significant enhancement in prediction accuracy.<\/jats:p>","DOI":"10.3390\/ijgi15040166","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:29:29Z","timestamp":1776065369000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Large Language Model for Traffic Flow Prediction Based on Stationary Wavelet Transform and Graph Convolutional Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8856-8746","authenticated-orcid":false,"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Observation and Research Station of Land Ecology and Land Use in Chengdu Plain, Ministry of Natural Resources, Chengdu 610072, China"},{"name":"Sichuan Provincial Key Laboratory of Philosophy and Social Sciences for Mountain Tourism Safety, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Observation and Research Station of Land Ecology and Land Use in Chengdu Plain, Ministry of Natural Resources, Chengdu 610072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangbing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Sichuan Provincial Key Laboratory of Philosophy and Social Sciences for Mountain Tourism Safety, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TITS.2020.3008612","article-title":"Deep reinforcement learning for intelligent transportation systems: A survey","volume":"23","author":"Ammar","year":"2022","journal-title":"IEEE Trans. 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