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However, forecasting OD travel flow presents greater challenges than estimating inflows or outflows of individual regions, as it requires modeling complex spatiotemporal dependencies between both origin and destination regions simultaneously. Furthermore, external factors such as weather conditions and calendar dates exert heterogeneous influences on OD travel flow, yet these are rarely adequately incorporated in existing approaches. To address these issues, this paper proposes a SpatioTemporal Relational Graph Learning (STRGL) model for OD travel flow prediction. Our framework first constructs four relational graphs to model multiple relationships among travel flows, including spatial distribution, origin and destination semantics, and temporal fluctuation patterns. A spatiotemporal relational graph convolutional network is then employed to jointly model spatial and temporal dependencies across these relational graphs. Additionally, an attention\u2010based environmental feature\u2010learning module is designed to quantify the heterogeneous effects of external factors. The learned representations are finally decoded by a multilayer perceptron to predict future OD travel flows. Extensive experiments on two real\u2010world travel datasets show that STRGL consistently outperforms multiple state\u2010of\u2010the\u2010art baseline methods across most evaluation metrics. Ablation studies further validate the contribution of each component in the proposed STRGL framework.<\/jats:p>","DOI":"10.1111\/tgis.70274","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T05:38:42Z","timestamp":1777700322000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Origin\u2013Destination Travel Flow Based on a Spatiotemporal Relational Graph Learning Approach"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9136-9764","authenticated-orcid":false,"given":"Yan","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Geo\u2010Informatics Central South University  Changsha China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geo\u2010Informatics Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0428-5982","authenticated-orcid":false,"given":"Da","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geo\u2010Informatics Central South University  Changsha China"},{"name":"College of Electronic Science and Technology National University of Defense Technology  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Geo\u2010Informatics Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Architecture and Planning Hunan University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.70092"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.10.021"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43762-025-00161-5"},{"key":"e_1_2_10_5_1","unstructured":"Bai S. 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