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Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Predicting traffic speed is a crucial task in intelligent transportation systems, as it helps analyze traffic congestion and improve road flow. The complex spatio-temporal interactions present in traffic data make accurate predictions challenging. In recent years, many studies have focused on extracting and learning spatio-temporal features. Deep learning methods, particularly spatio-temporal graph learning models, show promising performance and become the mainstream approach in this area of research. However, existing methods cannot exploit and unify the heterogeneous spatio-temporal interactions hidden in traffic data to achieve multi-correlation modeling. As a result, they cannot effectively model the complex evolving patterns in traffic dynamics. To this end, we propose a heterogeneous spatio-temporal traffic graph learning framework (HSTGL) to capture these diverse spatio-temporal interactions comprehensively. In terms of design, HSTGL consists of three modules: spatio-temporal heterogeneous graph construction, spatio-temporal heterogeneous graph attention learning, and heterogeneous information supplementation. The first two modules utilize similar temporal pattern clustering and heterogeneous spatio-temporal graph attention mechanisms (HSTGAT) to learn heterogeneous spatio-temporal interactions in traffic data. The latter feature fusion module (FFM) is developed to complement potential heterogeneous information in the global spatio-temporal context. Our HSTGL conducts extensive experiments on three real-world public traffic datasets: METR-LA, PEMS-BAY, and PEMSD7M. The results demonstrate that HSTGL achieves superior predictive performance compared to representative benchmark methods with average improvements of 3.3% in MAE, 1.8% in RMSE, and 5.2% in MAPE compared to the optimal baseline.<\/jats:p>","DOI":"10.1145\/3783987","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T03:01:04Z","timestamp":1765508464000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Heterogeneous Spatio-Temporal Interactions for Traffic Speed Prediction"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6106-0214","authenticated-orcid":false,"given":"Xigang","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9570-1456","authenticated-orcid":false,"given":"Jiahui","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3906-527X","authenticated-orcid":false,"given":"Haojia","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1632-4347","authenticated-orcid":false,"given":"Wenchao","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1897-3473","authenticated-orcid":false,"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"Brody Shaked","year":"2022","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2022. 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