{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:59:49Z","timestamp":1772989189152,"version":"3.50.1"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":176,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    A traffic network exhibits inherent characteristics of networks while also possessing unique features that hold significant research value. In this study, the limitations of static graph structures and the challenges of accurately modeling spatiotemporal dependencies in traffic flow have been addressed through a hybrid GCN\u2010gated recurrent unit (GRU)\u2010transformer model. The proposed model integrates a dynamic topology module (DTM) with graph attention networks (GATs), GRUs, and transformer\u2010based temporal modules to adaptively capture the evolving dynamics of traffic networks. The DTM dynamically updates graph structures based on real\u2010time traffic conditions, while GATs focus on identifying critical spatial relationships. GRUs efficiently capture temporal dependencies, and the transformer model captures long\u2010term sequential patterns, providing a comprehensive framework for real\u2010time traffic forecasting. The proposed model was trained and evaluated using the METR\u2010LA dataset, which comprises traffic data from 207 sensors at 5\u2010minute intervals. The model demonstrated superior performance across various metrics, achieving RMSE, MAE, and MAPE values of 4.125%, 2.985%, and 5.432%, respectively, for 15\u2010minute predictions, with an\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    value of 0.928. For longer prediction horizons (30, 45, and 60\u2009min), the model consistently outperformed baseline methods, maintaining competitive RMSE and MAPE values. The experimental setup included normalization, graph construction using adjacency matrices, and preprocessing steps to ensure data quality and robustness. The integration of spatial and temporal features through the GCN\u2010GRU\u2010transformer framework enhanced the model\u2019s ability to generalize across varying traffic scenarios, including peak hours and disruptions. Compared to traditional methods, which often rely on static graphs and fail to adapt to real\u2010time changes, the hybrid model effectively addresses both spatial heterogeneity and temporal dependencies. The results indicate its robustness in handling complex traffic dynamics, adaptability to real\u2010world variations, and potential applications in intelligent transportation systems. Future work will focus on incorporating multimodal data sources and enhancing computational efficiency to achieve broader scalability and deployment in smart city infrastructures.\n                  <\/jats:p>","DOI":"10.1155\/acis\/5572638","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T08:05:07Z","timestamp":1750925107000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GTN\u2010GCN: Real\u2010Time Traffic Forecasting Using Graph Convolutional Network and Transformer"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6082-5029","authenticated-orcid":false,"given":"Sadia Naj","family":"Jinia","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2025-7089","authenticated-orcid":false,"given":"Sumaiya Binte","family":"Azad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9196-6676","authenticated-orcid":false,"given":"Rima","family":"Akter","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8585-8624","authenticated-orcid":false,"given":"Taivan Reza","family":"Dipto","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-1610","authenticated-orcid":false,"given":"Md.","family":"Khaliluzzaman","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi10070485"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13116796"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17248-y"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2307.01227"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18348-z"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.18520\/cs\/v110\/i3\/373-385"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/0144164042000195072"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2018.04.015"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/9981657"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"HoangT. L. PhamT. D. andTaV. C. Improving Graph Convolutional Networks with Transformer Layer in Social-Based Items Recommendation 2021 13th International Conference on Knowledge and Systems Engineering (KSE) 2021 Bangkok Thailand 1\u20136 https:\/\/doi.org\/10.1109\/KSE53942.2021.9648823.","DOI":"10.1109\/KSE53942.2021.9648823"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9091474"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.02.013"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102078"},{"key":"e_1_2_9_16_2","unstructured":"KipfT. N.andWellingM. Semi-supervised Classification with Graph Convolutional Networks Proc. 5th Int. Conf. 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