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| Title | Gtn-Gcn: Real-Time Traffic Forecasting Using Graph Convolutional Network And Transformer |
|---|---|
| ID_Doc | 28556 |
| Authors | Jinia S.N.; Azad S.B.; Akter R.; Dipto T.R.; Khaliluzzaman M. |
| Year | 2025 |
| Published | Applied Computational Intelligence and Soft Computing, 2025, 1 |
| DOI | http://dx.doi.org/10.1155/acis/5572638 |
| Abstract | 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-gated recurrent unit (GRU)-transformer model. The proposed model integrates a dynamic topology module (DTM) with graph attention networks (GATs), GRUs, and transformer-based temporal modules to adaptively capture the evolving dynamics of traffic networks. The DTM dynamically updates graph structures based on real-time traffic conditions, while GATs focus on identifying critical spatial relationships. GRUs efficiently capture temporal dependencies, and the transformer model captures long-term sequential patterns, providing a comprehensive framework for real-time traffic forecasting. The proposed model was trained and evaluated using the METR-LA dataset, which comprises traffic data from 207 sensors at 5-minute 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-minute predictions, with an R2 value of 0.928. For longer prediction horizons (30, 45, and 60 min), 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-GRU-transformer framework enhanced the model’s 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-time 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-world 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. Copyright © 2025 Sadia Naj Jinia et al. Applied Computational Intelligence and Soft Computing published by John Wiley & Sons Ltd. |
| Author Keywords | dynamic graph convolutional network; gated attention network; gated recurrent unit; graph convolutional network; graph transformer network; long-short term memory; METR-LA dataset; traffic forecasting |
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