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To address these challenges, this research presents an innovative framework known as the Continual Learning-based Spatial\u2013Temporal Graph Convolutional Recurrent Neural Network (STGNN-CL) for persistent and accurate long-term traffic flow prediction. By utilizing techniques such as Elastic Weight Consolidation (EWC), Memory Aware Synapses (MAS), and Synaptic Intelligence (SI), the proposed model effectively addresses the issue of catastrophic forgetting while simultaneously enhancing its capacity to incrementally assimilate new traffic data streams. An advanced traffic pattern fusion strategy is introduced, utilizing the Kullback\u2013Leibler Divergence (KLD) metric to measure traffic divergence across different scenarios. This approach improves the efficiency of the Continual Learning (CL) process by enabling the model to adapt to new traffic patterns more effectively over time. Extensive experiments conducted on the PeMSD3, PeMSD4, PeMSD7, and PeMSD8 datasets reveal the superiority of the proposed models, STGCN-EWC, STGCN-MAS, and STGCN-SI models achieve significant reductions in error rates compared to baseline methodologies. These results highlight the potential of continual learning in developing efficient, scalable, and adaptive traffic flow prediction systems, paving the way for advancements in transportation management and autonomous driving technologies.<\/jats:p>","DOI":"10.1007\/s40747-025-02049-7","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T07:57:41Z","timestamp":1755676661000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A framework for continual learning in real-time traffic forecasting utilizing spatial\u2013temporal graph convolutional recurrent networks"],"prefix":"10.1007","volume":"11","author":[{"given":"Mariam Labib","family":"Francies","sequence":"first","affiliation":[]},{"given":"Abeer Twakol","family":"Khalil","sequence":"additional","affiliation":[]},{"given":"Hanan M.","family":"Amer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4151-9717","authenticated-orcid":false,"given":"Mohamed Maher","family":"Ata","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"2049_CR1","doi-asserted-by":"crossref","unstructured":"Zhu D, Zhai G, Di Y, Manhardt F, Berkemeyer H, Tran T, Busam B (2023) IPCC-TP: utilizing incremental Pearson correlation coefficient for joint multi-agent trajectory prediction. 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We further confirm that the order of authors listed in the manuscript has been approved by all of us. No potential competing interest was reported by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"420"}}