A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
Riaz Ali, Ahmad Ali, Hafiz Muhammad Yasir Naeem, Mujtaba Asad, Tamam Alsarhan, Md Belal Bin Heyat
Abstract
Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS) and smart city infrastructures. This survey paper provides a comprehensive analysis of recent advancements in deep learning-based approaches for traffic flow prediction, focusing on spatiotemporal correlations and attention mechanisms. We systematically review five seminal papers that propose innovative neural network architectures including DHSTNet, Att-DHSTNet, and ASTMGCNet for citywide traffic prediction. Our survey examines their methodologies, key contributions, experimental results, and comparative performance. We organize the discussion around three main themes: (1) modeling dynamic spatiotemporal dependencies, (2) attention mechanisms for traffic prediction, and (3) hybrid neural network architectures. The paper includes detailed comparison tables and conceptual figures synthesized from the reviewed works. Our analysis shows that attention-based hybrid models outperform traditional techniques, with ASTMGCNet having the lowest RMSE (4.06) and MAPE (12.56%) on benchmark datasets. We end by outlining current issues and potential research directions in this rapidly changing subject.