Improvement of deep learning for traffic flow prediction in intelligent transportation

To improve urban mobility and back sophisticated transportation systems, accurate traffic flow prediction is crucial. The intrinsic spatiotemporal difficulties in traffic data are addressed by this study's novel traffic prediction framework, which blends attention processes with graph convolutional networks (GCNs). The proposed framework integrates GCNs to model spatial dependencies in road networks and employs temporal and spatial attention mechanisms to dynamically prioritize significant features. A unique feature of the method is the use of dynamic graph structures that adapt to changing traffic conditions, ensuring robust and real-time predictive capabilities. Additionally, the model leverages multi-scale spatial aggregation and advanced temporal encoding techniques to capture both local and global patterns effectively. Experimental evaluations on the datasets demonstrate the superiority of the proposed model over traditional methods such as ARIMA and hybrid deep learning models. The results show significant improvements in prediction accuracy, with reductions in MAE, RMSE, and MAPE metrics. Furthermore, scalability analysis highlights the model’s suitability for deployment in large-scale transportation networks. This framework represents a comprehensive and adaptive approach to real-time traffic flow prediction, offering a robust solution for complex urban traffic management challenges.