Leveraging deep learning for advanced routing optimization in urban vehicular networks

Abstract The increasing advances in smart mobility and connected systems have intensified interest in transforming urban mobility, with Vehicular Ad-hoc Networks (VANETs) technologies emerging as a key enabler for safer and more efficient cities. However, VANETs face significant routing challenges due to high vehicle mobility, dynamic network topologies, and privacy constraints. This paper proposes a Deep Learning-based Routing Optimization (DLRO) framework that integrates an attention-based DL model with Federated Learning (FL) for efficient and privacy-preserving route prediction in urban vehicular networks. The framework adopts a client–server architecture where local training is performed on each vehicle, and only model parameters are shared with the server. We evaluate DLRO using the SUMO simulator with a dataset containing vehicle trajectories across urban scenarios. Compared to baseline non-cooperative routing method, DLRO achieves up to 47.1–75% reduction in distance traveled. The Average Displacement Error (ADE) and Final Displacement Error (FDE) consistently decrease across training epochs, confirming the model’s predictive accuracy. These findings confirm that the integration of DL with FL provides an effective approach for privacy-preserving route optimization in urban vehicular networks.