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<p>One of the major problems smart cities face is how to efficiently route traffic, especially when connected vehicles and sensors produce a very large amount of real, time data. This heavy traffic load results in delays, inefficient routing, and excessive processing of central units. Hence, this article presents FL-TrafficNet, a hybrid routing framework that enhances traffic management in the Internet of Vehicles (IoV) and Vehicular Ad Hoc Networks (VANETs) scenarios. FL-TrafficNet combines Transformer models, and Graph Neural Networks (GNNs) to capture not only the time-dependent traffic variation but also the layout of the road network. A dual-attention component enables the model to pinpoint the most significant features in both spatial and temporal domains. To avoid uploading all the raw data to the cloud, the solution employs Federated Learning (FL), whereby vehicles and roadside units (RSUs), train their models on the spot and only share the resultant updates. This ensures data confidentiality and significantly reduces the network traffic. A reinforcement component embedded in the model dynamically makes path decisions by analyzing real-time traffic updates and feedback. The model works continuously by learning from nearby changes in traffic, weather, or road status. Simulation results show that FL-TrafficNet reduces errors in prediction (Mean Absolute Error (MAE): 1.95, Root Mean Squared Error (RMSE): 2.87, Mean Absolute Percentage Error (MAPE): 3.12), improves data privacy (97.8% privacy score), and increases traffic delivery rate (TDR) to 38.4%, a clear improvement over existing recent methods. These results make it suitable for real-time, privacy-aware routing in urban traffic networks.</p>

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FL-trafficNet: a hybrid federated transformer-GNN framework with dual attention for smart routing in IoV and VANET environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月24日

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  • Pallati Narsimhulu,
  • Rashmi Sahay

摘要

One of the major problems smart cities face is how to efficiently route traffic, especially when connected vehicles and sensors produce a very large amount of real, time data. This heavy traffic load results in delays, inefficient routing, and excessive processing of central units. Hence, this article presents FL-TrafficNet, a hybrid routing framework that enhances traffic management in the Internet of Vehicles (IoV) and Vehicular Ad Hoc Networks (VANETs) scenarios. FL-TrafficNet combines Transformer models, and Graph Neural Networks (GNNs) to capture not only the time-dependent traffic variation but also the layout of the road network. A dual-attention component enables the model to pinpoint the most significant features in both spatial and temporal domains. To avoid uploading all the raw data to the cloud, the solution employs Federated Learning (FL), whereby vehicles and roadside units (RSUs), train their models on the spot and only share the resultant updates. This ensures data confidentiality and significantly reduces the network traffic. A reinforcement component embedded in the model dynamically makes path decisions by analyzing real-time traffic updates and feedback. The model works continuously by learning from nearby changes in traffic, weather, or road status. Simulation results show that FL-TrafficNet reduces errors in prediction (Mean Absolute Error (MAE): 1.95, Root Mean Squared Error (RMSE): 2.87, Mean Absolute Percentage Error (MAPE): 3.12), improves data privacy (97.8% privacy score), and increases traffic delivery rate (TDR) to 38.4%, a clear improvement over existing recent methods. These results make it suitable for real-time, privacy-aware routing in urban traffic networks.