What question did this study set out to answer?

The aim is to develop an effective routing framework for traffic management in IoV and VANET environments that addresses delays and inefficiency.

May 26, 2026Scientific Reports0 citationsOpen Access

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

PNPallati Narsimhulu RSRashmi Sahay

Key Points

The aim is to develop an effective routing framework for traffic management in IoV and VANET environments that addresses delays and inefficiency.
Developed FL-TrafficNet incorporating Transformer models and Graph Neural Networks.
Employed Federated Learning to ensure data confidentiality while processing traffic data locally on vehicles and roadside units.
Incorporated reinforcement learning to adapt path decisions based on real-time traffic updates.
FL-TrafficNet achieved a Mean Absolute Error of 1.95 and a Root Mean Squared Error of 2.87, indicating significant prediction accuracy.
Data privacy was enhanced with a privacy score of 97.8%, ensuring secure information handling.
Overall traffic delivery rate increased to 38.4%, outperforming existing methods.

Abstract

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.

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