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<p>The increasing complexity of traffic patterns in modern urban environments necessitates intelligent and scalable solutions for real-time traffic forecasting within the Vehicular Ad Hoc Networks (VANETs). The proposed hybrid lightweight Deep Neural Network (DNN)–Convolutional Neural Network (CNN) framework integrates with Geographic Routing Protocols (GRP) to enable efficient traffic prediction in resource-constrained V2X environments. The model combines the spatial feature extraction ability of CNNs with the temporal learning strength of DNNs to capture the dynamic vehicular patterns. Evaluated using the TiHAN-V2X dataset, the model achieves an MSE of 0.0141, an MAE of 0.0951, and an R<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^{2}\)</EquationSource> </InlineEquation> of 0.9680 that surpassing state-of-the-art baselines. The lightweight design ensures low-latency inference suitable for edge devices such as RSUs and OBUs. The contributions include a scalable hybrid architecture for spatio-temporal traffic learning, integration of geographic routing for congestion-aware communication, and validation using real-world V2X data. The results demonstrate that the proposed framework enhances predictive accuracy, reduces latency, and supports intelligent traffic management in next-generation vehicular networks.</p>

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A secure and scalable traffic management framework using hybrid DNN-CNN and geographic routing in V2X networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Sonika Bhardwaj,
  • Ramesh Saha

摘要

The increasing complexity of traffic patterns in modern urban environments necessitates intelligent and scalable solutions for real-time traffic forecasting within the Vehicular Ad Hoc Networks (VANETs). The proposed hybrid lightweight Deep Neural Network (DNN)–Convolutional Neural Network (CNN) framework integrates with Geographic Routing Protocols (GRP) to enable efficient traffic prediction in resource-constrained V2X environments. The model combines the spatial feature extraction ability of CNNs with the temporal learning strength of DNNs to capture the dynamic vehicular patterns. Evaluated using the TiHAN-V2X dataset, the model achieves an MSE of 0.0141, an MAE of 0.0951, and an R \(^{2}\) of 0.9680 that surpassing state-of-the-art baselines. The lightweight design ensures low-latency inference suitable for edge devices such as RSUs and OBUs. The contributions include a scalable hybrid architecture for spatio-temporal traffic learning, integration of geographic routing for congestion-aware communication, and validation using real-world V2X data. The results demonstrate that the proposed framework enhances predictive accuracy, reduces latency, and supports intelligent traffic management in next-generation vehicular networks.