Citation :

R. Pushpavalli, Helina Rajini Suresh, Bingi Manorama, D.Lakshmi, R.E. Franklin Jino, M.Krishnamurthy, "Deep Learning Empowered Traffic Prediction and Management in Vehicular Networks: Advancing Efficiency and Safety on the Road," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 392-410, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P126

Abstract

Effective traffic management and prediction have been considered the key node in the evolution of modern vehicular networks, whereby great enhancement in road safety, congestion reduction, and overall urban mobility would be afforded. With appropriate traffic predictions, interventions by authorities can be affected in time, with optimization of resources, hence translating to efficient transportation systems. Issues of low predictive accuracy, inability to handle traffic dynamics, and general lack of integration with diverse data sources are also faced by traditional models of traffic prediction. The proposed research work solves the problems described above by putting forward a novel traffic prediction framework, which merges the Adaptive Bilinear Transformer Network, or ABTNet, with the Symbiosis-inspired Multi-Objective Optimizer, or SiMOO. Its architecture is supported by state-of-the-art advanced deep learning techniques with a focus on complex temporal dependencies in traffic data. The SiMOO optimization further enhances the performance of the model with its unique way of symbiotic parameter tuning. The result of such integration will not only improve the accuracy of the predictions but also equip the models to adapt to the fluctuating traffic scenarios, raising higher standards for traffic forecasting. The framework proposed in this study is quite effective and results in a remarkable accuracy of 99% with a low RMSE of 1.65, hence reflecting a far superior performance by the model when compared to the existing machine learning and deep learning approaches. Therefore, the proposed methodology contributes significantly to the development of traffic management systems in vehicular networks, offering good accuracy in real-time traffic predictions that open ways to smarter, responsive urban environments.

Keywords

Vehicular Networks, Traffic Prediction, Traffic Management, Deep Learning, Classification, Road Safety, and Optimization.

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