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Enhancing Vehicular Networks with Deep Radial Basis Function for Intelligent Traffic Management


Affiliations
1 Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, India
2 Department of Electrical and Electronics Engineering, Varuvan Vadivelan Institute of Technology, India
3 Department of Computer Science and Engineering, DMI College of Engineering, India
4 Department of Samsung Research and Development, Samsung, Bengaluru, India
     

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The vehicular networks has spurred research into intelligent traffic management systems to alleviate congestion and enhance safety. However, existing approaches often face challenges in capturing the complex dynamics of urban traffic flow efficiently. In this study, we propose an innovative framework integrating Deep Radial Basis Function (DRBF) networks into vehicular networks for intelligent traffic management. Our approach aims to address the limitations of conventional methods by leveraging the representational power of deep learning while incorporating the flexibility of radial basis function networks. The problem addressed in this research lies in the inadequacy of traditional traffic management systems to adapt to the dynamic nature of urban traffic flow. Existing methods often rely on simplistic models or predefined rules, which may fail to capture the intricate patterns and interactions among vehicles on the road. Consequently, these systems may struggle to provide real-time and accurate traffic management solutions, leading to increased congestion and safety hazards. To bridge this research gap, we propose the integration of DRBF networks, which offer a unique combination of deep learning capabilities and radial basis function interpolation. This hybrid architecture enables the model to learn complex spatial and temporal dependencies from vehicular network data while maintaining computational efficiency and interpretability. By training the DRBF network on historical traffic data and real-time sensor inputs, our methodology can effectively predict traffic flow, identify congestion hotspots, and optimize route recommendations in urban environments. Experimental results on real-world traffic datasets demonstrate the effectiveness of the proposed approach in enhancing traffic management performance. Compared to traditional methods, our DRBF-based framework achieves higher accuracy in traffic flow prediction and generates more efficient routing strategies, leading to reduced travel times and improved overall traffic conditions.

Keywords

Vehicular Networks, Deep Learning, Traffic Management, Radial Basis Function, Intelligent Transportation Systems.
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  • Mingchao Yu, Parastoo Sadeghi and Alex Sprintson, “Feedback-Assisted Random Linear Network Coding in Wireless Broadcast”, Proceedings of IEEE Globecom Workshops, pp. 1-6, 2017.

  • Shehu Jabaka Muhammad, Sijing Zhang and Vladimir Dyo, “Network Coding for Reliable Safety Message Communication in Vehicular Ad-Hoc Networks: A Review”, Proceedings of IEEE International Conference on Future Generation Communication Technology, pp. 125-131, 2015.

  • Nandhini Vineeth and H.S. Guruprasad, “The Influence of Network Coding on The Performance of Wireless Networks: A Survey”, International Journal of Advanced Computer Technology, Vol. 3, No. 6, pp. 884-892, 2014.

  • P. Vijayakumar, M. Azees, A. Kannan and L.J. Deborah, “Dual Authentication and Key Management Techniques for Secure Data Transmission in Vehicular Ad Hoc Network”, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 4, pp. 1015-1028, 2016.

  • Y.S. Chia, Z.W. Siew, H.T. Yew, S.S. Yang and K.T.K. Teo, “An Evolutionary Algorithm for Channel Assignment Problem in Wireless Mobile Networks”, ICTACT Journal on Communication Technology, Vol. 3, No. 4, pp. 613-618, 2012.

  • M.B. Mansour, C. Salama, H.K. Mohamed and S.A. Hammad, “VANET Security and Privacy-An Overview”, International Journal of Network Security and Its Applications, Vol. 10, No. 2, pp. 13-34, 2018.

  • L. Liu, Y. Wang, J. Zhang and Q. Yang, “A Secure and Efficient Group Key Agreement Scheme for VANET”, Sensors, Vol. 19, No. 3, pp. 482-494, 2019.

  • A. Sumathi, “Dynamic Handoff Decision based on Current Traffic Level and Neighbor Information in Wireless Data Networks”, Proceedings of International Conference on Advanced Computing, pp. 1-5, 2012.

  • S. Kannan and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoTbased Bat Agents for Traffic Management”, Electronics, Vol. 10, No. 7, pp. 785-798, 2021.

  • P. Kumar, R. Merzouki, B. Conrard and V. Coelen, “Multilevel Modeling of the Traffic Dynamic”, IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 3, pp. 1066-1082, 2014.

  • D.F. Allan and A.M. Farid, “A Benchmark Analysis of Open Source Transportation-Electrification Simulation Tools”, Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp. 1202-1208, 2015.

  • O. Csillik and M. Kelly, “Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery using Convolutional Neural Networks”, Drones, Vol. 2, No. 4, pp. 1-39, 2018.

  • L. Liu, Y. Wang, J. Zhang and Q. Yang, “A Secure and Efficient Group Key Agreement Scheme for VANET”, Sensors, Vol. 19, No. 3, pp. 482-494, 2019.


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  • Enhancing Vehicular Networks with Deep Radial Basis Function for Intelligent Traffic Management

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Authors

S. Vijayarangam
Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, India
N. Sivakumar
Department of Electrical and Electronics Engineering, Varuvan Vadivelan Institute of Technology, India
W. Agitha
Department of Computer Science and Engineering, DMI College of Engineering, India
Mohamed Mallick
Department of Samsung Research and Development, Samsung, Bengaluru, India

Abstract


The vehicular networks has spurred research into intelligent traffic management systems to alleviate congestion and enhance safety. However, existing approaches often face challenges in capturing the complex dynamics of urban traffic flow efficiently. In this study, we propose an innovative framework integrating Deep Radial Basis Function (DRBF) networks into vehicular networks for intelligent traffic management. Our approach aims to address the limitations of conventional methods by leveraging the representational power of deep learning while incorporating the flexibility of radial basis function networks. The problem addressed in this research lies in the inadequacy of traditional traffic management systems to adapt to the dynamic nature of urban traffic flow. Existing methods often rely on simplistic models or predefined rules, which may fail to capture the intricate patterns and interactions among vehicles on the road. Consequently, these systems may struggle to provide real-time and accurate traffic management solutions, leading to increased congestion and safety hazards. To bridge this research gap, we propose the integration of DRBF networks, which offer a unique combination of deep learning capabilities and radial basis function interpolation. This hybrid architecture enables the model to learn complex spatial and temporal dependencies from vehicular network data while maintaining computational efficiency and interpretability. By training the DRBF network on historical traffic data and real-time sensor inputs, our methodology can effectively predict traffic flow, identify congestion hotspots, and optimize route recommendations in urban environments. Experimental results on real-world traffic datasets demonstrate the effectiveness of the proposed approach in enhancing traffic management performance. Compared to traditional methods, our DRBF-based framework achieves higher accuracy in traffic flow prediction and generates more efficient routing strategies, leading to reduced travel times and improved overall traffic conditions.

Keywords


Vehicular Networks, Deep Learning, Traffic Management, Radial Basis Function, Intelligent Transportation Systems.

References