This paper presents an innovative approach to address traffic congestion and safety challenges in smart cities by leveraging Artificial Intelligence (AI)-driven Vehicular Ad-Hoc Networks (VANETs) within IoT-enabled transportation systems. The integration of AI algorithms, such as machine learning and deep learning, enables seamless communication among connected vehicles and IoT infrastructure. Real-time data analysis facilitates effective traffic flow control, congestion detection, and accident prediction at the same instance security and privacy concerns are addressed through robust solutions. The current work showcases simulations and case studies, highlighting significant improvements in traffic efficiency, reduced travel time, and enhanced transportation safety. The study emphasizes the transformative potential of AI-driven VANETs in creating intelligent transportation systems for future smart cities, fostering more sustainable and liveable urban environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    AI-Driven VANETs for IoT-Enabled Transportation Systems


    Additional title:

    Communic.Comp.Inf.Science


    Contributors:

    Conference:

    International Conference on Artificial Intelligence and its Application ; 2023 ; Pune, India November 21, 2023 - November 23, 2023



    Publication date :

    2025-03-13


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    HDMA: Hybrid D2D Message Authentication Scheme for 5G-Enabled VANETs

    Wang, Peng / Chen, Chien-Ming / Kumari, Saru et al. | IEEE | 2021


    Cloud enabled and cluster based efficient data broadcasting in VANETs

    Farooq, Mohd Umar / Pasha, Mohammed / Khan, Khaleel-Ur-Rahman | IEEE | 2015


    An Anonymous Batch Authentication and Key Exchange Protocols for 6G Enabled VANETs

    Vijayakumar, Pandi / Azees, Maria / Kozlov, Sergei A. et al. | IEEE | 2022


    6G Enabled Advanced Transportation Systems

    Liu, Ruiqi / Hua, Meng / Guan, Ke et al. | IEEE | 2024