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.


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    Titel :

    AI-Driven VANETs for IoT-Enabled Transportation Systems


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Gupta, Anish (Herausgeber:in) / Hinchey, Michael (Herausgeber:in) / Zalevsky, Zeev (Herausgeber:in) / Singh, Ramveer (Autor:in) / Kumar, Raj (Autor:in) / Singh, Anupam (Autor:in) / Vijay, Richa (Autor:in) / Khan, Rubina Liyakat (Autor:in) / Ather, Danish (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    13.03.2025


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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