Traffic control is one of the most challenging fields for smart city planning. Recent automatic traffic controllers are static, and in emergency conditions, they are shifted to manual mode for better management. The authors suggest an IoT and machine learning-based, dynamic in nature, traffic controller that can be an efficient solution for traffic flow management. The proposed algorithm determines the number of vehicles belonging to one of the three groups employed in this approach, light, medium, and heavy vehicles. The traffic light’s dynamic character can be enhanced by using the numbers derived from this method to calculate densities in real time. Making traffic lights dynamic can assist in alleviating the problem of traffic congestion and lead to smoother flow.


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    Titel :

    IoT-Based Vehicle Class Detection for Smart Traffic Control


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Roy, Satyabrata (Herausgeber:in) / Sinwar, Deepak (Herausgeber:in) / Dey, Nilanjan (Herausgeber:in) / Perumal, Thinagaran (Herausgeber:in) / R. S. Tavares, João Manuel (Herausgeber:in) / Bhura, Ayam (Autor:in) / Sahoo, Piyush (Autor:in) / Kumar, Anupam (Autor:in) / Choudhary, Usha (Autor:in)

    Kongress:

    International Conference on Innovations in Computational Intelligence and Computer Vision ; 2024 ; Jaipur, India April 10, 2024 - April 11, 2024



    Erscheinungsdatum :

    07.12.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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