In today’s rapidly changing world, the number of vehicles is increasing along with the population. A large flow of automobiles causes traffic congestion especially due to poor traffic management. Traffic jams not only lead to significant time wastage for commuters but are also economically expensive in terms of fuel consumption and environmental hazards. Many recent studies have shown how traffic jams increase stress levels and thus can have negative psychological impacts. Therefore, our proposed system, which is a real-time vehicle counting model is integrated with traffic signal control systems to optimize traffic flow which helps to potentially achieve more efficient traffic management, reduce congestion, and enhance overall transportation operations.


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    Titel :

    Smart Traffic Analyser Using Deep Learning


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Fong, Simon (Herausgeber:in) / Dey, Nilanjan (Herausgeber:in) / Joshi, Amit (Herausgeber:in) / Khare, Vijay (Autor:in) / Gupta, Vidhi (Autor:in) / Agarwal, Lakshya (Autor:in) / Kumar, Ramit (Autor:in)

    Kongress:

    International Conference on ICT for Sustainable Development ; 2024 ; Goa, India August 08, 2024 - August 09, 2024



    Erscheinungsdatum :

    12.03.2025


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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