One of the main issues facing today’s urban environments is traffic congestion, which is exacerbated by exponential population growth and the ensuing rise in vehicle traffic. This problem not only prolongs commutes but also has substantial impacts on environmental sustainability, public health, and total urban responsibility. To solve this problem, our idea proposes developing and putting into place an efficient traffic management system, the integration of PTV Vissim for dataset generation and the utilization of machine learning algorithms, combined with Dijkstra’s algorithm for route optimization, constitute comprehensive solution within our traffic congestion management system. By leveraging these advanced technologies, our system aims not only to alleviate current traffic congestion challenges but also to contribute to the creation of more sustainable and efficient urban transportation systems.


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    Titel :

    Traffic Congestion Management


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Tuba, Milan (Herausgeber:in) / Akashe, Shyam (Herausgeber:in) / Joshi, Amit (Herausgeber:in) / Mohanraj, K. Y. (Autor:in) / Santhameena, S. (Autor:in) / Deshi, Mahendra (Autor:in) / Naik, Prashant (Autor:in) / Kalburgi, Prabhanjan (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    03.05.2025


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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