With the increased use of private vehicles for commuting, traffic congestion has turn into a permanent dispute in the world. Traffic congestion results in delays, time waste, and financial losses. Currently, if a specific lane has a higher traffic density than the others, then the existing timer approach struggles to adjust for the increasing traffic density in variable lanes at variable times. While it is an ineffective on the road, dynamic traffic management system aids in the free flow of traffic and reduces traffic congestion. Hence, the proposed dynamic traffic management system relies on the “YOLO” machine learning algorithm. The proposed machine learning model detects the vehicles count in each lane and performs dynamic signal switching depending on traffic density that leads to alleviate the traffic congestion problems. The experimental outcomes illustrate that our proposed model works outperform than the other real-time object detectors.


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    Titel :

    Intelligent Traffic Management System Using YOLO Machine Learning Model


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Peter, J. Dinesh (Herausgeber:in) / Fernandes, Steven Lawrence (Herausgeber:in) / Alavi, Amir H. (Herausgeber:in) / Gomathi, B. (Autor:in) / Ashwin, G. (Autor:in)


    Erscheinungsdatum :

    2022-08-02


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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