Traffic congestion and transportation challenges are pressing issues in our modern society. They cause delays, fuel wastage, and environmental pollution. In this paper, we aim to develop an application that will continuously track the user's location and detect vehicles in real time using the YOLOv5 object detection model. Valuable insights into traffic patterns and congestion levels are gained. This helps in effective traffic management and infrastructure planning. Continuous traffic monitoring was made possible using live video feeds, which allowed us to identify important intersections and set vehicle count criteria. Personalized email alerts near crowded locations were particularly helpful to users giving them other routes and making getting around easier. The proposed system detected vehicles in real time with a precision of 94.8%. Furthermore, implementing an alert-based system on predefined vehicle counts enabled users to make informed decisions, resulting in less traffic congestion.


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    Titel :

    Smart Traffic: Real-Time Tracking, Vehicle Detection, and Congestion Alert System


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Hassanien, Aboul Ella (Herausgeber:in) / Anand, Sameer (Herausgeber:in) / Jaiswal, Ajay (Herausgeber:in) / Kumar, Prabhat (Herausgeber:in) / Siddiqua, Raqshanda (Autor:in) / Sujatha, B. (Autor:in)

    Kongress:

    International Conference On Innovative Computing And Communication ; 2024 ; New Delhi, India February 16, 2024 - February 17, 2024



    Erscheinungsdatum :

    24.07.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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