This research addresses the critical issue of urban trafficcongestion exacerbated by the inadequacies of traditional traffic management systems. Inefficient adaptation to real-time traffic conditions by static traffic light timers leads to wasted time, and fuel, increased pollution, and compromised safety during peak hours and emergencies. To mitigate these challenges, this study proposes a novel Smart Traffic Management (STM) system that leverages cutting-edge technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The STM framework dynamically adjusts traffic flow in response to real-time data, optimizing efficiency and enhancing emergency response mechanisms. Through comprehensive simulations and case studies, this research demonstrates the potential of the STM system to alleviate urban congestion and improve overall traffic management in diverse urban settings.


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    Titel :

    Smart Traffic Management System for Efficient Mobility and Emergency Response


    Beteiligte:
    Sultana, Farhain (Autor:in) / Bhardwaj, Ritika (Autor:in) / Kumar, Rohan N (Autor:in) / Gaur, Ruchir (Autor:in) / Shankar, Sahana.P. (Autor:in) / Bharadwaj, Aryan (Autor:in)


    Erscheinungsdatum :

    18.04.2024


    Format / Umfang :

    487940 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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