Traffic congestion to growing urban mobility is becoming a challenge and becomes inefficient, unwanted, damage to the environment and economic losses. The research in this paper proposes an intelligent traffic control system employing cloud computing, big data analytics and machine learning to achieve optimized traffic flow and improve urban mobility. The system takes a random forest algorithm to model its predictive modeling by collecting and analysing real time various traffic data like from sensors, GPS devices and social media. Dynamic traffic control strategies, including adaptive signal timings and route optimization, the system implements dynamic traffic control strategies, based on changing conditions in real time. With a cloud based infrastructure in use, scalability and proper data management becomes simpler. Realtime inputs and monitoring are made easy by a user-friendly interface and predictive analysis and testing with vehicle simulation. With this integrated approach, it promises to combine under a single roof congestion, decrease travel times, and improve all around safety while it is also a scalable and flexible solution to urban traffic management.


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    Titel :

    Dynamic Traffic Optimization through Cloud-Enabled Big Data Analytics and Machine Learning for Enhanced Urban Mobility


    Beteiligte:
    R, ParvathiR (Autor:in) / K, Bakyalakshmi (Autor:in)


    Erscheinungsdatum :

    28.05.2025


    Format / Umfang :

    629739 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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