Due to the need to solve urban mobility problems caused by traffic congestion, traffic consumes much energy, brings environmental harm and economic losses. This research proposes an intelligently automated traffic control system enabled by cloud computing and big data analytics to further enhance urban mobility while maximizing traffic efficiency. The system obtains and processes real-time traffic data through GPS devices, sensors and social media through Random Forest algorithms applied to predictive modeling. The system provides dynamic traffic control strategies including adaptive signal timings and route optimization which are responsive to changes in real time. A cloud based infrastructure is used for handling your data and scaling efficiently. Real time inputs and monitoring and vehicle simulation is performed over a user friendly interface for predictive analysis and testing purposes. This integrated approach promises to lead to reduced congestion, decreased travel times, and enhanced traffic safety using an affordable, scalable, and flexible urban traffic management solution.


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    Title :

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


    Contributors:


    Publication date :

    2025-04-16


    Size :

    801443 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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