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@article{186695,
        author = {Mr. Patil Kunal N. and Ms.Gorde V.S and Miss. Thorat Sakshi A. and Mr. Abhale B.A. and Mr. Paithankar Vinay S. and Miss. Thombare Pratiksha R.},
        title = {Traffic Congestion System Using Big Data and IOT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2410-2417},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186695},
        abstract = {Urban traffic congestion represents a significant and escalating challenge in modern cities, leading to substantial economic losses, environmental degradation, and a diminished quality of life for residents. The inadequacy of traditional traffic management systems to cope with the dynamic and ever-increasing volume of vehicles necessitates a paradigm shift towards more intelligent and adaptive solutions. This research proposes a comprehensive framework for an intelligent traffic management system by harnessing the synergistic power of the Internet of Things (IoT) and Big Data Analytics. The proposed system architecture is designed to collect vast amounts of heterogeneous, real-time data from a distributed network of IoT devices. These devices include roadside sensors, surveillance cameras, GPS-equipped vehicles, and mobile applications, which continuously monitor critical traffic parameters such as vehicle density, flow rate, speed, and congestion points across the urban landscape. This granular, live data stream forms the foundation for our analytical model. The core of our methodology involves the application of advanced Big Data analytics and machine learning algorithms to process this influx of information. By analyzing historical and real-time traffic data, the system can identify recurring patterns, predict congestion hotspots before they form, and understand the complex dynamics of urban mobility. Predictive models are employed to forecast traffic conditions with a high degree of accuracy, enabling proactive rather than reactive management strategies. Based on these predictive insights, the system facilitates dynamic and automated traffic control. This includes the real-time optimization of traffic signal timings to adapt to fluctuating traffic flow and the intelligent rerouting of vehicles through less congested alternative paths. Commuters receive timely alerts and optimized route suggestions through integrated mobile platforms, enhancing their travel experience. The primary objective is to significantly reduce travel times, decrease fuel consumption and associated emissions, and improve overall road safety and transportation efficiency. This study confirms that a data-driven approach can transform urban traffic management from a static system into a responsive, learning, and self-optimizing network. Ultimately, this research contributes to the development of sustainable, efficient, and adaptive smart city infrastructures, improving mobility and quality of life for all urban inhabitant.},
        keywords = {Internet of Things (IoT), Big Data Analytics, Intelligent Traffic Management, Smart Cities Predictive Analytics, Machine Learning, Real-Time Data Processing, Urban Mobility, Traffic Congestion.},
        month = {November},
        }