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@article{200894,
        author = {Muhammad Umar A and Vishal T and Vignesh K and Satheesh S and Vignesh M and Syed Mohamed Ali M},
        title = {Smart traffic management system for efficient mobility and response},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2714-2717},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200894},
        abstract = {Smart traffic management is essential for improving urban mobility, reducing congestion, and ensuring road safety in rapidly growing cities. However, traditional traffic control systems rely on fixed signal timings and manual monitoring, which are inefficient in handling dynamic traffic conditions. These conventional systems often fail to respond to real-time traffic density, emergency vehicle movement, and unexpected incidents, leading to increased travel time, fuel consumption, and accidents. While basic automation techniques can manage predefined traffic patterns, they lack the ability to adapt to changing traffic environments and predict future congestion scenarios.
To address these challenges, an Intelligent Smart Traffic Management System is proposed using advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Deep Learning. The system aims to analyze real-time traffic data collected from sensors, cameras, and GPS devices to optimize traffic flow dynamically. By leveraging machine learning models, the system can detect congestion, predict traffic patterns, and adjust signal timings automatically. This intelligent approach enhances traffic efficiency, reduces delays, and supports smart city infrastructure.},
        keywords = {},
        month = {May},
        }