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@article{173136,
        author = {Mrs.Swati.S.Patil and Apurva Aniket Deuskar and Maithili Sunil Sawant and Pavan Sanjay Jagtap and Sakshi Pradip Nikam and Rukmini Suresh Waghmare},
        title = {Intelligent IoT-Driven Smart Traffic Management System for Efficient Urban Mobility},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2645-2651},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173136},
        abstract = {Smart Traffic Management (STM) is a transformative approach to urban transportation that leverages advanced technologies to optimize traffic flow, reduce congestion, and enhance road safety. This system integrates real-time data from a network of sensors, cameras, and connected vehicles to dynamically manage traffic signals, monitor road conditions, and coordinate vehicle movement.
By utilizing Artificial Intelligence (AI) and Internet of Things (IoT) technologies, STM systems can predict and respond to traffic patterns, minimizing delays and improving overall efficiency. The implementation of smart traffic solutions has shown significant potential in reducing travel times, lowering emissions, and improving the quality of life in urban environments. The increasing urbanization and rising vehicle population have led to significant traffic congestion, impacting transportation efficiency, safety, and the environment. This project proposes a Smart Traffic Management System utilizing Internet of Things (IoT) technologies to enhance urban traffic control and optimize traffic flow.
The system integrates various IoT devices, including smart traffic signals, vehicle detection sensors, and surveillance cameras, to collect real- time data on traffic conditions. The proposed system aims to reduce traffic congestion, enhance road safety, and lower emissions, contributing to more sustainable urban environments. Pilot studies indicate a potential reduction in travel time by up to 30%, showcasing the effectiveness of IoT-based traffic management. Future work will focus on scalability, integration with existing transportation systems, and user feedback mechanisms to continuously refine the approach.},
        keywords = {},
        month = {February},
        }