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@article{187081,
        author = {Mohammed jalaluddin  Asst prof and Syed ghulqm yaseen and Md ikhwan uddin and Mohd yawar and Sing malum arshad},
        title = {Development of an Intelligent Traffic Monitoring System for Urban Roads},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3669-3674},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187081},
        abstract = {Urbanisation has led to a significant increase in vehicle density, resulting in chronic traffic congestion, accidents, fuel wastage, and environmental pollution. Traditional traffic management systems, primarily dependent on static signalling and manual control, have proven insufficient to handle the dynamic nature of urban traffic flows. To address these challenges, the integration of intelligent technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Computer Vision into transportation infrastructure has become a necessity.
This project proposes the development of an Intelligent Traffic Monitoring System (ITMS) designed for urban roads, which utilises real-time video feeds, sensor data, and machine learning algorithms to monitor, analyse, and manage vehicular movement efficiently. The data collected is transmitted to a centralised server, where advanced analytics are used to generate traffic insights and optimise signal timings. Additionally, the system supports emergency vehicle prioritisation and accident detection using pattern recognition models. The results demonstrate that the intelligent system can significantly improve traffic flow efficiency, reduce waiting time at intersections, and minimise human intervention.
The research contributes to the field of Intelligent Transportation Systems (ITS) by presenting a scalable, low-cost, and adaptable framework suitable for deployment in developing urban centres. Future extensions include integration with cloud-based analytics, predictive maintenance of traffic infrastructure, and autonomous vehicle compatibility.},
        keywords = {Intelligent Transportation System, Computer Vision, Traffic Monitoring, IoT, Machine Learning, Smart Cities, Urban Mobility.},
        month = {November},
        }