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@article{197962,
        author = {NUTHALAPATI SAVIPYA and KOTERU SURENDRA REDDY and KOLA DASARADHA NAIDU and PULIBANDLA KALLE POTHU RAJU and Mrs.V.SWARNALATHA},
        title = {SMART TRAFFIC MANAGEMENT SYSTEM USING IOT AND CAMERA FEED},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7076-7080},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197962},
        abstract = {The rapid escalation of urban population density has rendered traditional time-based traffic signaling systems increasingly inefficient, leading to chronic congestion, increased carbon emissions, and delayed emergency response times. This paper presents the design and implementation of a Smart Traffic Management System that leverages the Internet of Things (IoT) and real-time computer vision to dynamically regulate traffic flow. Unlike conventional systems that operate on fixed intervals, the proposed solution utilizes a laptop camera as a primary sensor to capture live video feeds of traffic lanes.
The core architecture integrates high-level image processing with low-level hardware control. The camera feed is processed using a Python-based environment to perform vehicle detection and density estimation. By analyzing the number of vehicles present in each lane, the system calculates an optimal green-light duration. This processed data is transmitted via serial communication to an Arduino Uno microcontroller, which acts as the central processing unit for hardware execution. The Arduino interface manages the switching logic for a localized LED-based traffic light module.
The hardware setup is powered by a regulated power supply to ensure stable operation of the microcontroller and signaling components. By prioritizing lanes with higher vehicle density, the system effectively reduces the "idle time" often found in empty lanes during traditional cycles. Furthermore, the IoT integration allows for the logging of traffic data to a cloud platform, enabling urban planners to analyze long-term congestion patterns. Experimental results demonstrate a significant reduction in average waiting times compared to static signaling methods. This research provides a scalable, cost-effective framework for smart city infrastructure, emphasizing the seamless convergence of embedded systems and machine learning for sustainable urban mobility.},
        keywords = {Arduino Uno, Computer Vision, Smart Traffic Control, Vehicle Density Estimation, Embedded Systems, Urban Automation.},
        month = {April},
        }