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@article{197965,
        author = {N.SINDHU and K. PRASANNA and POGIRI MANIDEEP NAIDU and P .AKHIL and Mr. ABDUL AZEEZ},
        title = {TRAFFIC CONTROL SYSTEM FOR SMART CITIES USING IOT AND MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7305-7310},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197965},
        abstract = {The rapid urbanization of modern metropolitan areas has led to a critical increase in traffic congestion, resulting in significant economic losses, environmental degradation, and delayed emergency response times. Conventional traffic management systems, which rely on fixed-time intervals, are increasingly inadequate for handling the dynamic and unpredictable nature of contemporary vehicle flow. This research proposes an intelligent, automated Traffic Control System for Smart Cities that integrates the Internet of Things (IoT) and Machine Learning (ML) to optimize traffic management.
The hardware architecture of the proposed system is centered around the Arduino Uno microcontroller, which acts as the primary processing hub. To achieve real-time traffic density detection, Infrared (IR) sensors are strategically deployed along road lanes to monitor vehicle presence. Unlike static timers, this system utilizes these sensor inputs to calculate real-time density, allowing for dynamic adjustment of signal durations. A Liquid Crystal Display (LCD) is integrated into the interface to provide live status updates to commuters, while a standard LED array serves as the visual signaling mechanism.
A distinctive feature of this system is the inclusion of an Emergency Override Module. By utilizing physical emergency buttons (simulating pre-emption signals from ambulances or fire engines), the system can immediately interrupt the standard cycle to grant a "green corridor" to priority vehicles. This hardware layer is augmented by a Machine Learning framework that analyzes historical traffic data to predict congestion patterns, enabling the system to preemptively adjust signaling cycles during peak hours.
The IoT integration ensures that all traffic data is logged to a centralized cloud platform, facilitating remote monitoring and long-term urban planning analytics. Experimental results demonstrate that the proposed smart system significantly reduces average waiting times at intersections compared to traditional methods. By combining low-cost hardware components with sophisticated ML algorithms, this project provides a scalable, cost-effective, and highly efficient solution for the evolving infrastructure needs of future smart cities, ultimately enhancing road safety and reducing carbon emissions.},
        keywords = {Arduino Uno, LCD, LED, IR Sensor, Emergency buttons, Machine learning.},
        month = {April},
        }