Volume 2 - Issue 2, March - April 2026

Traffic congestion has become a major issue in modern urban areas due to rapid population growth and the increasing number of vehicles. Traditional traffic signal systems operate on fixed-time schedules and cannot adapt to real-time traffic conditions, resulting in unnecessary delays, increased fuel consumption, and higher pollution levels. To overcome these limitations, an intelligent traffic management system is required. This project presents Traffic Signal Control Using Density Based Adaptive Heuristic Algorithm to dynamically regulate traffic flow at intersections. The system uses computer vision techniques to detect, track, and count vehicles from live video streams captured by roadside cameras. Based on the calculated traffic density in each lane, an adaptive heuristic algorithm determines the optimal green signal duration to ensure fair and efficient traffic movement. The backend of the system is implemented using the Flask framework with Python, while the frontend interface is developed using HTML, CSS, Bootstrap, and JavaScript for real-time monitoring and visualization. Hardware components such as microcontrollers, traffic lights, and LCD displays are integrated to control signal operations. The proposed system reduces congestion, minimizes waiting time, improves road efficiency, and lowers fuel consumption and emissions. Overall, the system contributes to the development of smart transportation infrastructure and supports the vision of intelligent and sustainable urban mobility.

Bhavani K, Dr.N.Sumathi,"Traffic Signal Control Using Density Based Adaptive Heuristic Algorithm" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(524-526) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.