Adaptive Traffic Signal Timer for Traffic Control System Using Artificial Intelligence

The rapid growth of urban populations and vehicular traffic has led to significant congestion challenges in cities worldwide. Beyond causing delays and frustration for commuters, traffic congestion contributes to increased fuel consumption and environmental pollution. Traditional traffic management systems, which rely on fixed-time signal intervals, often fail to adapt to real-time traffic conditions, exacerbating inefficiencies. To address these issues, this paper proposes an adaptive traffic signal control system that leverages artificial intelligence (AI) and computer vision techniques. By utilizing real-time image processing from CCTV cameras installed at intersections, the system dynamically estimates traffic density and adjusts signal timings accordingly. This approach not only optimizes traffic flow but also reduces fuel consumption and emissions by minimizing vehicle idle times. The proposed system represents a scalable and cost-effective solution for modern urban traffic management.