Abstract: Urban traffic congestion poses significant challenges, including prolonged travel times, elevated fuel consumption, and increased environmental pollution. Frequent stop-and-go conditions lead to higher emissions and degraded air quality, adversely affecting public health and the environment. The Traditional traffic signal systems operates on fixed time cycles and are often unable to adapt to real-time traffic conditions. To address this issue, we propose a Smart Traffic Signal Control System that dynamically adjusts signal timings based on real-time traffic flow data. Utilizing technologies such as sensors, cameras, and machine learning algorithms, the system monitors vehicular density at intersections and optimizes signal phases accordingly. The goal is to minimize the waiting time, reduce congestion, and improve overall traffic efficiency. Simulation results demonstrate significant improvements in traffic flow and a reduction in average waiting times compared to conventional fixed-time control systems. This smart system represents a crucial step toward the development of intelligent transportation infrastructure for smarter, more sustainable cities.

Index Terms: Smart Traffic Control, Vehicle Detection, Smart Cities, Adaptive Signal Control, Dynamic Signal Timing

| DOI: 10.17148/IARJSET.2025.125394