Smart Traffic Signal Model for Reducing Vehicular Pollution

Vehicular emissions are a major contributor to urban air pollution, traffic congestion, and associated health hazards in rapidly growing cities. Conventional traffic signal systems operate on fixed timing schedules that do not adapt to real-time traffic density, leading to unnecessary idling, increased fuel consumption, and elevated carbon emissions. This paper proposes a Smart Traffic Signal Model designed to reduce vehicular pollution through adaptive signal control based on real-time traffic conditions. The proposed system integrates Internet of Things (IoT) sensors, cameras, and artificial intelligence (AI) algorithms to monitor traffic flow, vehicle density, and waiting times at intersections. Using machine learning techniques, the system dynamically adjusts signal timings to optimize traffic movement, minimize idle time, and reduce stop-and-go patterns. Additionally, the model prioritizes emergency vehicles and promotes smoother traffic dispersion during peak hours