Smart Traffic Signal Control System

Rapid urban growth has intensified traffic congestion in most cities, making traditional fixed-time traffic signals increasingly ineffective. These conventional systems fail to respond to real-time traffic variations, resulting in long waiting periods, fuel wastage, and environmental pollution. To overcome these limitations, this project introduces a Smart Traffic Signal Control System that automatically adjusts signal timings based on actual vehicle flow. The proposed model uses sensors or camera-based detection to monitor traffic density at each lane. The collected data is processed through a microcontroller or intelligent algorithm, which optimizes the duration of green and red lights dynamically. The system also incorporates a priority mechanism for emergency vehicles such as ambulances and fire engines, ensuring uninterrupted passage during critical situations. By adapting to changing traffic conditions, the system significantly improves traffic movement, reduces congestion, and lowers carbon emissions. This thesis presents the design, methodology, and performance evaluation of the smart traffic system. The findings demonstrate that adaptive signal control offers a more efficient and sustainable solution compared to traditional traffic management methods, making it suitable for modern smart city infrastructure.