REAL TIME TRAFFIC SIGNAL OPTIMIZATION USING ARTIFICIAL INTELLIGENCE

Traffic congestion is a major problem in urban areas due to the increasing number of vehicles and inefficient traffic signal management. This project proposes a Real Time Traffic Signal Optimization system using Artificial Intelligence (AI) to improve traffic flow at road intersections. The system uses video input from traffic cameras to monitor vehicle density in different lanes. Using Convolutional Neural Networks (CNN) and deep learning techniques, the system analyzes real-time traffic conditions and dynamically adjusts signal timing based on the number of vehicles present in each direction. This adaptive approach helps reduce waiting time, minimize traffic congestion, and improve overall road efficiency. The proposed system provides a smart and automated traffic management solution that can support modern intelligent transportation systems (ITS) and contribute to safer and more efficient urban traffic control. Rapid urbanization and the continuous increase in the number of vehicles have led to serious traffic congestion in many cities. Traditional traffic signal systems operate with fixed time intervals and often fail to adapt to real-time traffic conditions. This project presents a Real Time Traffic Signal Optimization system using Artificial Intelligence (AI) to manage traffic more efficiently. The proposed system uses video cameras to capture live traffic data from different lanes at an intersection. By applying Convolutional Neural Networks (CNN) and deep learning techniques, the system detects vehicle density and automatically adjusts signal timings according to traffic flow. This dynamic signal control helps reduce vehicle waiting time, improve traffic movement, and decrease congestion at busy intersections. The system aims to provide an intelligent and automated traffic management solution for modern smart cities.