AI-based Dynamic Traffic Signal Control

The AI-Based dynamic traffic signal control system aimed at improving traffic efficiency at four-way intersections. Traditional traffic signals operate on fixed time intervals, which often results in unnecessary delays and traffic congestion, especially during peak hours or in emergency situations. To address this, the proposed system incorporates real-time object detection using YOLOv5, a highly accurate and efficient deep learning model. The model processes video feeds from each road connected to the intersection, identifying the presence and type of vehicles approaching the signal. Special emphasis is placed on the detection of emergency vehicles such as ambulances, fire trucks, and police cars. This system continuously monitors traffic conditions and adjusts signal timings dynamically based on vehicle density and emergency vehicle presence, rather than relying on static schedules.A key feature of the system is its ability to prioritize emergency vehicles, ensuring they receive a green signal upon detection to facilitate quicker and safer passage through intersections. This is crucial for minimizing response times during critical situations, potentially saving lives. The architecture of the system includes integration between a trained YOLOv5 model enabling smooth and coordinated decision-making. By combining computer vision with intelligent control mechanisms, the system not only enhances overall traffic management but also supports the broader goal of smart city infrastructure. Simulation results and practical implementation scenarios demonstrate significant improvements in traffic flow and emergency response efficiency, validating the system’s effectiveness and real-world applicability.