Intelligent Traffic Management using Machine Learning

A smart traffic control system that gives priority to emergency vehicles like ambulances and adjusts traffic signals dynamically according to vehicle density is the primary focus of the project. To accurately measure vehicle density in real time and change traffic signals accordingly, the system utilizes a YOLO (You Only Look Once) object detection model. By sending green lights to the most congested lanes, it relieves traffic congestion and facilitates better flow of traffic. In addition, an RF module is installed in ambulances to provide real-time detection of emergency vehicles. The RF technology prioritizes the ambulance automatically by altering the light green as it comes near within a given distance from the traffic signal. This enables the ambulance to move through traffic without any hindrances. In case of emergency, this automated clearance system ensures a faster response time for ambulances, potentially leading to saving lives. The adaptive divider control in the system is another critical element. Real-time traffic density information from the YOLO model can be utilized for adjusting road lane widths, allocating more space to lanes with higher traffic densities. This enhances use of roads and reduces congestion points. By adapting to evolving traffic conditions, this adaptive approach optimizes traffic flow and works toward offering an efficient and responsive urban traffic solution. Overall, this technology ensures a smarter, real-time reaction to traffic conditions, enhancing emergency response times in addition to traffic flow efficiency.