An Intelligent Traffic Management System with Adaptive Traffic Light Control Based on Vehicle Density Estimation

The Intelligent Traffic Management System utilizes computer vision and advanced traffic control techniques to detect two- and four-wheelers precisely and estimate vehicle density at intersections. Urban areas becoming increasingly congested, so efficient traffic control is essential. Traditional traffic lights operate on fixed intervals, often causing delays and long waits during peak hours. This proposed system utilizes Faster R-CNN accurate vehicle detection, while a mixture of Gaussian model aids in vehicle density estimation. Additionally, fuzzy logic is implemented to adaptively control traffic light timings based on real-time data, prioritizing high-traffic flows to reduce congestion and travel times. This approach has shown promise when integrated with existing frameworks of managing traffic which can be expanded as needed; it has been found to have an accuracy level of 93.2%, a precision rate of 95.4%, and a recall rate of 91%, demonstrating its effectiveness and adaptability