Density-Based Traffic Control System Using IoT and Machine Learning

Traffic congestion is a critical issue in cities due to the increasing population and automobiles. Traffic jams create delays, tension, increased fuel usage, and air pollution, with megacities particularly affected. Real-time road traffic density estimates are vital for better signal control and traffic management. The current traffic management system lacks emphasis on live scenarios, leading to inefficiencies. Our project, implemented using Python OpenCV and a pre-trained YOLO model, aims to prevent congestion. Image processing captures lane footage, and processed data dictates traffic LED commands for signal management and uses sensors and cameras to determine vehicle count and understand the actual crowd of vehicles on each road in real-time. Machine Learning, an intelligent computer system, helps us analyze all this data and make decisions to improve traffic flow based on the current vehicular count. We also provide features for detecting emergency vehicles and giving them more priority over other vehicles to pass through. Additionally, the paper enhances pedestrian safety by incorporating a virtual speed bump when the light is red. It also prioritizes emergency vehicles, improving the efficiency of public services, and introduces a solution called the Density-Based Traffic Control System (DBTCS), which combines the power of the Internet of Things (IoT) and Machine Learning (ML) to revolutionize how we manage traffic.