Real-Time Traffic Density Monitoring and Adaptive Signal Control Using YOLOv8 and Arduino-Based LED System

This research paper introduces a real-time adaptive traffic management system designed to improve urban transportation efficiency and emergency vehicle prioritization. The system integrates advanced machine learning and IoT technologies, using the YOLOv8 object detection model and an Arduino-based microcontroller to monitor vehicle density, detect emergency vehicles, and dynamically manage traffic signals. The proposed solution incorporates multiple features, including software-controlled signals and real-time ambulance detection, to address the limitations of conventional fixed-timer traffic systems. Sensors and cameras capture live traffic data, which is processed to categorize density levels—Easy, Medium, and Heavy—for each direction (North, South, East, West). The integration of real-time data with dynamic signalling reduces traffic congestion while ensuring uninterrupted passage for emergency vehicles. Key innovations include an ambulance detection feature, which overrides normal traffic logic to immediately provide green-light passage, and centralized software management for greater flexibility and scalability. The YOLOv8 model, trained on a dataset of 10,000 annotated images, achieves a detection accuracy of 97.8%, ensuring precise and reliable operation. Experimental results demonstrate a 30% reduction in congestion wait times and a 40% improvement in ambulance response times. By leveraging real-time data and automated decision-making, this intelligent traffic management system addresses critical urban traffic challenges, offering a scalable and cost-effective solution. The system enhances operational efficiency and emergency response, marking a significant advancement in modern transportation networks.