AI-Based Traffic Violation Detection and Ambulance Path Clearance System

Abstract

Traffic violations, traffic congestion, and delayed emergency response are major challenges in modern urban transportation systems. Traditional traffic monitoring and control methods rely heavily on manual observation, resulting in slow response times and inefficient traffic management. This project presents an AI-Based Traffic Violation Detection and Ambulance Path Clearance System to enhance road safety and improve emergency vehicle movement.

The proposed system uses live video feeds from CCTV or IP cameras and applies computer vision techniques using deep learning models such as YOLOv8 to detect vehicles, traffic signals, and emergency vehicles. Traffic violations including signal jumping, helmetless riding, wrong-lane driving, triple riding, and overspeeding are automatically identified. For each detected violation, the system generates digital evidence in the form of photos along with timestamps and vehicle details, enabling reliable verification and enforcement.

The system also performs ambulance detection, where emergency vehicles are identified in real time and traffic signals are automatically controlled to clear the path, ensuring faster and uninterrupted movement. In addition, traffic signal timing is dynamically adjusted based on traffic flow density to reduce congestion.

A centralized dashboard at the traffic control office allows authorities to monitor violations, view evidence, track traffic conditions, and oversee ambulance movement in real time. This project demonstrates the effective use of artificial intelligence for intelligent traffic management, improved emergency response, and safer urban road environments.