Smart Traffic Violation Detection Using Hybrid Techniques
Shreya R. Rodge1, Student, Sipna COET, Amravati (Mh.)
Dr. Ketaki R. Ingole2, Associate Prof., Sipna COET, Amravati (Mh.)
Abstract - The rapid growth of vehicular traffic and increasing violation rates have created a critical need for automated and intelligent traffic monitoring systems. This work presents a Hybrid Traffic Safety System designed to enhance road safety through real-time detection and analysis of traffic violations using artificial intelligence and modern web technologies. The primary aim of the system is to automatically identify common traffic violations such as riding without a helmet, triple riding on two-wheelers, and vehicle identification through Automatic Number Plate Recognition (ANPR).
The proposed system employs deep learning and computer vision models developed using TensorFlow, PyTorch, and OpenCV to analyze live and recorded traffic surveillance footage. These AI models operate as independent Python-based microservices and communicate with a MERN stack (MongoDB, Express.js, React.js, Node.js) backend through secure REST APIs and WebSocket connections. Detected violations, along with time-stamped image evidence and metadata, are securely stored in MongoDB and cloud-based storage.
A real-time React-based dashboard provides visualization, monitoring, and analytical insights for traffic authorities. Experimental evaluation demonstrates high detection accuracy, low response time, and reliable system performance across varying traffic and lighting conditions. The hybrid architecture improves scalability, modularity, and maintainability while significantly reducing manual monitoring efforts. The proposed system offers a practical and extensible solution for intelligent transportation systems and serves as a strong foundation for smart city traffic management and automated enforcement applications.
Key Words: Traffic Safety, Intelligent Transportation System, Artificial Intelligence, Computer Vision, MERN Stack, ANPR, Helmet Detection, Triple Ride Detection.
