DESIGN AND DEPLOYMENT OF INTELLIGENT TRAFFIC SYSTEM USING EDGE AI

Abstract

The increasing complexity of urban transportation networks and limitations of traditional fixed-cycle traffic control systems lead to excessive congestion, fuel wastage, and delays during emergencies. To overcome these challenges, this project titled “Design and Deployment of Intelligent Traffic System Using Edge AI” introduces a decentralized and fully automated adaptive traffic management architecture. The system leverages Edge Artificial Intelligence for rapid on-device video analytic using Conventional Neural Networks (CNN), enabling real-time detection of vehicles, lane-wise traffic density estimation, and intelligent dynamic adjustment of traffic signal timings based on actual road conditions instead of Predefined cycles. By deploying lightweight deep learning models such as Tiny- YOLO/SSD-MobileNet. directly on edge hardware (e.g., NVIDIA Jetson Nano, ESP32-CAM), data processing remains local, thereby reducing communication latency, enhancing response time, and significantly lowering cloud dependency. The system incorporates advanced techniques including frame , motion analysis, and object classification to ensure high-accuracy detection even under varying illumination and weather conditions. Additionally, GPS integration and IoT communication through Protocols facilitate continuous monitoring, system health feedback, and data logging for further optimization and analytics.

A major focus of the system is the support for public safety and emergency vehicle prioritization. The design includes siren-based acoustic pattern detection and image-based ambulance recognition to automatically generate a “Green Corridor,” ensuring the uninterrupted movement of emergency services through congested intersections. Moreover, the platform implements accident detection using anomaly recognition algorithms and sensor fusion, where abnormal stoppage patterns or collision signatures trigger immediate safety measures.