Next-Gen Smart Traffic Violation Detection Using Edge AI and IoT for Safer Urban Mobility

Abstract: Urban traffic is more stressed with the increase in traffic offenses like signal jumping, overspeeding, and helmet-less driving, which are compromising the safety of the roads and making transportation inefficient. Traditional methods of enforcement that rely on human monitoring and post-incident analysis are inadequate for real-time intervention. This paper introduces a future-proof smart traffic violation detection system driven by Edge AI and Internet of Things (IoT) technology to provide efficient, autonomous, and scalable traffic monitoring for contemporary urban environments. The envisioned system combines YOLOv5-based object detection, optical character recognition (OCR) for license plate extraction, and OpenCV for visual analysis. Edge devices like Raspberry Pi devices, along with IoT sensors, analyze and process video feeds at the point of origin, cutting latency and bandwidth consumption drastically. Besides detection, the system uses machine learning-based predictive analytics to predict hotspots of violations and peak hours, enabling authorities to implement preemptive safety measures. Real-time notification, automatic reporting, and integration with smart city infrastructure further increase responsiveness and public accountability. Field tests show high detection accuracy in various lighting and weather conditions, and the edge-IoT architecture provides cost savings and simplified deployment. This research helps in developing intelligent transport systems, providing secure, intelligent, and adaptable urban mobility solutions.

Edge AI, IoT, Smart City, Traffic Violation Detection, YOLOv5, License Plate Recognition, Computer Vision, Real-time Monitoring, Intelligent Transportation System, Predictive Analytics

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