An Intelligent Edge-Integrated Vision and Identification Framework for Automated Traffic Violation Detection and Digital Penalty Issuance

Abstract

Road traffic accidents remain a major global concern, particularly in developing countries where traffic management systems are often inadequate and enforcement is inconsistent. A significant number of accidents occur due to signal violations, especially during night hours when monitoring is minimal. Conventional traffic enforcement methods rely on CCTV (Closed-Circuit Television) cameras, which require continuous human supervision and often fail to capture clear vehicle identification due to issues such as poor visibility or obstructed number plates. These limitations highlight the need for an automated, reliable, and real-time traffic violation detection system. To address this problem, the proposed system introduces an RFID (Radio Frequency Identification)-based smart traffic monitoring solution integrated with a microcontroller and wireless communication modules. In this system, RFID tags embedded in vehicles store unique identification data, while RFID readers installed at traffic signals detect vehicles that cross during red signals. The detected information is processed using a NodeMCU (ESP8266-based microcontroller with Wi-Fi capability), which automatically generates an electronic challan (e-challan) for the violating vehicle. The system further utilizes a GSM (Global System for Mobile Communications) module to send violation details directly to the vehicle owner and the RTO (Regional Transport Office). This automated approach eliminates the need for manual monitoring, ensures accurate identification, and enables efficient enforcement of traffic rules. Additionally, it supports real-time tracking and integration with online payment systems. The proposed system enhances road safety, reduces human effort, and provides a scalable solution for smart traffic management.