SMART TRAFFIC POLICING: DEEP LEARNING-BASED AUTOMATED ECHALLAN SYSTEM

Abstract

Traffic violations remain one of the leading causes of road accidents, congestion, and enforcement challenges in urban areas. Traditional monitoring systems rely heavily on manual supervision or semi-automated methods, which are often time-consuming, error-prone, and limited in scalability. With the rapid progress of artificial intelligence, particularly in deep learning and computer vision, automated traffic law enforcement has emerged as a promising solution. This paper presents Smart Traffic Policing, a Deep Learning-Based Automated e-Challan System designed to detect traffic violations in real-time and generate electronic challans without human intervention. The system employs Convolutional Neural Networks (CNNs) for vehicle detection, OCR-based license plate recognition for offender identification, and a rule-based violation detection module that classifies actions such as signal jumping, overspeeding, and helmetless riding. Once a violation is detected, the system automatically cross-verifies vehicle details from a centralized database and issues a digitally authenticated e-challan to the violator through an integrated portal. Experimental results demonstrate that the proposed framework achieves high accuracy in diverse environmental conditions, including variations in lighting, weather, and traffic density. Compared to conventional surveillance approaches, the system significantly reduces manual workload, improves enforcement transparency, and ensures timely penalty issuance. By integrating deep learning, computer vision, and IoT-enabled databases, this study highlights the potential of AIdriven governance systems to enhance road safety, enforce compliance, and contribute toward building smarter, safer cities.