Traffic Violation Detection Using Machine Learning: A Comprehensive Study

Abstract

Traffic violations such as riding without a helmet, triple riding, illegal parking, and lane violations are major contributors to road accidents and urban congestion. Traditional enforcement methods rely heavily on manual monitoring and static surveillance, which are inefficient, labor intensive, and prone to human error. The advancement of machine learning (ML) and computer vision has enabled automated, real-time detection of such violations, improving accuracy and scalability. This paper presents a comprehensive study on ML-driven traffic violation detection, utilizing deep learning-based object detection models and real-time video analytics to identify and classify violations. We explore the integration of geospatial data for precise location tagging and the use of decentralized storage for secure and tamper-proof evidence logging. Additionally, we discuss how AI-powered monitoring and automated reporting can enhance enforcement efficiency while encouraging responsible driving behavior. By leveraging AI-driven smart surveillance and automated enforcement, this study highlights how ML-based solutions can significantly improve traffic law compliance, reduce accidents, and assist law enforcement in creating safer roads.