Abstract

Metropolitan cities in India are experiencing increasing challenges in traffic management due to rapid urbanization, high vehicle density, and limited enforcement capacity. Conventional traffic monitoring techniques, including manual surveillance and static camera systems, are often inadequate for handling complex and dynamic traffic scenarios. Recent advancements in Artificial Intelligence (AI), computer vision, and deep learning have enabled the development of automated and intelligent traffic violation detection systems. This paper presents a comprehensive review of AI-driven methodologies for detecting traffic violations such as helmet non-compliance, signal violations, overspeeding, and unauthorized lane usage. It examines state-of-the-art object detection frameworks, including YOLOv5 and Faster R-CNN, as well as spatio-temporal modeling approaches for traffic flow prediction. These techniques support real-time video analytics and facilitate data-driven decision-making in urban traffic control systems. Additionally, the paper identifies key research gaps specific to Indian metropolitan environments and proposes the need for scalable and integrated AI-based architectures. The study highlights the potential of AI technologies to improve enforcement efficiency, enhance road safety, and enable adaptive and intelligent traffic management systems.

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