Paper Contents
Abstract
The integration of Artificial Intelligence (AI) in traffic surveillance is transforming how cities monitor and manage road networks. Traditional traffic monitoring systems often fall short in providing timely, accurate data for effective decision-making. AI technologies, particularly machine learning, computer vision, and deep learning, enable real-time analysis of traffic data, enhancing the accuracy and automation of surveillance. This study investigates AI-driven traffic surveillance systems that use cameras, sensors, and IoT devices to capture traffic data. AI algorithms, such as object detection and tracking, are applied to identify vehicles, pedestrians, and road conditions. Additionally, AI models can detect unusual events, including accidents or violations, and predict traffic congestion, aiding in dynamic traffic management. AI-based systems offer significant improvements in traffic flow, accident prevention, and overall road safety by automating monitoring and response processes. However, challenges such as data security, integration with current infrastructure, and system dependability remain. This paper examines the potential of AI to enhance urban transportation systems, providing more efficient, intelligent, and safe solutions for traffic management.
Copyright
Copyright © 2025 Amol Dhenge, Shreyash Kamble, Tanvir Sheikh, Kunal Gautam, Ashish Virmuttu, Karan Gedam. This is an open access article distributed under the Creative Commons Attribution License.
