Development of AI-Based Real-Time Traffic Monitoring System for Smart Cities

Abstract

Urban centers across the globe are grappling with the growing challenge of traffic congestion, vehicular emissions, and inefficient road usage. As cities expand and vehicle numbers continue to rise, conventional traffic monitoring systems are proving increasingly inadequate. They are often reactive, manually managed, and lack the dynamic adaptability to respond to real-time traffic fluctuations. In the context of the smart city framework, there is a pressing need for intelligent, autonomous systems that can actively monitor, analyze, and optimize traffic flows using real-time data. This paper presents a comprehensive approach to the development of an AI-based real-time traffic monitoring system tailored specifically for the requirements of modern smart cities. Using a combination of machine learning algorithms, deep learning object detection frameworks, edge computing, and IoT integration, this system can detect vehicle counts, classify vehicle types, identify congestion hotspots, and detect road anomalies such as stalled vehicles or accidents. The system architecture is designed for scalability, with an emphasis on low-latency processing and user-friendly dashboard visualization. Experimental deployments demonstrate a significant improvement in traffic management efficiency, situational awareness, and response coordination. The proposed system is not only technically robust but also aligned with sustainable urban mobility goals.