AI-Powered Real-Time Traffic Monitoring and Prediction System

Abstract

Traffic management in urban areas has become increasingly complex due to rapid city expansion, diverse traffic conditions, and limitations in existing infrastructure. This paper proposes an AI-powered real-time traffic monitoring system using IoT, deep learning, and time-series models for accurate detection and prediction. It incorporates anomaly detection along with an adaptive model that adjusts continuously to evolving  traffic conditions. Additionally, reinforcement learning is used to optimize traffic signal control, providing a flexible and high-performance approach for today’s urban transportation systems.