DEVELOPMENT OF AN ARTIFICIAL INTELLIGENCE-BASED ANALYTICAL SYSTEM FOR MANAGING URBAN TRAFFIC FLOW

Rapid urbanization and the increasing number of vehicles have created significant challenges for urban traffic management systems worldwide. Traffic congestion, delays, and inefficient road utilization negatively affect economic productivity, environmental sustainability, and public safety. Artificial intelligence technologies offer new opportunities for improving the efficiency of transportation systems through intelligent data analysis and automated decision-making. This study presents the development of an artificial intelligence–based analytical system for managing urban traffic flow using machine learning algorithms, predictive analytics, and real-time traffic monitoring techniques. The proposed system processes data collected from traffic cameras, road sensors, and GPS devices to identify traffic patterns, predict congestion levels, and optimize traffic signal operations. In addition, the system supports route optimization and rapid response to traffic incidents.