Mathematical modeling of an AI-based adaptive traffic signal control system for urban intersections: a case study of Ferghana city

This study presents an AI-based adaptive traffic management system for the urban road network of Fergana city, Uzbekistan. A comprehensive field campaign conducted from January to March 2025 at four key intersections employed YOLOv5-based object detection cameras to measure real-time vehicle arrival rates and queue lengths. The collected empirical data were used to parameterize a store-and-forward Model Predictive Control (MPC) framework that dynamically allocates green time to minimize total network delay. System performance was evaluated through microscopic traffic simulation in SUMO (v1.19.0) using a two-hour morning peak demand of 4,100 vehicles/hour. Compared to the existing static signal plan, simulation results indicate that The proposed adaptive control framework has the potential to reduce average intersection delay by 27.5 % (from 82.5 to 59.8 s/vehicle), decrease average queue length by 29.5 % (from 44 to 31 vehicles/approach), and increase network throughput by 18.5 % (from 4,100 to 4,860 vehicles/hour). The proposed framework integrates field-calibrated YOLOv5 traffic detection with Model Predictive Control into a unified adaptive signal control architecture for medium-density urban networks, providing a practically deployable methodology for intelligent traffic management in Uzbekistan.