Reducing Traffic Congestion at Urban Intersections Through Adaptive Signal Control: A Sumo-Based Study

Rapid urbanization and the growing number of vehicles in Uzbekistan’s major cities, particularly in Tashkent, have led to severe traffic congestion, prolonged delays, and heightened environmental pollution. Traditional Fixed-Time traffic signal control systems currently used in many urban intersections lack the dynamic flexibility required to manage fluctuating traffic volumes effectively. This study evaluates the performance of an Adaptive Traffic Signal Control system compared to conventional Fixed-Time control at a high-density urban intersection using the SUMO (Simulation of Urban Mobility) micro-simulation platform. Key performance indicators annual fuel consumption, CO2 and NOx emissions, and average time loss per vehicle were quantitatively assessed. The simulation results demonstrate that adaptive signal control substantially outperforms traditional fixed timing. Specifically, the implementation of the adaptive system achieved a 35.39% reduction in annual fuel consumption (saving 47,254.65 liters/year) and a corresponding 35.39% decrease in tailpipe emissions, mitigating 129.95 tons of CO2 and 189.02 kg of NOx annually. Furthermore, average annual delay per vehicle dropped by 33.30%, saving 3.90 hours per vehicle each year. These empirical findings from SUMO simulations demonstrate that adopting adaptive traffic control presents a highly scalable, economically viable, and environmentally sustainable framework to support Uzbekistan’s green urban mobility initiatives.