This article investigates methods for optimizing traffic light control parameters with the aim of improving urban traffic efficiency. Modern approaches to ensuring the sustainability of urban transport systems are discussed. The study concludes that simulation modelling serves as a leading methodology for analysing transportation systems and identifying optimal solutions to diverse challenges, which ultimately enhances traffic flow characteristics and increases the resilience of the transport network. A simulation model of a major city intersection has been developed, an optimization experiment carried out, and optimal values for traffic light signal phases have been determined. The model was parameterized using real traffic flow data collected through video observations during morning peak hours. An optimization experiment was conducted with traffic light phase durations as variable inputs, aiming to minimize total vehicle travel time through the intersection. The results demonstrated significant improvements: a reduction in crossing times of 11.71% for passenger cars, 16.93% for buses, and 17.68% for trucks. These optimizations translate into direct benefits such as reduced fuel consumption from idling and substantial indirect co-benefits, including social gains from shorter travel times, enhanced road safety, and a positive environmental impact through lowered vehicular emissions. The research underscores the efficacy of integrating simulation-based Decision Support Systems into urban traffic management for developing adaptive, sustainable, and human-centric transport networks.