Budak, Zulal Hilal YildizCeltek, Seyit AlperenDurdu, Akif2026-03-102026-03-1020261300-06321303-6203https://doi.org/10.55730/1300-0632.4166https://hdl.handle.net/20.500.13091/13050Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, T & uuml;rkiye, and demonstrated significant performance improvements in comparison to the existing benchmarks within the Simulation of Urban Mobility (SUMO) simulation environment. Specifically, for isolated intersections, the method resulted in a 0.45% reduction in average travel time and a 3.74% reduction in average waiting time when compared to the classical MaxWeightedFlow method. Moreover, it led to a 0.24% increase in average speed, indicating enhanced traffic flow efficiency. For coordinated intersections, the novel MaxWeightedFlow reduced the average travel time by 2.63% and the average waiting time by 17.52%. Additionally, the approach improved the average speed by 1.60%, further underscoring its effectiveness in optimizing traffic dynamics. These findings underscore the effectiveness of the new method in enhancing traffic flow efficiency, leading to faster traffic management and higher operational efficiency, which aligns closely with real-world traffic measurements.eninfo:eu-repo/semantics/openAccessAdaptive Traffic Signal ControlMaximum Weighted Flow MethodReal-Time Traffic Signal ControlRegression MethodsArticle10.55730/1300-0632.41662-s2.0-105030190521