Extending the KT cellular automata model for signalized urban trafficRigler, Andrej (Avtor) Turk, Goran (Avtor) traffic light modelcellular automataKT modelheterogeneous conditionsurban trafficUrban congestion continues to worsen worldwide, underscoring the need for efficient traffic management models. In this study, we extend the existing kinematic theory (KT) cellular automata (CA) model by incorporating a traffic-light module to systematically evaluate how key parameters affect traffic flow and average velocity on a one-lane road containing multiple signalized intersections. Simulations are primarily conducted under periodic boundary conditions to isolate and examine the influence of each parameter. All original KT model features—including the safety-analysis-based determination of velocity and acceleration at each time step—are retained, enabling realistic heterogeneous driving behavior. Additionally, we analyze arrival and departure dynamics under semi-open boundary conditions to gain deeper insight into urban traffic behavior. The results indicate that the maximum traffic flow at a maximum velocity of 60 km/h and a reaction time of 1.0 s is 794 vehicles/h at a density of 0.4 vehicle/cell. Adaptive acceleration increases traffic flow by up to 20% for densities below 0.7 vehicle/cell and even more at higher densities, while reducing the reaction time by 0.2 s increases traffic flow by up to 17%. Increasing the maximum acceleration by 1 m/s2 yields only a modest rise of up to 5%. At a maximum velocity of 80 km/h, traffic flow is up to 46% higher relative to 40 km/h, although the effect diminishes at higher densities. Furthermore, semi-open boundary conditions produce consistently higher traffic flow than periodic boundaries. These findings demonstrate that the enhanced KT–CA model can capture the effects of driver behavior and traffic-signal timing, offering an improved framework for analyzing and optimizing urban traffic systems.20262026-08-03 13:37:39Članek v reviji185401UDK: 625.746.53:004.94ISSN pri članku: 2076-3417DOI: 10.3390/app16136468COBISS_ID: 286749699sl