Road traffic changes dynamically during some periods, and it can be specialy noticed during peak hours. Nowadays, there can be changes in urban zones depending on the new construction or renovation of old facilities and the increased migration of the population coming to live in cities in search of better opportunities. Due to this problem of increasing traffic congestion on the city streets appears with the growing demand for transport services. For this reason, new solutions are needed for reducing traffic congestion, environmental pollution, and improving the existing transport infrastructure, given that the city streets can no longer be expanded. For these reasons, this thesis aims to present an adaptive traffic light controller to reduce the queue length and number of stop vehicles for traffic intersections. The algorithm has four types of control and was evaluated using real traffic data such as the number of vehicles (traffic demand), and the algorithm was tested on the different conditions where traffic demand was increased or decreased by 40% of the original traffic demand. An adaptive traffic signal controller was tested using a model of the isolated intersection in the microscopic simulator PTV VISSIM. The algorithm of an adaptive traffic light controller was created in the Python programming language, which was used for running and testing our simulations and four types of algorithms through the COM connection Python-VISSIM.