This article presents new principles for managing signal-controlled intersections. By using machine learning and radar detection a controller has been developed using the optimization tool UPPAAL Stratego. Using VISSIM, the controller has been tested in four signal-controlled intersections . The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30% and 50% in average delays, queues and number of stops in all four intersections. Moreover, the fuel consumption and total travel time through the four intersections are reduced by about 20%.