Dealing with the ever-increasing demand for traffic management is one of the main challenges of the 21st century. The issue is much more apparent in urban areas during rush hours. Traffic congestion causes economic losses due to delays and increased fuel consumption and, on top of that, is a major health risk. Intelligent centralized traffic routing is an important concept aiming at reducing traffic congestion in urban areas by more effectively utilizing road networks. In this demo, we present a framework that, in a nutshell, integrates techniques for intelligent centralized traffic routing into the well-known SUMO simulator, so these techniques can be evaluated in realistic settings on real/realistic datasets. In particular, the framework automatically identifies ``problematic'' urban regions by analyzing historical traffic data, then simplifies the road networks by precomputing promising routes (for each considered traffic flow), and finally, leverages a planning-based approach to generate routes. Our framework is evaluated on a real dataset from Dublin's metropolitan area.