Worldwide, many cities spend considerable effort to reduce traffic and specifically to avoid traffic congestions. Adaptive traffic control systems serve this purpose by dynamically adjusting traffic signals for optimizing the traffic flow on intersections. Systems such as SCOOT are based on an “intelligent” combination of different traffic optimization strategies. However, they miss the possibility (i) to add and change on-demand rules to implement new optimization strategies, and (ii) to simulate the outcome of new strategies on-the-fly which is similar to the capabilities of microscopic traffic simulation tools such as SUMO. In order to overcome the above limitations, we present a novel approach for calculating signal phase plans (SPPs) used for optimizations in traffic control systems. Our approach is based on Answer Set Programming (ASP) and combines ASP encodings of an abstract mesoscopic flow model and a strategy for generating possible SPPs. Experimental results shows that traffic simulation can be well approximated and that the generated SPPs improve the traffic flow effectively.