  Avoiding congestion and controlling traffic in urban scenarios is becoming
nowadays of paramount importance due to the rapid growth of our cities'
population and vehicles. The effective control of urban traffic as a means to
mitigate congestion can be beneficial in an economic, environmental and health
way. In this paper, a framework which allows to efficiently simulate and
optimize traffic flow in a large roads' network with hundreds of vehicles is
presented. The framework leverages on an Answer Set Programming (ASP) encoding
to formally describe the movements of vehicles inside a network. Taking
advantage of the ability to specify optimization constraints in ASP and the
off-the-shelf solver Clingo, it is then possible to optimize the routes of
vehicles inside the network to reduce a range of relevant metrics (e.g., travel
times or emissions). Finally, an analysis on real-world traffic data is
performed, utilizing the state-of-the-art Urban Mobility Simulator (SUMO) to
keep track of the state of the network, test the correctness of the solution
and to prove the efficiency and capabilities of the presented solution.
