  Urban traffic congestion remains a pressing challenge in our rapidly
expanding cities, despite the abundance of available data and the efforts of
policymakers. By leveraging behavioral system theory and data-driven control,
this paper exploits the DeePC algorithm in the context of urban traffic control
performed via dynamic traffic lights. To validate our approach, we consider a
high-fidelity case study using the state-of-the-art simulation software package
Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC
outperforms existing approaches across various key metrics, including travel
time and CO$_2$ emissions, demonstrating its potential for effective traffic
management
