  Traffic assignment (TA) is crucial in optimizing transportation systems and
consists in efficiently assigning routes to a collection of trips. Existing TA
algorithms often do not adequately consider real-time traffic conditions,
resulting in inefficient route assignments. This paper introduces METIS, a
cooperative, one-shot TA algorithm that combines alternative routing with edge
penalization and informed route scoring. We conduct experiments in several
cities to evaluate the performance of METIS against state-of-the-art one-shot
methods. Compared to the best baseline, METIS significantly reduces CO2
emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip
distribution considerably while still having low computational time. Our study
proposes METIS as a promising solution for optimizing TA and urban
transportation systems.
