The goal of SIMBAD is to develop and evaluate a set of machine learning approaches aimed at providing state of-the-art ATM microsimulation models with the level of reliability, tractability and interpretability required to effectively support performance evaluation at ECAC level.
SIMBAD is conducted by a consortium composed by Nommon Solutions and Technologies (Coordinator), CRIDA, Fraunhofer Society, University of Piraeus Research Centre, and Technical University of Catalonia.
