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SIMBAD
Combining Simulation Models and Big Data Analytics for ATM Performance Analysis
The project
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.
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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.
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The project started in January 2021 and finished in January 2023.
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