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SIMBAD

Combining Simulation Models and Big Data Analytics for ATM Performance Analysis

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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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Latest news

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This project has received funding from the SESAR Joint Undertaking (JU) under grant agreement No 894241. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union. © 2021 SIMBAD Consortium. All rights reserved.

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