SIMBAD in the 12th SESAR Innovation Days
The SIMBAD project team will participate with three different presentations
top of page
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.
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.
The project started on 1 January 2021 and will run for 24 months.
bottom of page