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Combining Simulation Models and Big Data Analytics for ATM Performance Analysis

Objective

The development of performance modelling methodologies able translate new ATM concepts and technologies into their impact on high-level, system wide KPIs has been a long-time objective of the ATM research community. Bottom-up, microsimulation models are often the only feasible approach to address this problem in a reliable manner. However, the practical application of large-scale simulation models to strategic ATM performance assessment is often hindered by their computational complexity. 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. The specific objectives of the project are the following:
1. Explore the use of machine learning techniques for the estimation of hidden variables from historical air traffic data, with particular focus on airspace users’ preferences and behaviour, in order to enable a more robust calibration of air traffic microsimulation models.
2. Develop new machine learning algorithms for the classification of traffic patterns that enable the selection of a sufficiently representative set of simulation scenarios allowing a comprehensive assessment of new ATM concepts and solutions.
3. Investigate the use of active learning metamodelling to facilitate a more efficient exploration of the input output space of complex simulation models through the development of more parsimonious performance metamodels, i.e., analytical input/output functions that approximate the results of a more complex function defined by the microsimulation models.
4. Demonstrate and evaluate the newly developed methods and tools through a set of case studies in which the proposed techniques will be integrated with existing, state-of-the-art ATM simulation tools and used to analyse a variety of ATM performance problems.

Field of science

  • /natural sciences/computer and information sciences/data science/big data
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning
  • /social sciences/educational sciences/pedagogy/active learning

Call for proposal

H2020-SESAR-2019-2
See other projects for this call

Funding Scheme

SESAR-RIA - Research and Innovation action

Coordinator

NOMMON SOLUTIONS AND TECHNOLOGIES SL
Address
Calle Claudio Coello 124 - Planta 4A Trasera
28006 Madrid
Spain
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
EU contribution
€ 314 312,50

Participants (4)

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
Germany
EU contribution
€ 183 000
Address
Hansastrasse 27C
80686 Munchen
Activity type
Research Organisations
CENTRO DE REFERENCIA INVESTIGACION DESARROLLO E INNOVACION ATM, A.I.E.
Spain
EU contribution
€ 195 375
Address
Avda De Aragon 402 4 Edificio Allende
28022 Madrid
Activity type
Research Organisations
UNIVERSITY OF PIRAEUS RESEARCH CENTER
Greece
EU contribution
€ 125 500
Address
Gr. Lampraki 122
185 32 Piraeus
Activity type
Higher or Secondary Education Establishments
UNIVERSITAT POLITECNICA DE CATALUNYA
Spain
EU contribution
€ 181 750
Address
Calle Jordi Girona 31
08034 Barcelona
Activity type
Higher or Secondary Education Establishments