SIMBAD
Financiado
Combining Simulation Models and Big Data Analytics for ATM Performance Analysis
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, mic...
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.
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Duración del proyecto: 26 meses
Fecha Inicio: 2020-10-21
Fecha Fin: 2022-12-31
Fecha Fin: 2022-12-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2022-12-31
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
SESAR-ER4-03-2019:
Complexity and Data Science for ATM Performance
Cerrada
hace 5 años
Presupuesto
El presupuesto total del proyecto asciende a
1,000K€
Líder del proyecto
NOMMON SOLUTIONS AND TECHNOLOGIES
La investigacion de herramientas y soluciones de ayuda a la decision para el diseño, optimizacion y gestion de sistemas complejos. actividad...
Perfil tecnológico
TRL
4-5
260K
Perfil tecnológico
TRL
4-5
260K
125.50K€ |
Participante