Descrizione del progetto
Migliori simulazioni delle pratiche di gestione del traffico aereo favoriscono una valutazione conclusiva
Con la continua crescita del numero di aerei nei cieli e di quelli che rullano a terra, la gestione del traffico aereo diventa sempre più impegnativa. La Conferenza europea dell’aviazione civile, fondata nel 1955 come organizzazione intergovernativa e paneuropea, promuove politiche e pratiche tra i suoi Stati membri a sostegno della sicurezza, dell’efficienza e della sostenibilità del sistema di trasporto aereo europeo. Il progetto SIMBAD, finanziato dall’UE, farà progredire la valutazione efficiente e affidabile delle prestazioni di queste politiche e pratiche sviluppando nuovi approcci di modellizzazione delle prestazioni basati sulla combinazione di tecniche di apprendimento automatico e microsimulazione del traffico aereo.
Obiettivo
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.
Campo scientifico
- natural sciencescomputer and information sciencesdata sciencebig data
- social sciencessocial geographytransporttransport planningair traffic management
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- social scienceseducational sciencespedagogyactive learning
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
Parole chiave
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
28020 Madrid
Spagna
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.