Descripción del proyecto
Unas mejores simulaciones de las prácticas de gestión del tráfico aéreo propician una evaluación concluyente
A medida que sigue aumentando el número de aviones que surcan los cielos y ruedan en tierra, la gestión del tráfico aéreo es un reto cada vez mayor. La Conferencia Europea de Aviación Civil, fundada en 1955 como organización intergubernamental y paneuropea, promueve entre sus Estados miembros políticas y prácticas que respaldan la seguridad, la eficiencia y la sostenibilidad del sistema de transporte aéreo de Europa. El proyecto SIMBAD, financiado con fondos europeos, profundizará en la evaluación eficaz y fiable de los resultados de estas políticas y prácticas mediante el desarrollo de nuevos métodos de modelización del rendimiento basados en la combinación de técnicas de aprendizaje automático y microsimulación del tráfico aéreo.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Régimen de financiación
RIA - Research and Innovation actionCoordinador
28020 Madrid
España
Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.