SIMBAD: Combining Simulation Models and Big Data Analytics for ATM Performance Analysis
- Contents:
The overall goal of SIMBAD is to advance the state-of-the-art in ATM performance modelling through the combination of model-based approaches and data-driven methods, taking advantage of the opportunities opened by recent advances in big data technologies and data science.
The approach proposed by SIMBAD relies on three main pillars:
1. the estimation of hidden variables in aircraft trajectory models,
2. the development of new traffic pattern clustering and classification techniques, and
3. the application of active learning meta-modelling to large-scale traffic simulations.
Duration:
2020 - 2022
Objectives:
The specific objectives of the project are the following:
1. Explore the use of machine learning techniques for the modelling of trajectories and estimation of hidden variables from historical air traffic data.
2. Develop new machine learning algorithms for traffic pattern classification.
3. Investigate the use of active learning metamodelling to enable a more efficient exploration of the input‑output space of complex ATM simulation models.
4. Demonstrate and evaluate the newly developed techniques in order to assess their maturity, derive recommendations on how to apply them to ATM performance assessment, and propose a roadmap for the transition of the project results to the next stages of the R&D cycle.
Person Responsible:
Funding Body:
SESAR JU - H2020
Research topic:
Machine Learning, Reinforcement Learning
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