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Latest revision as of 05:53, 2 February 2021
Abstract
International audience; This paper presents an original method to evaluate air traffic complexity metrics. In previous works, we applied a principal component analysis (PCA) to find the correlations among a set of 27 complexity indicators found in the literature. Neural networks were then used to find a relationship between the components and the actual airspace sector configurations. Assuming that the decisions to group or split sectors are somewhat related to the controllers workload, this method allowed us to identify which components were significantly related to the actual workload. We now focus on the subset of complexity indicators issued from these components, and use neural networks to find a simple relationship between these indicators and the sector status.
Original document
The different versions of the original document can be found in:
- http://pom.tls.cena.fr/S2D2/printable_slides_307giana.pdf,
- https://www.researchgate.net/profile/Kevin_Guittet/publication/224057197_Selection_and_Evaluation_of_Air_Traffic_Complexity_Metrics/links/53f70dfe0cf22be01c452fd6.pdf?disableCoverPage=true,
- https://hal-enac.archives-ouvertes.fr/hal-00938180/document,
- https://ieeexplore.ieee.org/document/4106256,
- http://ieeexplore.ieee.org/document/4106256,
- https://hal.archives-ouvertes.fr/hal-00938180,
- https://academic.microsoft.com/#/detail/2533069617
Document information
Published on 01/01/2006
Volume 2006, 2006
DOI: 10.1109/dasc.2006.313710
Licence: CC BY-NC-SA license
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