Skip to Main content Skip to Navigation
Conference papers

Évaluation of air traffic complexity metrics using neural networks and sector status

Abstract : This paper presents an original method to evaluate air traffic complexity metrics. Several complexity indicators, found in the litterature, were implemented and computed, using recorded radar data as input. A principal component analysis (PCA) provides some results on the correlations between these indicators. Neural networks are then used to find a relationship between complexity indicators and the actual sector configurations. Assuming that the decisions to group or split sectors are somewhat related to the controllers workload, this method allows to identify which types of complexity indicators are significantly related to the actual workload.
Document type :
Conference papers
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal-enac.archives-ouvertes.fr/hal-00938105
Contributor : Laurence Porte <>
Submitted on : Tuesday, May 13, 2014 - 11:12:04 AM
Last modification on : Tuesday, October 27, 2020 - 1:14:01 PM
Long-term archiving on: : Wednesday, August 13, 2014 - 10:40:58 AM

File

296.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00938105, version 1

Collections

Citation

David Gianazza, Kevin Guittet. Évaluation of air traffic complexity metrics using neural networks and sector status. ICRAT 2006, 2nd International Conference on Research in Air Transportation, Jun 2006, Belgrade, Serbia. pp xxxx. ⟨hal-00938105⟩

Share

Metrics

Record views

411

Files downloads

329