Skip to Main content Skip to Navigation
Conference papers

Selection and evaluation of air traffic complexity metrics

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [24 references]  Display  Hide  Download
Contributor : Laurence Porte Connect in order to contact the contributor
Submitted on : Tuesday, May 13, 2014 - 10:48:07 AM
Last modification on : Tuesday, October 19, 2021 - 11:02:47 AM
Long-term archiving on: : Wednesday, August 13, 2014 - 10:41:16 AM


Files produced by the author(s)




David Gianazza, Kevin Guittet. Selection and evaluation of air traffic complexity metrics. DASC 2006, 25th Digital Avionics Systems Conference, Oct 2006, Portland, United States. pp 1-12, ⟨10.1109/DASC.2006.313710⟩. ⟨hal-00938180⟩



Record views


Files downloads