Multi-scale analysis of the European airspace using network community detection

TL;DR

This paper models the European airspace as a multi-scale traffic network and uses community detection algorithms to analyze its structure, aiming to improve airspace management and design.

Contribution

It introduces a multi-scale network model of European airspace and evaluates community detection algorithms for potential airspace optimization.

Findings

Community detection reveals meaningful airspace partitions.

Null models considering spatial distance improve community detection.

Algorithms can guide the design of new control units.

Abstract

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspaces and improve it by guiding the design of new ones. Specifically, we compare the performance of three community detection algorithms, also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.