New centrality and causality metrics assessing air traffic network interactions

TL;DR

This paper introduces novel centrality and causality metrics tailored for air traffic management networks, addressing the limitations of existing measures in capturing delay propagation and system-wide effects.

Contribution

The authors develop generalized metrics that incorporate temporal and multi-layer structures and focus on extreme event propagation, improving analysis of ATM systems.

Findings

Existing metrics are inadequate for ATM delay analysis

New metrics effectively capture temporal and multi-layer network features

Proposed causality metric models extreme event propagation

Abstract

In ATM systems, the massive number of interacting entities makes it difficult to identify critical elements and paths of disturbance propagation, as well as to predict the system-wide effects that innovations might have. To this end, suitable metrics are required to assess the role of the interconnections between the elements and complex network science provides several network metrics to evaluate the network functioning. Here we focus on centrality and causality metrics measuring, respectively, the importance of a node and the propagation of disturbances along links. By investigating a dataset of US flights, we show that existing centrality and causality metrics are not suited to characterise the effect of delays in the system. We then propose generalisations of such metrics that we prove suited to ATM applications. Specifically, the new centrality is able to account for the temporal and multi-layer structure of ATM network, while the new causality metric focuses on the propagation of extreme events along the system.