  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.
