G. Gurtner

Multi-Scale Analysis of the European Airspace Using Network Community Detection

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 airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and 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.

The economic value of additional airport departure capacity

This article presents a model for the economic value of extra capacity at an airport. The model is based on a series of functional relationships linking the benefits of extra capacity and the associated costs. It takes into account the cost of delay for airlines and its indirect consequences on the airport, through the loss or gain of aeronautical and non-aeronautical revenues. The model is highly data-driven and to this end a number of data sources have been used. In particular, special care has been used to take into account the full distribution of delay at the airports rather than its average only. The results with the simple version of the model show the existence of a unique maximum for the operating profit of the airport in terms of capacity. The position of this maximum is clearly dependent on the airport and also has an interesting behaviour with the average number of passenger per aircraft at the airport and the predictability of the flight departure times. In addition, we also show that there exists an important trade-off between an increased predictability and the punctuality at the airport. Finally, it is shown that a more complex behavioural model for passengers can introduce several local maxima in the airport profit and thus drive the airport towards suboptimal decisions.