Reconstructing functional networks of air transport delay propagations with minimal information

In the last decade, statistical physics has joined the effort of the scientific community in the endeavour of understanding the structure and dynamics of air transport delay propagation, especially through the reconstruction and analysis of functional networks. While being a powerful instrument, such networks rely on the availability of large quantities of real data, and can only be used to describe historical dynamics. This contribution presents an alternative way of analysing public delay data, based on minimal information and a set of hypotheses about why and where observed delays had to be generated. We show how this analysis allows recovering known behaviours of the system, as the dependence of delays on the saturation of the arrival airport; but also how local and network propagation patterns can be detected ahead of time.