Abstract
The aviation industry is of great importance for a globally connected economy. Customer satisfaction with airlines and airport performance is considerably influenced by how much flights are delayed. But how should the delay be quantified with thousands of flights for each airport and airline? Here, we present a statistical analysis of arrival delays at several UK airports between 2018 and 2020. We establish a procedure to compare both mean delay and extreme events among airlines and airports, identifying a power-law decay of large delays. Furthermore, we note drastic changes in plane delay statistics during the COVID-19 pandemic. Finally, we find that delays are described by a superposition of simple distributions, leading to a superstatistics.
Highlights
The aviation industry is of great importance for a globally connected economy
One problem of any data-driven approach is that many articles on aviation research solely rely on proprietary data: In a recent review investigating 200 research articles, 68% were based on proprietary d ata[10]
We show that the power law of large positive delays can be linked to a superposition of exponential delays with a varying decay parameter, in a superstatistical approach
Summary
The aviation industry is of great importance for a globally connected economy. Customer satisfaction with airlines and airport performance is considerably influenced by how much flights are delayed. Thereby, we consider all possible delay values, from highly negative delays (i.e. flights arriving significantly earlier than their scheduled arrival time) to severely positively delayed flights These delay distributions are influenced by many different aspects, including random events, congestion, delay propagation between airports[11,12] and (for long-haul flights on large scales) the topological structure of the worldwide air transportation n etwork[13,14]. To explain the emergence of heavy tails in a local distribution, i.e. extreme deviations from the mean, we will utilize superstatistical modelling[15] Such an approach has been successfully applied in transport before, for modelling train d elays[16]; it has attracted recent interest when describing fluctuations in the energy system[17] and air pollutant concentrations[18] and it has been extended to the general framework of diffusing diffusivities in nonequilibrium statistical physics and biologically inspired physics[19,20,21]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
