Benchmarking different aspects of air transport, and especially airport operations, is a research topic both important and complex, as proposed solutions are usually limited by data availability and underlying hypotheses. We here propose the use of two well-known statistical physics concepts, the Hurst exponent and the irreversibility, to measure the presence of interactions between landing flights from a macro-scale perspective. We firstly present a synthetic model of landings at an airport, showing how the two metrics are able to detect interactions arising from high volumes of traffic. Real landing data for twelve major European airports and ten Chinese ones are then analysed, showing that more interactions are present than what expected for the corresponding traffic. Results are nevertheless not homogeneous, with some airports (most notably Milano Malpensa, Madrid and Paris Charles de Gaulle) yielding statistically significant high values; and with Frankfurt airport having been especially impacted by the COVID-19 pandemics, which resulted in an increase in interactions in spite of a reduction in traffic. Some possible reasons behind these results, and their operational significance in terms of efficiency and safety, are finally discussed.
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