Markov-modulated model for landing flow dynamics: An ordinal analysis validation

TL;DR

This paper introduces a Markov-modulated model to analyze landing flow dynamics at airports, revealing changes in operational efficiency and flow patterns, especially before and after COVID-19, with implications for real-world airport management.

Contribution

The paper presents a novel Markov-based model for ordinal pattern analysis of landing sequences, validated against real data, and explores its application in assessing airport efficiency and impacts of COVID-19.

Findings

Correlation between hourly landing flows varies across airports.

Post-COVID-19 dynamics differ significantly from pre-pandemic patterns.

Model effectively captures operational changes in landing sequences.

Abstract

Air transportation is a complex system characterised by a plethora of interactions at multiple temporal and spatial scales; as a consequence, even simple dynamics like sequencing aircraft for landing can lead to the appearance of emergent behaviours, which are both difficult to control and detrimental to operational efficiency. We propose a model, based on a modulated Markov jitter, to represent ordinal pattern properties of real landing operations in European airports. The parameters of the model are tuned by minimising the distance between the probability distributions of ordinal patterns generated by the real and synthetic sequences, as estimated by the Permutation Jensen-Shannon Distance. We show that the correlation between consecutive hours in the landing flow changes between airports, and that it can be interpreted as a metric of efficiency. We further compare the dynamics pre and post COVID-19, showing how this has changed beyond what can be attributed to a simple reduction of traffic. We finally draw some operational conclusions, and discuss the applicability of these findings in a real operational environment.