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

F Olivares1, L Zunino2, M Zanin1

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This study models aircraft landing sequences using a modulated Markov jitter to analyze operational efficiency in European airports. Findings reveal correlations in landing flow that serve as efficiency metrics and show significant post-COVID-19 changes beyond traffic reduction.

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Area of Science:

  • Complex Systems Science
  • Transportation Engineering
  • Operations Research

Background:

  • Air transportation systems exhibit complex dynamics leading to emergent behaviors.
  • Aircraft landing sequencing is crucial for operational efficiency but difficult to control.
  • Existing models may not fully capture the ordinal pattern properties of real-world landing operations.

Purpose of the Study:

  • To develop and validate a model for representing ordinal pattern properties of aircraft landing sequences.
  • To identify and quantify metrics of operational efficiency in air traffic management.
  • To analyze the impact of the COVID-19 pandemic on air traffic landing dynamics.

Main Methods:

  • A modulated Markov jitter model was developed to represent landing sequence dynamics.
  • Model parameters were tuned using the Permutation Jensen-Shannon Distance to match real and synthetic data.
  • Correlation analysis of hourly landing flow was used to assess airport efficiency.

Main Results:

  • The study identified correlations between consecutive hourly landing flows as a quantifiable metric of airport efficiency.
  • Significant changes in landing dynamics were observed post-COVID-19, exceeding simple traffic volume reductions.
  • The model successfully captured ordinal pattern properties of real landing operations.

Conclusions:

  • The modulated Markov jitter model provides a novel approach to understanding air traffic landing dynamics.
  • Landing flow correlation serves as a valuable metric for assessing and improving operational efficiency.
  • The COVID-19 pandemic induced lasting changes in air traffic patterns beyond traffic volume.