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Statistical characterization of airplane delays.

Evangelos Mitsokapas1, Benjamin Schäfer2, Rosemary J Harris1

  • 1School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK.

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Flight delays significantly impact airline and airport performance. This study quantifies delays using statistical analysis, revealing power-law distributions and pandemic-driven changes.

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Area of Science:

  • Aviation analytics
  • Statistical modeling
  • Transportation science

Background:

  • Aviation is crucial for global connectivity, with customer satisfaction heavily influenced by flight delays.
  • Quantifying flight delays across numerous flights, airports, and airlines presents a significant analytical challenge.

Purpose of the Study:

  • To develop a statistical procedure for comparing mean delays and extreme delay events.
  • To analyze flight arrival delay data from UK airports between 2018 and 2020.
  • To investigate the impact of the COVID-19 pandemic on flight delay statistics.

Main Methods:

  • Statistical analysis of flight arrival delay data.
  • Development of a comparative procedure for mean and extreme delays.
  • Identification of delay distribution patterns, including power-law decay.

Main Results:

  • A method to compare mean delays and extreme events across airlines and airports was established.
  • Large flight delays exhibit a power-law decay distribution.
  • Significant shifts in delay statistics were observed during the COVID-19 pandemic.
  • Flight delays can be modeled as a superposition of simple distributions, forming a superstatistics.

Conclusions:

  • The study provides a robust statistical framework for analyzing and comparing flight delays.
  • Understanding delay patterns, especially extreme events, is vital for airline and airport operations.
  • The COVID-19 pandemic markedly altered flight delay characteristics, highlighting the dynamic nature of air travel.