Statistical characterization of airplane delays.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
12 04 2021
Historique:
received: 21 12 2020
accepted: 22 03 2021
entrez: 13 4 2021
pubmed: 14 4 2021
medline: 14 4 2021
Statut: epublish

Résumé

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.

Identifiants

pubmed: 33846509
doi: 10.1038/s41598-021-87279-8
pii: 10.1038/s41598-021-87279-8
pmc: PMC8041857
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

7855

Subventions

Organisme : H2020 Marie Sklodowska-Curie Actions
ID : 840825

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Auteurs

Evangelos Mitsokapas (E)

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

Benjamin Schäfer (B)

School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK. b.schaefer@qmul.ac.uk.

Rosemary J Harris (RJ)

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

Christian Beck (C)

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

Classifications MeSH