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Research Article
Statistical Characterization of Airplane Delays
Benjamin Schäfer
Queen Mary University of London
Corresponding Author
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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.
Keywords
aviation industry, globally connected economy, COVID-19 pandemic
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CURRENT STATUS: Under Review
Version 1
Posted 29 Dec, 2020
No community comments so far
Review #1 received
Received 15 Jan, 2021
Reviewer #6 agreed
On 03 Jan, 2021
Reviewer #4 agreed
On 03 Jan, 2021
Reviewer #5 agreed
On 03 Jan, 2021
Reviewer #2 agreed
On 03 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Reviewer #3 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Editor assigned
On 03 Jan, 2021
Editor invited
On 24 Dec, 2020
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On 24 Dec, 2020
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On 21 Dec, 2020
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Statistical Characterization of Airplane Delays
Benjamin Schäfer
Queen Mary University of London
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 29 Dec, 2020
No community comments so far
Review #1 received
Received 15 Jan, 2021
Reviewer #6 agreed
On 03 Jan, 2021
Reviewer #4 agreed
On 03 Jan, 2021
Reviewer #5 agreed
On 03 Jan, 2021
Reviewer #2 agreed
On 03 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Reviewer #3 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Editor assigned
On 03 Jan, 2021
Editor invited
On 24 Dec, 2020
Submission checks complete
On 24 Dec, 2020
First submitted
On 21 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.