Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146108
Title: Universal patterns in passenger flight departure delays
Authors: Wang, Yanjun
Cao, Yakun
Zhu, Chenping
Wu, Fan
Hu, Minghua
Duong, Vu
Watkins, Michael
Barzel, Baruch
Stanley, H. Eugene
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Wang, Y., Cao, Y., Zhu, C., Wu, F., Hu, M., Duong, V., . . . Stanley, H. E. (2020). Universal patterns in passenger flight departure delays. Scientific Reports, 10(1), 6890-. doi:10.1038/s41598-020-62871-6
Journal: Scientific Reports 
Abstract: Departure delays are a major cause of economic loss and inefficiency in the growing industry of passenger flights. A departure delay of a current flight is inevitably affected by the late arrival of the flight immediately preceding it with the same aircraft. We seek to understand the mechanisms of such propagated delays, and to obtain universal metrics by which to evaluate an airline’s operational effectiveness in delay alleviation. Here we use big data collected by the American Bureau of Transportation Statistics to design models of flight delays. Offering two dynamic models of delay propagation, we divided all carriers into two groups exhibiting a shifted power law or an exponentially truncated shifted power law delay distribution, revealing two universal delay propagation classes. Three model parameters, extracted directly from dual data mining, help characterize each airline’s operational efficiency in delay mitigation. Therefore, our modeling framework provides the crucially lacking evaluation indicators for airlines, potentially contributing to the mitigation of future departure delays.
URI: https://hdl.handle.net/10356/146108
ISSN: 2045-2322
DOI: 10.1038/s41598-020-62871-6
Rights: © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles

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