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.
|State||Published - 1 Dec 2020|
Bibliographical noteFunding Information:
This research was supported by the National Natural Science Foundation of China (Grant Nos. 61304190, 61773203, 11175086, 11775111, and U1833126), the Fundamental Research Funds for the Central Universities (Grant No. NJ20150030). This research was also supported by the Israel Science Foundation (Grant No. 499/19). We thank Max Li from Massachusetts Institute of Technology for proofreading the manuscript.
© 2020, The Author(s).