Highlights Statistical methods can support quantification of delay abnormalities in airline networks. Network metrics can supplement, but not replace data-driven methods. Hybrid data-driven network metrics can identify impacts of major events. Individual airline resilience is important to analyze, in addition to the overall system.

    Abstract Network theory has provided key insights into the overall resilience of air transportation systems. We expand upon these insights by using Mahalanobis distance to quantify delay abnormalities, complex network metrics for high-level insights, and a hybrid method that combines data-driven and network approaches. We apply these methods to public data and discuss trends in resilience among four US airlines. We find that our data-driven methods enable more detailed insights into airline resilience than traditional network methods. We also find that simultaneously considering all three approaches provides a more comprehensive understanding of resilience than the consideration of any one in isolation.


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    Title :

    Data-driven analysis of resilience in airline networks


    Contributors:


    Publication date :

    2020-08-23




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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