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Airport Scheduling and Operational Performance: A Clustering Analysis of Airport Response to COVID-19

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dc.contributor.authorAlsalous, Osamaen
dc.contributor.authorHotle, Susanen
dc.date.accessioned2024-08-27T19:42:38Zen
dc.date.available2024-08-27T19:42:38Zen
dc.date.issued2023-06en
dc.description.abstractIn early 2020, the Coronavirus disease 2019 (COVID-19) pandemic started and forced air travel demand to decrease sharply in most parts of the world due to travel restrictions that were put in place to limit the spread of the virus. The pandemic also impacted capacity due to reasons such as workforce social distancing, days when Air Traffic Control (ATC) facilities were shut down due to COVID cases, and financial challenges due to the decreased demand. The reduced demand created a unique challenge in the system since capacity exceeded demand by very large margins in the NAS, however, delays in the system did not fall to zero despite the sharp drop in demand. This study analyzed operations at 77 United States (US) airports to compare and contrast their responses to the COVID-19 pandemic in terms of capacity, throughput, and the resulting operational performance. We evaluate the response of airports to the initial shock event during 2020 in addition to the recovery period that followed in 2021. The data showed a 67% decline in total operations at the lowest point during the pandemic. The impact during the shock time period varied greatly across the airports, ranging from a reduction of 14.8% at MEM to 81.5% at LGA. We performed a clustering analysis to study airports’ response to the COVID-19 pandemic. There was a number of airport characteristics that were correlated to the changes in airport metrics. For example, the data showed that being located in a multi-airport city was significantly correlated to the decrease in operations during the shock, however, it was not significant in the recovery trends. Our analysis showed that delays in the system did not change proportionately to the change in operations. Similarly, there were only minor improvements in punctuality, on-time flights at the ASPM 77 airports increased by 9.5% while operations declined by 52% during the shock event time period compared to pre-COVID. Part of this phenomenon was a result of schedule peaking which caused delays due to creating busy hours at the airports. This analysis can inform airport management when responding to future disruptive events, it provides insight into airport operational resiliency, response to disruption, and demand recovery patterns based on airport characteristics.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.2514/6.2023-4212en
dc.identifier.urihttps://hdl.handle.net/10919/121025en
dc.language.isoenen
dc.publisherAmerican Institute of Aeronautics and Astronauticsen
dc.subjectAirportsen
dc.subjectCOVID-19en
dc.titleAirport Scheduling and Operational Performance: A Clustering Analysis of Airport Response to COVID-19en
dc.title.serialAIAA AVIATION 2023 Forumen
dc.typeConference proceedingen
dc.type.dcmitypeTexten

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