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dc.contributor.authorMonmousseau, Philippe
dc.contributor.authorMarzuoli, Aude
dc.contributor.authorFeron, Eric
dc.contributor.authorDelahaye, Daniel
dc.date.accessioned2020-06-02T11:51:55Z
dc.date.available2020-06-02T11:51:55Z
dc.date.issued2020-04-29
dc.identifier.urihttp://hdl.handle.net/10754/662969
dc.description.abstractThis paper aims at analyzing the effect on the US air transportation system of the travel restriction measures implemented during the COVID-19 pandemic from a passenger perspective. Flight centric data are not already publicly and widely available therefore the traditional metrics used to measure the state of this system are not yet available. Seven metrics based on three different passenger-generated datasets are proposed here. They aim to measure in close to real-time how the travel restriction measures impacted the relation between major stakeholders of the US air transportation system, namely passengers, airports and airlines.
dc.description.sponsorshipThe authors would like to thank Nikunj Oza from NASA-Ames, the French École Nationale de l’Aviation Civile and the King Abdullah University of Science and Technology for their financial support, as well as SafeGraph for making their data available for this study. The authors would also like to deeply thank all workers and researchers associated in the fight against the COVID-19 pandemic, with a special thought to health-care workers and providers
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2004.14372
dc.rightsArchived with thanks to arXiv
dc.subjectAir transportation system
dc.subjectpassenger-generated data
dc.subjectpassenger-centric metrics
dc.subjectCOVID19
dc.titlePutting the Air Transportation System to sleep: a passenger perspective measured by passenger-generated data
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionOptimization and Machine Learning Team
dc.contributor.institutionPrincipal Scientist, Replica
dc.contributor.institutionProfessor, Division of Electrical, Computer and Mathematical Science and Engineering
dc.contributor.institutionHead of Optimization and Machine Learning Team
dc.identifier.arxividarXiv:2004.14372
refterms.dateFOA2020-06-02T11:53:42Z


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