A study of airline route data

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dc.availability.bitstreamembargoed
dc.check.date2025-12-11
dc.contributor.advisorO'Sullivan, Janeten
dc.contributor.advisorWolsztynski, Ericen
dc.contributor.authorYu, Xiaochen
dc.date.accessioned2021-05-17T11:36:29Z
dc.date.available2021-05-17T11:36:29Z
dc.date.issued2020
dc.date.submitted2020
dc.description.abstractFor decades, with the advancement of airline information system construction, the aviation industry has successfully built a number of information systems. An enormous amount of data has been accumulated through the successful operation of these systems for the aviation sector. The effective use of these invaluable data assets has increasingly become a requirement for the relevant airline departments, and the focus of aviation industry. Revenue management is crucial for measuring the operational success of the airlines. However, the traditional forecasting methods cannot support processing of the underlying data that keeps changing over time, which have impaired the accuracy of the forecast, and thus, the credibility. The new airline passenger ticket revenue pricing methods proposed in this thesis have explored the possibility of solving the existing problems through advanced modeling techniques, and thus provide a view for better airline route planning and optimisation. First of all, the airline data is classified in a targeted manner. The factors of available seat kilometers, revenue passenger kilometers, load factors, total number of passengers, average fares, etc. collected from different time periods were used to establish a multiple linear regression model in the statistical software, R. Through empirical analysis it is found that the factors affecting the passenger ticket revenue, over different time periods of the same company, are different. Therefore, a multivariate linear regression model was established which was based on the data of different airlines in one specific time period. It was empirically found through this approach that different airlines had different factors affecting ticket revenue in the same time period. A multivariate linear regression model was established for analysing the data of the head office and the various branches at the same time period and through this it was found that the factors affecting the passenger ticket revenue of the head office and the branches at the same time period were different. The research results of this dissertation can provide scientific evidence that airlines should consider analysing real-time ticket data for ticket price and flight plan collected from the IT system to maximise the revenue.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationYu, X. 2020. A study of airline route data. MRes Thesis, University College Cork.en
dc.identifier.endpage55en
dc.identifier.urihttps://hdl.handle.net/10468/11329
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2020, Xiaochen Yu.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectRevenue managementen
dc.subjectMultivariate linear regressionen
dc.subjectStepwise regression methoden
dc.subjectAirline ticket pricingen
dc.subjectAvailable seat kilometersen
dc.subjectRevenue passenger kilometersen
dc.titleA study of airline route dataen
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMRes - Master of Researchen
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