Show simple item record

dc.contributor.authorWesonga, Ronald
dc.contributor.authorNabugoomu, Fabian
dc.contributor.authorMasimbi, Brian
dc.date.accessioned2018-01-18T02:24:57Z
dc.date.available2018-01-18T02:24:57Z
dc.date.issued2014
dc.identifier.citationWesonga, R., Nabugoomu, F., Masimbi, B. (2014). Airline delay time series differentials: Autoregressive integrated moving average model. International Journal of Aviation Systems, Operations and Training, 1(2): 64-76en_US
dc.identifier.issn2334-5314
dc.identifier.uriDOI: 10.4018/IJASOT.2014070105
dc.identifier.urihttp://hdl.handle.net/10570/5845
dc.description.abstractFlight delays affect passenger travel satisfaction and increase airline costs. The authors explore airline differences with a focus on their delays based on autoregressive integrated moving averages. Aviation daily data were used in the analysis and model development. Time series modelling for six airlines was done to predict delays as a function of airport's timeliness performance. Findings show differences in the time series prediction models by airline. Differential analysis in the time series prediction models for airline delay suggests variations in airline efficiencies though at the same airport. The differences could be attributed to different management styles in the countries where the airlines originate. Thus, to improve airport timeliness performance, the study recommends airline disaggregated studies to explore the dynamics attributable to determinants of airline unique characteristics.en_US
dc.language.isoenen_US
dc.publisherIGI Globalen_US
dc.subjectAirporten_US
dc.subjectAirlinesen_US
dc.subjectDeveloping airportsen_US
dc.subjectForecastingen_US
dc.subjectForeign exchange earningen_US
dc.titleAirline delay time series differentials: Autoregressive integrated moving average modelen_US
dc.typeJournal articleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record