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Predictive modelling

2018-01-01Documento de conferência Acesso aberto
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dc.contributor.authorHenriques, Roberto
dc.contributor.authorFeiteira, Inês
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2021-09-30T03:01:21Z
dc.date.available2021-09-30T03:01:21Z
dc.date.issued2018-01-01
dc.descriptionCENTERIS 2018 - International Conference on ENTERprise Information Systems / ProjMAN 2018 - International Conference on Project MANagement / HCist 2018 - International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018
dc.description.abstractNowadays, a downside to traveling is the delays that are constantly being advertised to passengers resulting in a decrease in customer satisfaction and causing costs. Consequently, there is a need to anticipate and mitigate the existence of delays helping airlines and airports improving their performance or even take consumer-oriented measures that can undo or attenuate the effect that these delays have on their passengers. This study has as main objective to predict the occurrence of delays in arrivals at the international airport of Hartsfield-Jackson. A Knowledge Discovery Database (KDD) methodology was followed, and several Data Mining techniques were applied. Historical data of the flight and weather, information of the airplane and propagation of the delay were gathered to train the model. To overcome the problem of unbalanced datasets, we applied different sampling techniques. To predict delays in individual flights we used Decision Trees, Random Forest and Multilayer Perceptron. Finally, each model's performance was evaluated and compared. The best model proved to be the Multilayer Perceptron with 85% of accuracy.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent512562
dc.identifier.doi10.1016/j.procs.2018.10.085
dc.identifier.issn1877-0509
dc.identifier.otherPURE: 33999298
dc.identifier.otherPURE UUID: cc494ef8-a360-4184-ab9f-157465ac743e
dc.identifier.otherScopus: 85061973609
dc.identifier.otherORCID: /0000-0002-4862-8177/work/152174873
dc.identifier.urihttp://hdl.handle.net/10362/125382
dc.identifier.urlhttps://www.scopus.com/pages/publications/85061973609
dc.language.isoeng
dc.peerreviewedyes
dc.subjectAtlanta International Airport
dc.subjectData Mining
dc.subjectFlight Delays
dc.subjectHartsfield-Jackson International Airport
dc.subjectPredictive Analysis
dc.subjectGeneral Computer Science
dc.titlePredictive modellingen
dc.title.subtitleFlight delays and associated factors, Hartsfield-Jackson Atlanta international airporten
dc.typeconference object
degois.publication.firstPage638
degois.publication.lastPage645
degois.publication.titleCENTERIS 2018 - International Conference on ENTERprise Information Systems / ProjMAN 2018 - International Conference on Project MANagement / HCist 2018 - International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018
degois.publication.titleInternational Conference on ENTERprise Information Systems / International Conference on Project MANagement / International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018
degois.publication.volume138
dspace.entity.typePublication
rcaap.rightsopenAccess

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