 A data analytics approach for anticipating congested days at the São Paulo International AirportRodrigo Arnaldo Scarpel and Luciele Cristina Pelicioni Journal of Air Transport Management, 2018, vol. 72, issue C, 1-10 Abstract: Worldwide, most of the airports are not able to operate as planned due to delay problems. Since a high proportion of flights are affected by delays in congested days, for developing effective strategies to reduce flight delays and support response planning, a critical issue is how to anticipate the occurrence of congested days. The goal of this work is to employ a data analytics approach to build an early warning model to anticipate the occurrence of such days at the São Paulo International Airport. Therefore, a Mixture-of-experts model (MEM) was used to combine modelling approaches that rely on different assumptions regarding the data available to process. Such approach allows generating a more flexible and powerful model that makes good promises of improvement in the prediction accuracy. The built MEM is composed of a Classification and Regression Tree, a multiple linear regression and a seasonal ARIMA and it was used to generate predictions for three periods ahead. The accuracy of the early warning model was considered satisfactory for anticipating congested days. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:72:y:2018:i:c:p:1-10 DOI: 10.1016/j.jairtraman.2018.07.002 Access Statistics for this article Journal of Air Transport Management is currently edited by Anne Graham More articles in Journal of Air Transport Management from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.