A data analytics approach for anticipating congested days at the São Paulo International Airport

• Published in: Journal of air transport management 2018-09, Vol.72, p.1-10
• Main Authors: Arnaldo Scarpel, Rodrigo, Pelicioni, Luciele Cristina
• Format: Article
• Language: English
• ISSN: 0969-6997; 1873-2089
• DOI: 10.1016/j.jairtraman.2018.07.002

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. •Anticipate congested days occurrence in order to support response planning.•Combine modelling approaches that rely on different assumptions.•In order to promote a cooperative learning scheme between the individual experts.•Mixture-of-experts allows to generate a more flexible and powerful model.