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

Autor(es)

Scarpel, Rodrigo Arnaldo ^[1] ; Pelicioni, Luciele Cristina ^[1]

Afiliação do(s) autor(es)

^[1] ITA, Sao Jose Dos Campos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF AIR TRANSPORT MANAGEMENT; v. 72, p. 1-10, SEP 2018.
• Citações Web of Science: 0

Resumo

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 Sao 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. (AU)

• Processo FAPESP: 13/22416-4 - Mineração de dados aplicada a detecção precoce de atrasos por congestionamento em aeroportos brasileiros
• Beneficiário: Rodrigo Arnaldo Scarpel
• Modalidade de apoio: Auxílio à Pesquisa - Regular