| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] ITA, Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | JOURNAL OF AIR TRANSPORT MANAGEMENT; v. 72, p. 1-10, SEP 2018. |
| Web of Science Citations: | 0 |
| 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 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) | |
| FAPESP's process: | 13/22416-4 - Data mining on early detection of congestion delays for Brazilian airports |
| Grantee: | Rodrigo Arnaldo Scarpel |
| Support Opportunities: | Regular Research Grants |