Worldwide forecasting of airport runway usage: A big data approach

R. A. Torres Díaz, P. C. Alvarez Esteban

Air transportation growth is a reality described by different sources (e.g. The World Bank report (2018)), then it is advisable to anticipate airport operations to improve safety. Particularly, runway usage is one of these operations.
However, detailed information of runway usage is inaccessible. This is a drawback for a forecast task given that there is no feasible target to fit a model. Thus, the goal of this work is twofold: 1) To introduce an algorithm that allows to reconstruct the target for any airport. 2) To fit models able to predict airport runway usage, based on weather joined to operation data at the airport.
The quality of the results in the first part report a target reconstruction above the 90\% of accuracy for the available comparisons and the forecasting task was successfully applied massively, and we present the results of a standard and a complex case scenarios, Barajas and Schiphol airports.

Keywords: Big data, machine learning, airport runway usage, ADS-B technology

AM-1 Multivariate Analysis
September 4, 2019  12:00 PM
I3L8. Georgina Blanes building

J. J. Egozcue, V. Pawlowsky-Glahn, J. A. Martín-Fernández, M. I. Ortego

N. Portillo Poblador, M. Zarzo Castelló, F. J. Oliver Villarroya, A. Carrión Garcia

L. Pardo, S. Muñoz, E. M. Castilla González, N. Martin Apaolaza