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Prediction of departure flight delays through the use of predictive tools based on machine learning/deep learning algorithms

Published online by Cambridge University Press:  19 May 2023

J.G. Muros Anguita Open the ORCID record for J.G. Muros Anguita [Opens in a new window]  and
O. Díaz Olariaga Open the ORCID record for O. Díaz Olariaga [Opens in a new window]
J.G. Muros Anguita
Affiliation:
Escuela Internacional de Postgrado, Universidad de Granada, Granada, Spain
O. Díaz Olariaga*
Affiliation:
Facultad de Ingeniería Civil, Universidad Santo Tomás, Bogota, Colombia
*
Corresponding author: O. Díaz Olariaga; Email: oscardiazolariaga@usta.edu.co
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Abstract

The objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. Since the novel contribution of this work is, first, to make the comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented and, second, to determine the coefficients of the importance of the features or flight attributes, using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. From the results obtained, it is worth mentioning that the model that presents the best performance is the ensemble or combinatorial method of random forest regressor models, with an acceptable prediction range (measured with the root-mean-square-error).

Keywords

Flight delayAir trafficAir transportAirportMachine learningDeep learning
Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1319 , January 2024 , pp. 111 - 133
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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