2026-06-21T17:49:01Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/69972024-05-20T09:43:09Zcom_10259.4_104com_10259_2604col_10259_6848
Repositorio Institucional de la Universidad de Burgos
author
Tenorio, Víctor M.
author
García Marqués, Antonio
author
Cadarso, Luis
2022-09-21T12:41:05Z
2022-09-21T12:41:05Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/6997
10.36443/10259/6997
As data quality and quantity increase, the prediction of future events using machine learning
(ML) techniques across engineering disciplines grows by the day. Air transportation cannot
be an exception. Delay prediction is paramount in the aerospace industry, since air traffic
delays are responsible for millions of dollars in losses to airlines and passengers, along with
negative impacts on the environment. In this contribution, we leverage recent signal
processing and ML advances to put forth a processing-and-learning pipeline for the
prediction of air traffic delays. The proposed approach is executed in several steps. Firstly,
we apply signal processing and data science techniques to filter and denoise the original
information. Secondly, we run a descriptive analysis of the data and design new features
tailored to the prediction problem. Thirdly, we implement a scheme to select the most
informative of those features, contributing to a better generalization performance, and
offering useful insights. Two algorithms are used to that end: one based on random forests
and one employing a sparse logistic regression approach. Finally, once the features are
selected, we implement, analyse, and compare several ML architectures (from classical
classifiers to deep learning) to predict the delay. While the focus of the comparison is
prediction accuracy, metrics such as sample and computational complexity are also
discussed. Numerical experiments are drawn from the US domestic market for the year 2018,
when more than 7 million flights between 358 airports were flown. The designed
processing/learning pipeline reveals interesting insights and achieves better prediction
results than the state of the art. The results confirm that air traffic delay prediction is a
challenging problem, mainly because the delay is extremely airport-dependent and the data
is highly unbalanced (i.e., only a small percentage of flights are noticeable delayed), and
identify worth-pursuing future lines of work.
eng
Industria aérea
Airline industry
Signal processing and machine learning for air traffic delay prediction
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/6997/1/Tenorio_CIT2021_2603-2614.pdf
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Tenorio_CIT2021_2603-2614.pdf