Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6997
Signal processing and machine learning for air traffic delay prediction
R-Evolucionando el transporte
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
Fecha de publicación
Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
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.
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