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oai:riubu.ubu.es:10259/69972024-05-20T09:43:09Zcom_10259.4_104com_10259_2604col_10259_6848

Tenorio, Víctor M. García Marqués, Antonio 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 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. The authors want to thank Sergio Rozada for his help in designing and coding the algorithms used in this publication and the Spanish “Agencia Estatal de Investigación” for the support via Project Grants TRA2016-76914-C3-3-P, PID2019-105032GB-I00, and CAS19/00036 and CAM grant PEJ-2020-AI/TIC-18964. application/pdf eng Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional R-Evolucionando el transporte http://hdl.handle.net/10259/6490 https://doi.org/10.36443/9788418465123 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TRA2016-76914-C3-3-P/ES/ROBUSTEZ, EFICIENCIA Y RECUPERACION DE SISTEMAS DE TRANSPORTE PUBLICO info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105032GB-I00/ES/Procesamiento de señal para datos definidos sobre grafos: Aprovechando la estructura en dominios irregulares. SPGraph info:eu-repo/grantAgreement/MICIU/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/CAS19%2F00036 info:eu-repo/grantAgreement/CAM//PEJ-2020-AI%2FTIC-18964 Industria aérea Airline industry Ingeniería civil Transportes Civil engineering Transportation Signal processing and machine learning for air traffic delay prediction info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/openAccess 2603 2614