RT info:eu-repo/semantics/conferenceObject
T1 Signal processing and machine learning for air traffic delay prediction
A1 Tenorio, Víctor M.
A1 García Marqués, Antonio
A1 Cadarso, Luis
K1 Industria aérea
K1 Airline industry
K1 Ingeniería civil
K1 Civil engineering
K1 Transporte
K1 Transportation
AB 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 cannotbe an exception. Delay prediction is paramount in the aerospace industry, since air trafficdelays are responsible for millions of dollars in losses to airlines and passengers, along withnegative impacts on the environment. In this contribution, we leverage recent signalprocessing and ML advances to put forth a processing-and-learning pipeline for theprediction 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 originalinformation. Secondly, we run a descriptive analysis of the data and design new featurestailored to the prediction problem. Thirdly, we implement a scheme to select the mostinformative of those features, contributing to a better generalization performance, andoffering useful insights. Two algorithms are used to that end: one based on random forestsand one employing a sparse logistic regression approach. Finally, once the features areselected, we implement, analyse, and compare several ML architectures (from classicalclassifiers to deep learning) to predict the delay. While the focus of the comparison isprediction accuracy, metrics such as sample and computational complexity are alsodiscussed. 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 designedprocessing/learning pipeline reveals interesting insights and achieves better predictionresults than the state of the art. The results confirm that air traffic delay prediction is achallenging problem, mainly because the delay is extremely airport-dependent and the datais highly unbalanced (i.e., only a small percentage of flights are noticeable delayed), andidentify worth-pursuing future lines of work.
PB Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
SN 978-84-18465-12-3
YR 2021
FD 2021-07
LK http://hdl.handle.net/10259/6997
UL http://hdl.handle.net/10259/6997
LA eng
NO 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
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 26-abr-2024