Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/380
Title: Prediction of aircraft estimated time of arrival using machine learning methods
Authors: Baştürk, Onur
Çetek, Cem Aybek
Keywords: Aircraft arrival time prediction
ETA prediction
Machine learning
Random forests
Deep neural networks
Deep learning
Issue Date: 2021
Publisher: Cambridge Univ Press
Abstract: In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.
URI: https://doi.org/10.1017/aer.2021.13
https://hdl.handle.net/20.500.13087/380
ISSN: 0001-9240
2059-6464
Appears in Collections:Kimya Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender

WEB OF SCIENCETM
Citations

1
checked on Jun 22, 2022

Page view(s)

18
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.