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Prediction of aircraft estimated time of arrival using machine learning methods

Published online by Cambridge University Press:  17 March 2021

O. Basturk Open the ORCID record for O. Basturk [Opens in a new window]  and
C. Cetek Open the ORCID record for C. Cetek [Opens in a new window]
O. Basturk*
Affiliation:
Republic of Turkey Ministry of Justice Directorate General for Information Technologies AnkaraTurkey
C. Cetek
Affiliation:
Eskisehir Technical UniversityEskisehirTurkey
*
onur.basturk@adalet.gov.tr
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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.

Keywords

Aircraft arrival time predictionETA predictionMachine learningRandom forestsDeep neural networksDeep learning
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1289 , July 2021 , pp. 1245 - 1259
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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