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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
*
[email protected]
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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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References

REFERENCES

Eurocontrol. Challenges of Growth 2013 Task 4: European Air Traffic in 2035, 2013.Google Scholar
Federal Aviation Administration. FAA Aerospace Forecast: Fiscal Years 2018-2038, 2018.Google Scholar
Ayhan, S., Costas, P. and Samet, H. Predicting estimated time of arrival for commercial flights, Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining (KDD 2018), 2018.10.1145/3219819.3219874CrossRefGoogle Scholar
Kern, C.S., Medeiros, I.P. and Yoneyama, T. Data-driven aircraft estimated time of arrival prediction, Systems Conference (SysCon), 2015.Google Scholar
Glina, Y., Jordan, R. and Ishutkina, M. A tree-based ensemble method for prediction and uncertainty quantification of aircraft landing times, Proceedings 10th Conference on Artificial and Computational Intelligence and its Applications to Environmental Sciences, 2012.Google Scholar
Slattery, R. and Zhao, Y. Trajectory synthesis for air traffic automation. J Guidance Cont Dyn, 1997, 20, (2).Google Scholar
Mondoloni, S. Aircraft Trajectory Prediction Errors: Including a Summary of Error Sources and Data, FAA/Eurocontrol Action Plan 16, 2006.Google Scholar
Murphy, J.R., Clayton, J., Reisman, R.J. and Wright, R. Physics-based and parametric trajectory prediction performance comparison for traffic flow management, AIAA Guidance, Navigation, and Control Conference and Exhibition, 2003.10.2514/6.2003-5629CrossRefGoogle Scholar
Breiman, L. Random forests. Mach Learn, 2001, 45, (1), pp 5–32.10.1023/A:1010933404324CrossRefGoogle Scholar
Takacs, G. Predicting flight arrival times with a multistage model, IEEE International Conference on Big Data, 2014.10.1109/BigData.2014.7004435CrossRefGoogle Scholar
Wang, Z., Liang, M. and Delahaye, D. A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area. Transport Res Part C Emerging Tech, 2018, 95, pp 280–29410.1016/j.trc.2018.07.019CrossRefGoogle Scholar
Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A. Classification and Regression Trees, 1984.Google Scholar
Dietterich, T. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization, Mach Learn, 2000, 40, pp 139–157.10.1023/A:1007607513941CrossRefGoogle Scholar
Breiman, L. Bagging predictors, Mach Learn, 1996, 26, pp 123–140.10.1007/BF00058655CrossRefGoogle Scholar
Ghatak, A. Machine Learning with R, Springer, Singapore, 2017.10.1007/978-981-10-6808-9CrossRefGoogle Scholar
Alpaydin, E. Introduction to Machine Learning, The MIT Press, Massachusetts, 2010.Google Scholar
Dechter, R. Learning while searching in constraint-satisfaction-problems, Proceedings of AAAI-86, 1986, pp 178–183.Google Scholar
Aizenberg, I.N., Aizenberg, N.N. and Vandewalle, J. Multiple-Valued Threshold Logic and Multi-Valued Neurons, Multi-Valued and Universal Binary Neurons. Springer, 2000, pp 25–80.10.1007/978-1-4757-3115-6_2CrossRefGoogle Scholar
Lecunn, Y., Bengio, Y. and Hinton, G. Deep learning. Nature, 2015, 521, pp 436–444.10.1038/nature14539CrossRefGoogle Scholar
H2O.ai, https://www.h2o.ai/ (accessed 13 October 2020)Google Scholar