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Proceeding Paper

Using Open Source Data for Landing Time Prediction with Machine Learning Methods †

1
Chair of Econometrics and Statistics esp. Transportation, Dresden University of Technology, 01187 Dresden, Germany
2
Chair of Air Transport Technology and Logistics, Dresden University of Technology, 01069 Dresden, Germany
*
Author to whom correspondence should be addressed.
Presented at the 8th OpenSky Symposium 2020, Online, 12–13 November 2020.
Proceedings 2020, 59(1), 5; https://doi.org/10.3390/proceedings2020059005
Published: 1 December 2020
(This article belongs to the Proceedings of 8th OpenSky Symposium 2020)
Increasing demands on a highly efficient air traffic management system go hand in hand with increasing requirements for predicting the aircraft’s future position. In this context, the airport collaborative decision-making framework provides a standardized approach to improve airport performance by defining operationally important milestones along the aircraft trajectory. In particular, the aircraft landing time is an important milestone, significantly impacting the utilization of limited runway capacities. We compare different machine learning methods to predict the landing time based on broadcast surveillance data of arrival flights at Zurich Airport. Thus, we consider different time horizons (look ahead times) for arrival flights to predict additional sub-milestones for n-hours-out timestamps. The features are extracted from both surveillance data and weather information. Flights are clustered and analyzed using feedforward neural networks and decision tree methods, such as random forests and gradient boosting machines, compared with cross-validation error. The prediction of landing time from entry points with a radius of 45, 100, 150, 200, and 250 nautical miles can attain an MAE and RMSE within 5 min on the test set. As the radius increases, the prediction error will also increase. Our predicted landing times will contribute to appropriate airport performance management.
Keywords: landing time prediction; random forest; feedforward neural network; airport performance; ADS-B; A-CDM landing time prediction; random forest; feedforward neural network; airport performance; ADS-B; A-CDM
MDPI and ACS Style

Chen, G.; Rosenow, J.; Schultz, M.; Okhrin, O. Using Open Source Data for Landing Time Prediction with Machine Learning Methods. Proceedings 2020, 59, 5. https://doi.org/10.3390/proceedings2020059005

AMA Style

Chen G, Rosenow J, Schultz M, Okhrin O. Using Open Source Data for Landing Time Prediction with Machine Learning Methods. Proceedings. 2020; 59(1):5. https://doi.org/10.3390/proceedings2020059005

Chicago/Turabian Style

Chen, Gong, Judith Rosenow, Michael Schultz, and Ostap Okhrin. 2020. "Using Open Source Data for Landing Time Prediction with Machine Learning Methods" Proceedings 59, no. 1: 5. https://doi.org/10.3390/proceedings2020059005

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