Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162777
Title: A deep neural network approach for prediction of aircraft top of descent
Authors: Ang, Haojie
Cai, Qing
Alam, Sameer
Keywords: Engineering::Aeronautical engineering
Issue Date: 2022
Source: Ang, H., Cai, Q. & Alam, S. (2022). A deep neural network approach for prediction of aircraft top of descent. International Workshop on ATM/CNS (IWAC 2022), 208-215. https://dx.doi.org/10.57358/iwac.1.0_208
Conference: International Workshop on ATM/CNS (IWAC 2022)
Abstract: An arrival flight starts to transit from the cruise phase to the descent phase at the top of descent (TOD). Pilots get to know the TOD locations via onboard devices, while controllers can estimate the TOD locations with the help of radar surveillance and simple rules. In order to help controllers to get a better situation awareness of the traffic surrounding an aerodrome, it is of great operational importance to get an accurate prediction of the TOD locations for arrival flights. In this paper, we propose to apply deep learning for TOD location prediction for arrival flights. To do so, a TOD-specific feature engineering is suggested and applied to historical flight trajectories. Then the simple yet effective multilayer perceptron neural network model is adopted for TOD prediction. A case study on the arrival flights to Singapore Changi airport with respect to one-month historical trajectory data is carried out. Experiments demonstrate that the adopted deep learning method is effective for TOD location prediction. When compared against several typical machine learning models for regression, the adopted model yields a mean square error of 0.0039, which is smaller than the error achieved by the comparison models. Meanwhile, the adopted deep learning model yields TOD location prediction errors of 0.29 nautical miles (NM) on average with a standard deviation of 46.88 NM.
URI: https://hdl.handle.net/10356/162777
URL: https://www.jstage.jst.go.jp/browse/iwac/list/-char/en
DOI: 10.57358/iwac.1.0_208
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Air Traffic Management Research Institute 
Rights: © 2022 Electronic Navigation Research Institute (ENRI). All rights reserved. This paper was published in the Proceedings of International Workshop on ATM/CNS (IWAC 2022) and is made available with permission of Electronic Navigation Research Institute (ENRI).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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