Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151957
Title: A machine learning approach for the prediction of top of descent
Authors: Tan, Benjamin Zhi Yong
Alam, Sameer
Ma, Chunyao
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Tan, B. Z. Y., Alam, S. & Ma, C. (2021). A machine learning approach for the prediction of top of descent. 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). https://dx.doi.org/10.1109/DASC52595.2021.9594470
Abstract: Under the light of increasing air traffic congestion and reduced environmental impacts, the growing need for tailored approaches has arisen. To coordinate tailored approaches, accurate predictions of descent trajectories must be available. The ability of ground based automation for such endeavours however must be improved. Specifically in the ability to predict where descents begin, also referred to as the Top of descent (TOD). This paper attempts to provide a machine learning approach to TOD location prediction, considering the operational capabilities of modern-day air traffic management by working within the confines of parameters available to controllers and considerations of stakeholders. To identify the common TOD clusters and the resultant average TOD locations representing the controllers macroscopic perspective, an unsupervised learning approach is utilised. Specifically, an ensemble method of DBSCAN-K-means is used. This was compared to the more microscopic pilot controllers perspective and generic decision-making formulas to determine the proximity to expected TOD. Of which the microscopic option has proven to be more reliable in representing the individual Flight Management System (FMS) recommendations or pilots’ decisions on aircraft. In the prediction of TOD location, operationally available factors such as proximity to expected TOD, current flight level, approach heading, ground speed, fleet and aircraft type are used to train a decision tree model. A Singapore flight information region (FIR) case study is conducted to illustrate the methodology. Results of the methodology employed on the inbound A380 fleet consisting a total of 79 flights, yielded a maximum prediction range of 31NM range from actual TOD occurrence. This paper has demonstrates the possibility of using a machine learning approach to predict TOD of a flight considering both prior cruise characteristics and operational considerations of pilots.
URI: https://hdl.handle.net/10356/151957
DOI: 10.1109/DASC52595.2021.9594470
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/DASC52595.2021.9594470.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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