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https://hdl.handle.net/10356/146669
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DC Field | Value | Language |
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dc.contributor.author | Pham, Duc-Thinh | en_US |
dc.contributor.author | Alam, Sameer | en_US |
dc.contributor.author | Su, Yi-Lin | en_US |
dc.contributor.author | Duong, Vu N. | en_US |
dc.date.accessioned | 2021-03-04T07:37:44Z | - |
dc.date.available | 2021-03-04T07:37:44Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Pham, D., Alam, S., Su, Y. & Duong, V. N. (2018). A machine learning approach on past ADS-B data to predict planning controller’s actions. 8th International Conference on Research in Air Transportation (ICRAT ’18), 80. | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/146669 | - |
dc.description.abstract | En-route airspace is one of the most congested airspaces, as it is mainly used in the cruise phase of the flight. The en-route sector is usually managed by a team of two air traffic controllers: planning controller (D-side) and executive controller (R-side). The D-side controller is responsible for processing flight plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring the safety of flights in their sector. A better understanding and predictability of Dside controller actions, for a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on one month of ADS-B data over an en-route sector, and its generalization performance was assessed, using cross-fold validation, in the same sectors. Results indicate that the model for vertical maneuver actions provides the highest prediction accuracy (99.7%). Besides, the model for speed change and heading change action provides predictability accuracy of 88.7% and 72.4% respectively. The model to predict the set of all the actions (altitude, speed, and heading change) for each flight achieves an accuracy of 0.68 implying for 68% of flights, D-Side Controller’s can be predicted for all the actions from trajectory information at sector entry position. | en_US |
dc.description.sponsorship | Civil Aviation Authority of Singapore (CAAS) | en_US |
dc.language.iso | en | en_US |
dc.rights | © 2018 ICRAT. All rights reserved. This paper was published in International Conference for Research in Air Transportation (ICRAT) and is made available with permission of ICRAT. | en_US |
dc.subject | Engineering::Aeronautical engineering::Air navigation | en_US |
dc.title | A machine learning approach on past ADS-B data to predict planning controller’s actions | en_US |
dc.type | Conference Paper | en |
dc.contributor.school | School of Mechanical and Aerospace Engineering | en_US |
dc.contributor.conference | 8th International Conference on Research in Air Transportation (ICRAT ’18) | en_US |
dc.contributor.research | Air Traffic Management Research Institute | en_US |
dc.description.version | Accepted version | en_US |
dc.identifier.volume | 80 | en_US |
dc.description.acknowledgement | This research has been partially supported under Air Traffic Management Research Institute (NTU-CAAS) Grant No. M4061216.05K | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | ATMRI Conference Papers |
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File | Description | Size | Format | |
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ICRAT_2018_paper_102.pdf | 9.04 MB | Adobe PDF | View/Open |
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