Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146669
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dc.contributor.authorPham, Duc-Thinhen_US
dc.contributor.authorAlam, Sameeren_US
dc.contributor.authorSu, Yi-Linen_US
dc.contributor.authorDuong, Vu N.en_US
dc.date.accessioned2021-03-04T07:37:44Z-
dc.date.available2021-03-04T07:37:44Z-
dc.date.issued2018-
dc.identifier.citationPham, 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.urihttps://hdl.handle.net/10356/146669-
dc.description.abstractEn-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.sponsorshipCivil Aviation Authority of Singapore (CAAS)en_US
dc.language.isoenen_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.subjectEngineering::Aeronautical engineering::Air navigationen_US
dc.titleA machine learning approach on past ADS-B data to predict planning controller’s actionsen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.conference8th International Conference on Research in Air Transportation (ICRAT ’18)en_US
dc.contributor.researchAir Traffic Management Research Instituteen_US
dc.description.versionAccepted versionen_US
dc.identifier.volume80en_US
dc.description.acknowledgementThis research has been partially supported under Air Traffic Management Research Institute (NTU-CAAS) Grant No. M4061216.05Ken_US
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