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|Title:||A machine learning approach on past ADS-B data to predict planning controller’s actions||Authors:||Pham, Duc-Thinh
Duong, Vu N.
|Keywords:||Engineering::Aeronautical engineering::Air navigation||Issue Date:||2018||Source:||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.||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.||URI:||https://hdl.handle.net/10356/146669||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.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ATMRI Conference Papers|
Updated on Jun 25, 2022
Updated on Jun 25, 2022
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