Please use this identifier to cite or link to this item:
Title: An air traffic controller action extraction-prediction model using machine learning approach
Authors: Pham, Duc-Thinh
Alam, Sameer
Duong, Vu
Keywords: Engineering::Aeronautical engineering
Issue Date: 2020
Source: Pham, D.-T., Alam, S., & Duong, V. (2020). An air traffic controller action extraction-prediction model using machine learning approach. Complexity, 2020, 1659103-. doi:10.1155/2020/1659103
Journal: Complexity 
Abstract: In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller (D-side) and executive controller (R-side). 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 safety of flights in their sector. A better understanding and predictability of D-side 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 six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy (99%). Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions (altitude, speed, and course change) for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach.
ISSN: 1076-2787
DOI: 10.1155/2020/1659103
Rights: © 2020 Duc-Thinh Pham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles

Files in This Item:
File Description SizeFormat 
1659103(1).pdf4.89 MBAdobe PDFThumbnail

Page view(s)

Updated on May 6, 2021


Updated on May 6, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.