Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152776
Title: A machine learning-based framework for aircraft maneuver detection and classification
Authors: Dang, Phuoc H.
Tran, Phu N.
Alam, Sameer
Duong, Vu N.
Keywords: Engineering::Aeronautical engineering::Air navigation
Issue Date: 2021
Source: Dang, P. H., Tran, P. N., Alam, S. & Duong, V. N. (2021). A machine learning-based framework for aircraft maneuver detection and classification. Fourteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2021), 52-.
Abstract: The increasing availability of historical air traffic data (e.g., Automatic Dependent Surveillance-Broadcast (ADSB) data) has enabled more advanced post-analysis of traffic scenarios, which leads to a better understanding of decision making in air traffic control. Such kind of analysis is often complex and requires a careful design of analysis tools. Advanced machine learning techniques are shown to be very effective in dealing with the complexity of air traffic data analysis. This paper presents a machine learning-based framework to detect aircraft maneuvers in past traffic data and classify the maneuver into three key air traffic maneuvers. Aircraft maneuvers are identified in the ADS-B data using Isolation Forest algorithm, followed by maneuver clustering using Kmeans algorithm. Three time-dependent contextual features are proposed for dynamic traffic scenario representation and shown to be effective for maneuver clustering. Each maneuver cluster is associated with a label provided by Air Traffic Controlle (ATCOs), indicating the reason for such maneuver which took place in the past. Experiments were conducted on the framework using a dataset of 2793 arrival trajectories over 30 days in two Singapore Flight Information Region sectors. The results show that the framework efficiently allows post-analysis of air traffic scenarios, by which one can gain better insights into the decisionmaking patterns of ATCOs in response to various air traffic scenarios.
URI: https://hdl.handle.net/10356/152776
Rights: © 2021 The Author(s). All rights reserved. This paper was published by ATM Seminar in Proceedings of Fourteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2021) and is made available with permission of The Author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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