Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164437
Title: Air traffic flow prediction using transformer neural networks for flow-centric airspace
Authors: Ma, Chunyao
Alam, Sameer
Cai, Qing
Delahaye, Daniel
Keywords: Engineering::Aeronautical engineering
Engineering::Computer science and engineering
Issue Date: 2022
Source: Ma, C., Alam, S., Cai, Q. & Delahaye, D. (2022). Air traffic flow prediction using transformer neural networks for flow-centric airspace. 12th SESAR Innovation Days (SIDs 2022), 1-9.
Conference: 12th SESAR Innovation Days (SIDs 2022)
Abstract: The air traffic control paradigm is shifting from sector-based operations to cross-border flow-centric approaches to overcome sectors’ geographical limits. Under the flow-centric paradigm, prediction of the traffic flow at major flow intersections, defined as flow coordination points in this paper, may assist controllers in coordinating intersecting traffic flows which is the main challenge for implementing flow-centric concepts. This paper proposes to predict the flow at coordination points through a transformer neural network model. Firstly, the flow coordination points, i.e., the major flow intersections, are identified by hierarchical clustering of flight trajectory intersections whose location and connectivity characterize daily traffic flow patterns as a graph. The number of coordination points is optimized through graph analysis of the daily flow pattern evolution. Secondly, air traffic flow features in the airspace during a period are described as a “paragraph” whose “sentences” consist of the time and callsign sequences of flights transiting through the identified coordination points. Finally, a transformer neural network model is adopted to learn the sequential flow features and predict the future number of flights passing the coordination points. The proposed method is applied to French airspace based on one-month ADS-B data (from Dec 1, 2019, to Dec 31, 2019), including 158,856 flights. Results show that the proposed prediction model can approximate the actual flow values with a coefficient of determination (R2) between 0.909 to 0.99 and a mean absolute percentage error (MAPE) varying from 27.4% to 11.7% with respect to a 15-minute to 2-hour prediction window. The sustainability of the prediction accuracy under an increasing prediction window demonstrates the potential of the proposed model for longer-term flow prediction.
URI: https://www.sesarju.eu/sesarinnovationdays
https://hdl.handle.net/10356/164437
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Air Traffic Management Research Institute 
Rights: © 2022 SESAR 3 Joint Undertaking. All rights reserved. This paper was published in Proceedings of 12th SESAR Innovation Days (SIDs 2022) and is made available with permission of SESAR 3 Joint Undertaking.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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