Air traffic flow prediction using transformer neural networks for flow-centric airspace
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,...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://www.sesarju.eu/sesarinnovationdays https://hdl.handle.net/10356/164437 |
| _version_ | 1811692658454691840 |
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| author | Ma, Chunyao Alam, Sameer Cai, Qing Delahaye, Daniel |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Ma, Chunyao Alam, Sameer Cai, Qing Delahaye, Daniel |
| author_sort | Ma, Chunyao |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:39:17Z |
| format | Conference Paper |
| id | ntu-10356/164437 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:39:17Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1644372023-02-25T15:30:25Z Air traffic flow prediction using transformer neural networks for flow-centric airspace Ma, Chunyao Alam, Sameer Cai, Qing Delahaye, Daniel School of Mechanical and Aerospace Engineering 12th SESAR Innovation Days (SIDs 2022) Air Traffic Management Research Institute Engineering::Aeronautical engineering Engineering::Computer science and engineering Airports 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. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and the Civil Aviation Authority of Singapore. 2023-02-20T02:49:40Z 2023-02-20T02:49:40Z 2022 Conference Paper 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. https://www.sesarju.eu/sesarinnovationdays https://hdl.handle.net/10356/164437 1 9 en © 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. application/pdf |
| spellingShingle | Engineering::Aeronautical engineering Engineering::Computer science and engineering Airports Ma, Chunyao Alam, Sameer Cai, Qing Delahaye, Daniel Air traffic flow prediction using transformer neural networks for flow-centric airspace |
| title | Air traffic flow prediction using transformer neural networks for flow-centric airspace |
| title_full | Air traffic flow prediction using transformer neural networks for flow-centric airspace |
| title_fullStr | Air traffic flow prediction using transformer neural networks for flow-centric airspace |
| title_full_unstemmed | Air traffic flow prediction using transformer neural networks for flow-centric airspace |
| title_short | Air traffic flow prediction using transformer neural networks for flow-centric airspace |
| title_sort | air traffic flow prediction using transformer neural networks for flow centric airspace |
| topic | Engineering::Aeronautical engineering Engineering::Computer science and engineering Airports |
| url | https://www.sesarju.eu/sesarinnovationdays https://hdl.handle.net/10356/164437 |
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