Dynamic air traffic flow coordination for flow-centric airspace management

• Main Authors: Ma, Chunyao, Alam, Sameer, Qing, Cai, Delahaye, Daniel
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering::Aeronautical engineering; Air Traffic Flow; Co-ordination
• Online Access: https://hdl.handle.net/10356/168395; https://www.atmseminar.org/upcoming-seminar/
• Institution: Nanyang Technological University

The air traffic control paradigm is shifting from sector-based operations to cross-border flow-centric approaches to overcome sectors' geographical limits. Under this paradigm, effective air traffic flow coordination at flow intersections is crucial for efficiently utilizing available airspace resources and avoiding inefficiencies caused by high demand. This paper proposes a dynamic air traffic flow coordination framework to identify, predict, assess, and coordinate the evolving air traffic flows to enable more efficient flow configuration. Firstly, nominal flow intersections (NFI) are identified through hierarchical clustering of flight trajectory intersections and graph analytics of daily traffic flow patterns. Secondly, spatial-temporal flow features are represented as sequences of flights transiting through the NFIs over time. These features are used to predict the traffic demand at the NFIs during a given future period through a transformer-based neural network. Thirdly, for each NFI, the acceptable flow limit is determined by identifying the phase transition of the normalized flight transition duration from its neighboring NFIs versus the traffic demand. Finally, when the predicted demand exceeds the flow limit, by evaluating the available capacity at different NFIs in the airspace, the flow excess is alternated onto other NFIs to optimize and re-configure the air traffic demand to avoid traffic overload. An experimental study was carried out in French airspace using the proposed framework base on the ADS-B data in December 2019. Results showed that the proposed prediction model approximated the actual flow values with the coefficient of determination ($R^2$) above 0.9 and mean absolute percentage error (MAPE) below 20%. Acceptable flow limit determination showed that for above 68% NFIs, the flight transition duration increases sharply when the demand exceeds a certain level. The flow excess at an NFI whose demand was predicted to exceed its limit was coordinated, and the potential increase in the flight transition duration caused by the flow excess was avoided.