Transactions of Nanjing University of Aeronautics & Astronautics
Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management
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Abstract:
Air traffic controllers face challenging initiatives due to uncertainty in air traffic. One way to support their initiatives is to identify similar operation scenes. Based on the operation characteristics of typical busy area control airspace, an complexity measurement indicator system is established. We find that operation in area sector is characterized by aggregation and continuity, and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components. Using principle components, discrete features and time series features are constructed. Based on Gaussian kernel function, Euclidean distance and dynamic time warping (DTW) are used to measure the similarity of the features. Then the matrices of similarity are input in Spectral Clustering. The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system. Finally, actual vertical operation decisions for area sector and results of identification are compared, which are visualized by metric multidimensional scaling (MDS) plots. We find that identification results can well reflect the operation at peak hours, but controllers make different decisions under the similar conditions before dawn. The compliance rate of busy operation mode and division decisions at peak hours is 96.7%. The results also show subjectivity of actual operation and objectivity of identification. In most scenes, we observe that similar air traffic activities provide regularity for initiatives, validating the potential of this approach for initiatives and other artificial intelligence support.
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Clc Number:
V355
CHINA AERO-SCIENCES
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