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Promet - Traffic&Transportation, Vol. 37 No. 4, 2025.

Original scientific paper

https://doi.org/10.7307/ptt.v37i4.709

Subjective Air Traffic Complexity Analysis Based on Weak Supervised Learning

Weining ZHANG ; College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China
Weijun PAN ; Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan, China
Changqi YANG ; College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China
Xinping ZHU ; College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China
Jianan YIN orcid id orcid.org/0000-0002-1469-8759 ; Nanjing University of Aeronautics and Astronautics, Nanjing, China
Jinghan DU ; College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China *

* Corresponding author.

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Abstract

Controller subjective evaluation is one of the most important ways to assess air traffic complexity. However, the inconsistency of human experts has a negative impact on the inference of complexity analysis models. To solve this problem, this paper proposes to construct a weakly supervised air traffic complexity dataset using highly reliable traffic situation similarity as labelling information. On this basis, a distance metric learning model is trained to generate a distance metric matrix that satisfies the similarity relationship. Finally, the K-means algorithm is combined to realise preferred complexity situation level classification and evolution analysis. Taking the actual operating data of a mid-southern area sector of China as an example, the effectiveness of the proposed method is verified. Experimental results show that the aircraft density, aircraft ground speed variance, heading disorder, convergence speed and horizontal conflict have a greater impact on the complexity situation. Compared with the K-means algorithm based on Euclidean distance, metric learning improves the optimal silhouette coefficient and Davidson-Boldin index by 31.80% and 12.97%, respectively. In addition, it is confirmed that the situation evolution is driven by one or two key influencing factors.

Keywords

air traffic complexity; subjective similarity; weaklysupervised; distance metriclearning; cluster analysis

Hrčak ID:

333705

URI

https://hrcak.srce.hr/333705

Publication date:

14.7.2025.

Visits: 153 *