Identification Method for Key Nodes in En-Route Network

TIAN Wen; FANG Qin; ZHOU Xuefang; SONG Jinjin

doi:10.3969/j.issn.0258-2724.20220532

Volume 60 Issue 1

Jan. 2025

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Journal of Southwest Jiaotong University > 2025 > 60(1): 233-242.

YANG Cheng, LIU Jiaxin, TANG Zenan, ZHAO Baihan, XIN Benjian. Modelling of Velocity Pulse-Like Ground Motions Based on Adaptive Matching Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 726-732. doi: 10.3969/j.issn.0258-2724.20190075

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TIAN Wen, FANG Qin, ZHOU Xuefang, SONG Jinjin. Identification Method for Key Nodes in En-Route Network[J]. Journal of Southwest Jiaotong University, 2025, 60(1): 233-242. doi: 10.3969/j.issn.0258-2724.20220532

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Identification Method for Key Nodes in En-Route Network

doi: 10.3969/j.issn.0258-2724.20220532

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received Date: 28 Jul 2022
Rev Recd Date: 14 Oct 2022

Available Online: 05 Nov 2024

Publish Date: 25 Nov 2022

Abstract

Abstract

Accurate identification of key nodes is of great significance for enhancing network resilience and improving operational capabilities. In order to improve the identification accuracy of key nodes in the en-route network, a comprehensive evaluation method based on the technique for order preference by similarity to an ideal solution (TOPSIS)-grey correlation analysis method and a node classification method for the en-route network were proposed. Firstly, an evaluation index system of key nodes in the en-route network was constructed from three perspectives: complex network characteristics, traffic volume, and vulnerability. Then, the relative entropy was introduced to improve the TOPSIS method, and the importance of en-route waypoints was comprehensively evaluated by combining this method with the grey correlation analysis method. The K-means clustering method was used to effectively divide the levels of en-route waypoints. Finally, key node identification was carried out based on the actual operation data of civil air traffic management. It finds that the results obtained by the constructed evaluation index system of key nodes in the en-route network are more comprehensive than the evaluation results of a single index. The improved TOPSIS-grey correlation analysis is more accurate than the traditional TOPSIS method. The proposed identification method finds that there are 29 key nodes in the typical busy en-route network in Eastern China, which play a key role in the network structure and traffic volume.
- complex networks,
- TOPSIS,
- relative entropy,
- grey relational degree,
- K-means clustering

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References(29)

References

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