Transactions of Nanjing University of Aeronautics & Astronautics
Homogeneity Analysis of Multi-airport System Based on Airport Attributed Network Representation Learning
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Abstract:
The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation. Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis， and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network. In order to solve this problem， a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed. Firstly， the route network of a multi-airport system with attribute information is constructed. If there are flights between airports， an edge is added between airports， and regional attribute information is added for each airport node. Secondly， the airport attributes and the airport network vector are represented respectively. The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method， and then the airport vector integrating the airport attributes and the airport network characteristics is obtained. By calculating the similarity of the airport vectors， it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system. The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that， compared with other existing algorithms， the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.
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This work was supported by the Natural Science Foundation of Tianjin （No.20JCQNJC00720） and the Fundamental Research Fund for the Central Universities（No.3122021052）.
Clc Number:
TP399
CHINA AERO-SCIENCES
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