基于多层网络加权投影的站内关键轨道区段识别研究

The track sections play a critical role in train operations within a railway station. The failure of track sections can significantly reduce transportation efficiency and service quality. This study proposes a methodology to identify critical track sections based on complex network theory. Using signaling layouts and interlocking tables as primary data sources, the study first establishes a multi-layer network model of the station and its weighted projection network. Then, 12 representative node importance metrics including degree centrality, closeness centrality, and PageRank are integrated through an improved Rank-Sum Ratio method to derive comprehensive node significance rankings. The validity of the proposed methodology is verified through mapping the resulting ranking in station yards and analyzing network efficiency changes caused by node removal. A case study of a typical railway station demonstrates that the proposed method successfully establishes a multi-layer network consisting of 7 layers and 72 nodes, and derives a weighted projection network based on the actual operational volume proportions. The identified critical track sections are located at the throat areas of arrival and departure routes, which are consistent with the actual operational bottlenecks. Sequential removal of the top 10, 20, and 33 ranked nodes leads to network efficiency reductions of 37.57%, 51.26%, and 97.28%, respectively, confirming the method's marked effectiveness. Furthermore, the correlation analyses confirm that the proposed method effectively integrates topological, communicative efficiency, and influence features, thereby offering a comprehensive and robust evaluation of node importance in multi-layer complex networks.