###### Abstract
Degree Centrality (DC) [10], Betweenness Centrality (BC) [11], and Closeness Centrality (CC) [12] are some of these classical centrality methods. [PERSON] et al. [10] proposed a node occupancy rate index based on node degree to evaluate the utilization level of rail transit nodes, identifying key nodes in six rail transit networks, namely Beijing, Hong Kong, Tokyo, Paris, London, and New York. [PERSON] et al. [11] selected four indicators: DC, BC, CC, and Eigenvector Centrality (EC). They used principal component analysis to determine the weights of each indicator and then used factor analysis to identify key nodes in Shenzhen's rail transit. They also compared the topological complexity differences between the Space L model and the Space P model. [PERSON] et al. [9] proposed a new index based on betweenness centrality, closeness centrality, and weighted node degree to identify the importance of nodes in RTN. Meanwhile, researchers in [13] proposed community-aware centralities that classify nodes by taking into account local and global information based on intra-community and inter-community ties. A modularity vitality centrality measure was proposed in [14], which generalizes overlapping communities based on the community identification method. In [15], an incremental detection method is developed to explore critical nodes in the neighborhood and simulate the dynamics through snapshots. Two round-trip centralities are proposed to reinforce dense networks in [16; 17]. A number of approaches to dealing with static social networks have been proposed to determine the best solution for partitioning the network into k communities [18]. A key node is generally a topological location that has a significant influence on the network. [19]. The influence maximization problem was investigated in static social networks [20]. Literature [21] confirms that weak ties play a crucial role in the propagation process and that nodes with high clustering coefficients are generally not critical nodes. Literature [22] proposes a TI-SC algorithm to identify influential nodes by exploring the relationship between key nodes and the scoring ability of other nodes. The structure of transportation networks significantly influences their functions and performance. With in-depth research, scholars have conducted extensive studies on railway networks using complex theory methods. For example, [PERSON] et al. [23] considered both node network topology features and transportation service attributes, using entropy-TOPSIS to rank transportation network nodes and constructing a clustering node risk identification model to classify transportation risk nodes, though they didn't consider the actual spatial distance of nodes when building their network. [PERSON] et al. [24] proposeda node importance evaluation method based on the TOPSIS method by combining the topology, industry characteristics, and directionality of the air cargo network. [PERSON] et al. [25] first proposed a TOPSIS-based method to rank critical nodes in complex networks that simultaneously identifies alternatives that satisfy the worst aspect with the farthest distance and the optimal aspect with the shortest distance. [PERSON] et al. [26] proposed an improved weighted TOPSIS algorithm with the weights of evaluation indexes determined by the gray correlation analysis, which can identify the most important nodes in complex networks. [PERSON] et al. [27] evaluated the propagation ability of nodes by embedding relative entropy in the TOPSIS method and applied it to several practical complex networks. [PERSON] et al. [28] developed a comprehensive measure to identify important nodes in complex networks that combines the VIKOR method and the entropy weight method. In summary, there has been a great deal of research on the importance of nodes in transport networks, but there is less research on RVCTN. Most node importance evaluation studies qualitatively analyze the evaluation results, lacking verification of method effectiveness, highlighting the need for a more scientific evaluation method.