## 4 Discussion
### Comprehensive Evaluation of Node Importance Based on PCA-RSR
The results indicate that the majority of nodes exhibit consistent importance values across different network layers, suggesting a stable ranking trend. However, certain nodes demonstrate significant variations in importance between layers, highlighting structural differences in node significance among the three networks. Notably, nodes with exceptionally high PCA values are likely to function as key hubs or critical intersections within the railway station network, emphasizing their crucial role in overall connectivity and operational efficiency. The upper section presents the RSR-based analysis of node rankings, revealing a hierarchical distribution of node importance within the railway station network. Level 1 nodes (high-ranking, 11 nodes in total) are relatively sparse, indicating that only a limited number of nodes exert a dominant influence on network connectivity and operational efficiency. In contrast, Level 2 nodes (moderate-ranking, 46 nodes in total) constitute the majority, suggesting that most nodes contribute moderately to network structure and  
Figure 14: Comprehensive node importance evaluation using the PCA-RSR method across LC, YX, and DC networks. function. Level 3 nodes (low-ranking, 10 nodes in total) are evenly distributed, representing nodes with minimal structural impact. Furthermore, the transition between ranking levels appears gradual rather than abrupt, implying a continuous hierarchy in node importance rather than distinct categorical separations. The comparative analysis of PCA-derived node importance and RSR-based rankings reveals key structural insights into the railway station network. Level 1 nodes identified in the RSR results generally correspond to those with high PCA values, indicating a strong consistency between single-layer PCA evaluations and multi-layer RSR-based rankings. However, certain nodes exhibit notable discrepancies between the two methods, suggesting that while they may hold significant importance within individual network layers, their cross-layer influence is relatively lower. Furthermore, the spatial concentration of high-ranking nodes in specific regions aligns with critical operational zones, such as switching hubs, throat areas, and high-traffic intersections, reinforcing their functional importance. In contrast, the even distribution of low-ranking nodes suggests that the majority of network nodes function in a supporting capacity, contributing marginally to overall network efficiency while maintaining structural stability. Figure 15 shows all nodes annotated on the station's original signaling layout. The most important nodes are concentrated in the upward and downward throat areas; the important nodes, which are the most numerous, are evenly distributed throughout the station yard; and the moderately important nodes are located in low-utilization areas such as safety lines and dead-end tracks. This discussion, based on the PCA-RSR method, provides a quantitative framework for evaluating node importance in railway station networks:  
* Only a small subset of nodes are truly critical, as indicated by Level 1 rankings;
* Most nodes fall within the moderate-importance category, suggesting a balanced but non-uniform distribution of influence across the network;
* The PCA-RSR methodology effectively integrates single-layer and multi-layer evaluations, offering a comprehensive perspective on node significance. These distribution patterns align with general expectations, providing further evidence for the validity of the proposed method. Future research could explore real-time traffic data integration and failure simulations to validate the practical impact of high-ranking nodes on railway operations.