The connectivity of the rail transit stations is an effective way to evaluate the economic value of the station. In order to explore the influence of the local structure in the complex system of urban rail transit, this paper constructs complex network models based on the Beijing rail transit real network by using the space P and space L methods. The topological structure and global characteristics of the line and station networks are analyzed, and all motifs from 3 to 8 nodes of both networks are obtained using the motif detection algorithm. A subgraph decomposition algorithm for complex network motifs is designed based on five typical subgraphs. The results show that both networks of Beijing rail transit have different typical numbers and distributions of subgraphs, with Y-shaped subgraphs and line subgraphs being the most common for high-node and low-node motifs, respectively. This research proposes a way to assess the connectivity of the rail transit system and has significant reference value for optimizing network functions in the design and planning of rail transit networks. The findings also contribute to the ongoing discussions on network reliability and resilience, as the subgraphs identified in this study could potentially have implications for the network’s performance under different scenarios.

Peer Reviewed