Correlation Analysis Between Rail Network Nodal Attributes and Passenger Staying Rate: A Case Study of China’s Yangtze River Delta Region

Abstract

This study investigates the relationship between railway network characteristics and passenger behaviour in station areas across the Yangtze River Delta, China. Using 49 railway stations as case studies, the research develops a multilayer L-space railway network model based on national railway timetable data and combines it with location-based service (LBS) trajectory data covering 72 million passenger trips. The study evaluates station node attributes through Temporal Closeness Centrality and an Economically-Weighted PageRank indicator that integrates network connectivity with city-level GDP. Correlation analysis demonstrates that Economically-Weighted PageRank within the integrated railway network is strongly associated with passenger staying rates and with the proportions of business, commuter, and leisure travellers, while temporal accessibility alone shows limited explanatory power. Based on these findings, the paper proposes a "Node-Passenger" analytical framework as an extension of the traditional node-place model. The results indicate that stations with higher strategic importance in the integrated rail network attract more time-sensitive passenger groups and function more successfully as destinations rather than merely transfer hubs. The framework provides a new perspective for railway station area planning and suggests that future research should integrate built-environment characteristics into a comprehensive "Node-Passenger-Place" model for transport-oriented urban development.