Investigating influential factors on railway passenger flow utilizing multi-source data fusion and flow space theory: A case study of the Yangtze River Delta megalopolis, China

Abstract

Employing flow space theory and multi-source data, this study examines the spatial network structure and factors influencing railway passenger flow, which is crucial for rail planning in densely populated megalopolises. Focusing on China's Yangtze River Delta (YRD) megalopolis, we utilize social network analysis (SNA) to explore the characteristics of various flow networks and their interactions with the railway passenger flow network. Key findings include: (1) a pronounced polarization effect and core-periphery structure exist in the YRD, notably within industry and railway flow networks; (2) industry and corporation flow significantly contributes to rail passenger flow, with corporation networks in commerce, technical services, and finance showing higher similarity to the railway passenger flow network; (3) there is significant heterogeneity in the correlation between rail passenger flow and other flows within sub-networks formed by connections among nodes of different levels; (4) enhancing railway services at lower-level nodes is essential to mitigate the disparity between population mobility and rail passenger flow and to promote rail transportation equity. This research offers valuable insights for policymakers in developing countries to strategically plan railroad networks in megalopolises.