Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours

Abstract

In the context of spatio-temporal big data, the complexity of urban metro network is highlighted. For the safe operation and resilient management of urban rail transit networks, it is advantageous to correctly comprehend the complex topological dynamics characteristics of the weighted metro network based on the massive mobility of passenger flow. In this study, the weighted Shenzhen Metro networks (WSZMNs) in the morning and evening rush hours were modeled based on Space L model and spatio-temporal big data of cross-sectional passenger flow. Combined with six complex indicators, the topological complexity of WSZMNs in two periods was compared based on quantitative and geographical distributions. Based on the multi-attribute decision making method, the weighted comprehensive importance of all nodes in morning and evening rush hours was also quantitatively evaluated and geographically visualized. Results indicate that the WSZMN exhibited some geographical heterogeneity, and the complexity of WSZMN in the morning rush hours was more prominent than in the evening rush hours. Additionally, for the network’s critical stations, essential locations, and significant periods, there was often large-scale and massive mobility of passenger flow. The metro operation management department should strengthen the targeted passenger flow control to improve the safety and resilience of Shenzhen Metro network. The relevant research findings help us get a better understanding of the complexity of metro network system under the massive passenger flow mobility in the rush hours. This study can provide specific theoretical and practical references for the urban smart metro operation department to manage the massive mobility better.