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Nowadays, urban rail transit network (URTN) has become an indispensable and important transportation infrastructure in rapidly developing cities, then how to effectively guarantee the normal operation of urban rail transit network and effectively prevent the impact of risky incidents on the performance of URTN to be increasingly prominent. This paper identifies critical URTN stations and links through a quantitative study of urban metro network vulnerability modelling analysis, taking into account urban bus connections. The paper also uses big data acquisition and analysis processing techniques to obtain more realistic and accurate traffic analysis data for the passenger flow characteristics of the urban metro network. Afterwards, the vulnerability of the URTN network is analysed through the accessibility measurement of the change in travel cost when different stations (links) are deliberately attacked under alternative transportation modes, so as to identify the critical stations and links in the URTN network. This paper presents an analysis based on the Shenzhen metro network as a case study. The results show that the consequences of disrupting different stations on URTN accessibility clearly differ when stations with different passenger flow characteristics are processed in the context of big data technology, and that some stations that are not disrupted are found to become more vulnerable under the failure of surrounding stations. The proposed approach provides a reliable metric and methodology for the identification of critical stations and links in urban rail transport networks, and their impact on decision-making under disruption.
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