Recognizing the Critical Stations in Urban Rail Networks: An Analysis Method Based on the Smart-Card Data

The urban rail network is a confined and independent space, the interior stations and tracks of which are highly correlated. If one or some specific stations were disrupted, it would impact the whole network gravely. Therefore, it is meaningful to recognize these critical stations and put more human and material resources take care of them. Based on the real-world smart-card data, we propose an analysis method including four indicators to find the critical stations in the urban rail networks. A multi-agent based simulation and min-max normalization are developed to obtain the final importance degree for each station. A case study based on the real-world passenger volume data from Beijing Metro network is conducted to illustrate the validity and availability of the developed method.