Nowadays, Rail transit facilities are moving towards automation and modernization. Facing huge passenger flow, it’s significant to use limited space of subway station to improve the access efficiency of automatic gate machine. Based on the data of the entry and exit stations of Xi'an Metro Line 2, this paper uses the two-step clustering method to classify the site types. The Optimal scale regression model is adopted to explore the significant determinants influence the unbalanced utilization of automatic ticket gates. The result indicates that the positional relationship between gates and passenger walking direction, passenger flow intensity, the configuration quantity of gates, and the type of subway station have significant effects on the unbalanced utilization of automatic gates, and indicates that the configuration quantity of gates has a hierarchical negative impact on the capacity of automatic gates. Furthermore, this paper provides some suggestions on configuration of gates to improve automatic gates capacity, and also provides empirical evidence and reference values for other subway stations. Keywords: Urban rail transit; Automatic gate machine; Capacity reduction; Optimal scale regression

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