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Rail transit (RT) has been favored by passengers because it effectively alleviates the problems of dense population, housing shortages, small natural areas, and serious air pollution in urban centers. In this paper, we propose a framework that combines statistical analysis, fuzzy theory, and the technique for order preference by similarity to an ideal solution (TOPSIS) to evaluate the service quality of RT. First, the passenger perception of service quality is modeled as trapezoidal fuzzy numbers from the fuzzy theory, which solves the uncertainty problem of passenger perception that how factors affect service quality. Next, a case study that evaluates the service quality of the Beijing metro system is proposed using the fuzzy TOPSIS method. During the evaluation process, 8011 surveys are collected from 16 metro lines operated by Beijing Metro Operating Company Ltd. The evaluation results show that transfers, in-vehicle experience, and ticket purchases or recharges are the three factors that passengers find most unsatisfactory about metro travel and that need to be greatly improved in the future construction and management of metros. Furthermore, we analyze the stableness of the fuzzy TOPSIS method by the ranking change of service quality for a line from different comparison sets of metro lines. Finally, we provide suggestions and guidance for the optimization of RT infrastructure and investment.

Improving Service Quality With the Fuzzy TOPSIS Method: A Case Study of the Beijing Rail Transit System