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Improving the service level of metro station is the key to attracting passengers to achieve sustainable development. The satisfaction evaluation approach from the passenger's point of view can intuitively and meticulously reflect the passenger's experience when taking the metro and provide a basis for improving the service. Therefore, this paper proposes an integrated approach for a metro station satisfaction evaluation based on passenger perception. In this approach, the evaluation index system is first dynamically screened and established. Then, the structural equation mixture model (SEMM) is adopted to determine the index weight based on the customer satisfaction index model and the benchmark. The result clouds are generated by the basic algorithm of the cloud model. Finally, the targeted improvement suggestions are proposed based on the evaluation results. Taking the Shanghai Metro People's Square Station as a case, the evaluation approach was verified, which reflected the effectiveness and rationality of the approach. As the results show, this approach can not only obtain the desired level of passenger satisfaction, but also reflect the differences and consensus of passengers' perception, making it practically useful and valuable.

Structural equation mixture models (SEMMs), when applied as a semiparametric model (SPM), can adequately recover potentially nonlinear latent relationships without their specification. This SPM is useful for exploratory analysis when the form of the latent regression is unknown. The purpose of this article is to help users familiar with structural equation models to add SEMM to their toolkit of exploratory analytic options. We describe how the SEMM captures potential nonlinearity between latent variables, and how confidence bands (CBs; point wise and simultaneous) for the recovered latent function are constructed and interpreted. We then illustrate the usefulness of CBs for inference with an empirical example on the effect of emotions on cognitive processing. We also introduce a visualization tool that automatically generates plots of the latent regression and their CBs to promote user accessibility. Finally, we conclude with a discussion on the use of this SPM for exploratory research. [ABSTRACT FROM AUTHOR]

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the service of more flexibly recovering characteristics of the latent aggregate regression function. This article develops and evaluates delta method and parametric bootstrap approaches for obtaining approximate confidence intervals for Bauer's semiparametric approach to modeling latent nonlinear functions. Coverage rates of these approximate point-wise confidence intervals or nonsimultaneous confidence bands are evaluated by Monte Carlo and recommendations for their use are suggested. [ABSTRACT FROM PUBLISHER]