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In tourism accommodation statistics, accuracy (variance and bias) is fundamental. However, minimizing both the variance and bias of the estimate is a challenge due to the trade-off between the two. In many countries, tourism accommodation statistics, which popularly developed with the unbiased linear estimator, are less accurate. This study recommends an integration of bootstrap and regression to overcome the challenge. An unbiased linear estimator is used as a benchmark to investigate the integration method's effectiveness. With realistic accommodation data in Japan, we found that the integration method yields more than two times more accurate results than the unbiased linear estimator. The integration method contributes to an innovation in accommodation statistics in tourism.

This study aims to explore accessibility indicators influencing the interactions between users, transport service providers (TSPs), and a platform operator, generating a conceptual framework for modeling these interactions under Mobility as a Service context. A systematic literature review was conducted to identify all studies focusing on indicators and modeling the interactions. There are limitations in integrating psychological indicators and dynamic pricing into the existing models. Moreover, there are gaps in considering monthly service packages, the efficiency of transport systems, and the perspectives of the TSPs for modeling the demand–supply interactions. The study ends with conclusions, discussions, and directions for further studies.