ArticleHeliyon2023
Analysis of queue management in theme parks introducing the fast pass system.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 16 citations in OpenAlex.
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prolonged waits reduce tourist satisfaction and have become a major factor in the theme park experience. How to improve tourist satisfaction is one of the main challenges for theme parks, and the key is to develop an appropriate queue management strategy. We develop a theme park queueing model considering the Fast Pass system, derive the optimal pricing strategy under profit maximization, and analyze the Shanghai Disney theme park as an example. The contribution of this paper is to establish a mathematical model of the queuing of theme parks with heterogeneous tourists and incorporate tourist satisfaction into the objective function. The results show that the equilibrium result of the Fast Pass system under profit maximization can optimize both tourist satisfaction (travel utility) and theme park profit, which is more acceptable to theme park operators.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.