Abstract
Crowding is a key factor in tourists’ experience in theme parks, and mitigating crowding makes parks more competitive. This study examines how to effectively mitigate crowding in theme parks. First, a Markov-based method is developed to predict the spatial-temporal distribution of tourists in the park. Then, a prospective coordination approach based on the tourist distribution prediction is proposed. To evaluate the performance of this approach, an experiment is constructed using an agent-based simulation platform. The results indicate that the proposed method significantly outperforms existing methods. Furthermore, we conduct two experiments and, based on the results, offer several recommendations for crowd management.
Highlights
Theme parks are important tourism products that engage visitors’ imagination and create enjoyable experiences for tourists [1]
This study proposes a Markov-based method to predict the spatial-temporal distribution of tourists in the park and develops a prospective coordination approach based on the predicted distribution of tourists
This study contributes to the literature on tourism management by developing a tourist distribution prediction method that achieves a beneficial trade-off between prediction accuracy and efficiency and a prospective coordination approach to mitigate crowding
Summary
Theme parks are important tourism products that engage visitors’ imagination and create enjoyable experiences for tourists [1]. Crowding management is fundamental to improving tourists’ experience and making theme parks more competitive [11]. We have analyzed the current coordination approaches and identified a range of methods for improving crowding mitigation Most of these methods coordinate tourists’ visit sequences based on the current crowd situation, which leads to oscillating crowds due to the delay between the decision making and effect emergence [15, 16]. This study contributes to the literature on tourism management by developing a tourist distribution prediction method that achieves a beneficial trade-off between prediction accuracy and efficiency and a prospective coordination approach to mitigate crowding. This new approach avoids the crowding oscillation problem and achieves better performance than existing methods.
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