JDR Vol.19 No.2 pp. 303-315
doi: 10.20965/jdr.2024.p0303


Ticketing and Crowd Management System for Attraction Facilities: An Aquarium Case Study

Yoshiaki Nakagawa, Yukari Abe, and Masami Isobe

Goodfellows Co., Ltd.
7F Mitaka Takagi Bldg., 1-15-5 Nakacho, Musashino, Tokyo 180-0006, Japan

Corresponding author

October 5, 2023
February 15, 2024
April 1, 2024
tourist attractions, variable pricing, crowd management, revenue management, forecasting

In tourist facilities, managing ticket sales can reduce congestion imbalances. This study reports on the results of a pilot experiment conducted at Kaiyukan in Osaka, one of the largest aquariums in Japan. The experiment utilized pre-sale ticket data for controlling admission time intervals, smoothing the number of admissions through dynamic pricing, and predicting visitor numbers. The study reviewed the effectiveness of each of these approaches to alleviate congestion inside Kaiyukan. We then report on a method to predict the number of visitors from the pre-sale conditions of Kaiyukan admission tickets. It was found that setting entry times to 15-minute intervals was most operationally advantageous for the Kaiyukan. Moreover, the behavioral effects induced by variable pricing were more effective when prices were changed based on time slots rather than on a daily basis. Compared to the Holt–Winters’ method, we were able to maintain stable prediction accuracy even during consecutive holidays and long school vacation seasons.

Cite this article as:
Y. Nakagawa, Y. Abe, and M. Isobe, “Ticketing and Crowd Management System for Attraction Facilities: An Aquarium Case Study,” J. Disaster Res., Vol.19 No.2, pp. 303-315, 2024.
Data files:
  1. [1] Kaiyukan, Kaiyukan website. [Accessed September 1, 2023]
  2. [2] “Hakubutsukan Ho,” (Museum Act) (in Japanese). [Accessed November 15, 2023]
  3. [3] Japanese Association of Museums, “Hakubutsukan ni okeru shingata korona uirusu kansen kakudai yobou guideline (Guidelines for prevention against spread of COVID-19 infection in museums),” (in Japanese). [Accessed September 1, 2023]
  4. [4] Goodfellows Co., Ltd., Webket. [Accessed September 1, 2023]
  5. [5] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to Crowd Management—Managing Crowds in the Digital Era: Theory and Practice,” pp. 119-123, University of Tokyo Press, 2020 (in Japanese).
  6. [6] “Kansensho no Yobo Oyobi Kansensho no Kanja ni Taisuru Iryo ni Kansuru Horitsu (Act on the Prevention of Infectious Diseases and Medical Care for Patients with Infectious Diseases),” (in Japanese). [Accessed November 15, 2023]
  7. [7] C. R. Harris et al., “Array programming with NumPy,” Nature, Vol.585, pp. 357-362, 2020.
  8. [8] The pandas development team, “Pandas-dev/pandas: Pandas,” version 2.0.3.
  9. [9] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. of Machine Learning Research, Vol.12, No.85, pp. 2825-2830, 2011.
  10. [10] S. Seabold and J. Perktold, “Statsmodels: Econometric and statistical modeling with python,” Proc. of 9th Python in Science Conf., 2010.
  11. [11] J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, Vol.9, No.3, pp. 90-95, 2007.
  12. [12] C. C. Holt, “Forecasting seasonals and trends by exponentially weighted moving averages,” Int. J. Forecast, Vol.20, Issue 1, pp. 5-10, 2004.
  13. [13] P. R. Winters, “Forecasting Sales by Exponentially Weighted Moving Averages,” Management Science, Vol.6, No.3, pp. 324-342, 1960.
  14. [14] K. T. Talluri and G. J. Van Ryzin, “The theory and practice of revenue management,” Springer, New York, 2004.
  15. [15] R. J. Hyndman and G. Athanasopoulos, “Forecasting: Principles and practice, 3rd edition,” OTexts: Melbourne, Australia, 2021. [Accessed November 20, 2023]

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2024