JDR Vol.19 No.2 pp. 239-247
doi: 10.20965/jdr.2024.p0239


Recent Developments in Crowd Management: Theory and Applications

Katsuhito Nishinari, Claudio Feliciani ORCID Icon, Xiaolu Jia ORCID Icon, and Sakurako Tanida ORCID Icon

Department of Aeronautics and Astronautics, School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Corresponding author

October 3, 2023
January 5, 2024
April 1, 2024
crowd management, stampede, crowd sensing, crowd simulation, crowd control

Managing crowds is important not only during evacuation in disasters such as earthquakes and fires but also during normal situations. In particular, places where many people gather every day, such as stations or event venues, need such management to prevent crowd accidents. Moreover, efficient guidance that prevents people from waiting or queuing can improve facility services and lead to business opportunities. In this study, we propose a crowd management platform to prevent crowd accidents and provide efficient guidance to visitors. Specifically, we integrate real-time observations of crowd conditions, predictions, and risk assessments through simulation and crowd control in collaboration with security and facility managers. We also present the results of operating this platform in actual fields, which contribute to and support the safety and comfort of individuals.

Cite this article as:
K. Nishinari, C. Feliciani, X. Jia, and S. Tanida, “Recent Developments in Crowd Management: Theory and Applications,” J. Disaster Res., Vol.19 No.2, pp. 239-247, 2024.
Data files:
  1. [1] G. Le Bon, “The Crowd: A Study of the Popular Mind,” Penguin Books, 1977.
  2. [2] M. Baddeley, “Herding, social influence and economic decision-making: Socio-psychological and neuroscientific analyses,” Philos. Trans. R. Soc. B, Vol.365, No.1538, pp. 281-290, 2010.
  3. [3] J. Tubbs and B. Meacham, “Egress Design Solutions: A Guide to Evacuation and Crowd Management Planning,” John Wiley & Sons, Inc., 2007.
  4. [4] D. Helbing and A. Johansson, “Pedestrian, crowd and evacuation dynamics,” R. A. Meyers (Ed.), “Encyclopedia of Complexity and Systems Science,” pp. 6476-6495, Springer, 2010.
  5. [5] I. Echeverría-Huarte, A. Garcimartín, R. C. Hidalgo, C. Martín-Gómez, and I. Zuriguel, “Estimating density limits for walking pedestrians keeping a safe interpersonal distancing,” Sci. Rep., Vol.11, Article No.1534, 2021.
  6. [6] S. J. D. Prince, “Computer Vision: Models, Learning, and Inference,” Cambridge University Press, 2012.
  7. [7] D. Thalmann and S. R. Musse, “Crowd Simulation,” Springer, 2013.
  8. [8] R. H. Thaler and C. R. Sunstein, “Nudge: Improving Decisions About Health, Wealth, and Happiness,” Penguin Books, 2009.
  9. [9] R. Yaagoubi, Y. Miky, K. Faisal, and A. A. Shouny, “A combined agent-based modeling and GIS approach for HAJJ crowd simulation,” J. Eng. Res., Vol.11, No.1, Article No.100014, 2023.
  10. [10] R. Nishida, Y. Tanigaki, M. Onishi, and K. Hashimoto, “Multi-objective deep reinforcement learning for crowd route guidance optimization,” Transp. Res. Rec., 2023.
  11. [11] Q. Yu et al., “Intelligent visual-IoT-enabled real-time 3D visualization for autonomous crowd management,” IEEE Wirel. Commun., Vol.28, No.4, pp. 34-41, 2021.
  12. [12] A. K. Boomers et al., “Pedestrian crowd management experiments: A data guidance paper,” Collect. Dyn., Vol.8, 2023.
  13. [13] C.-H. Shao, P.-C. Shao, and F.-M. Kuo, “Stampede events and strategies for crowd management,” J. Disaster Res., Vol.14, No.7, pp. 949-958, 2019.
  14. [14] JST Project Website (in Japanese). [Accessed January 8, 2024]
  15. [15] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to crowd management: Managing crowds in the digital era: Theory and practice,” Springer, 2022.
  16. [16] J. J. Fruin, “Pedestrian Planning and Design,” Metropolitan Association of Urban Designers and Environmental Planners, 1971.
  17. [17] C. Feliciani and K. Nishinari, “Measurement of congestion and intrinsic risk in pedestrian crowds,” Transp. Res. C: Emerg. Technol., Vol.91, pp. 124-155, 2018.
  18. [18] P. Dong and Q. Chen, “LiDAR Remote Sensing and Applications,” CRC Press, 2017.
  19. [19] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 779-788, 2016.
  20. [20] A. Stanitsa S. H. Hallett, and S. Jude, “Investigating pedestrian behaviour in urban environments: A Wi-Fi tracking and machine learning approach,” Multimodal Transp., Vol.2, No.1, Article No.100049, 2023.
  21. [21] M. Ruiz-Pérez, V. Ramos, and B. Alorda-Ladaria, “Integrating high-frequency data in a GIS environment for pedestrian congestion monitoring,” Inf. Process. Manag., Vol.60, No.2, Article No.103236, 2023.
  22. [22] D. Yanagisawa, A. Tomoeda, and K. Nishinari, “Improvement of pedestrian flow by slow rhythm,” Phys. Rev. E, Vol.85, No.1, Article No.016111, 2012.
  23. [23] K. Yasufuku and A. Takahashi, “Development of a real-time crowd flow prediction and visualization platform for crowd management,” J. Disaster Res., Vol.19, No.2, pp. 248-255, 2024.
  24. [24] S. Tanida et al., “Investigating the congestion levels on a mesoscopic scale during outdoor events,” J. Disaster Res., Vol.19, No.2, pp. 347-358, 2024.
  25. [25] M. Zhai, X. Xiang, N. Lv, and X. Kong, “Optical flow and scene flow estimation: A survey,” Pattern Recognit., Vol.114, Article No.107861, 2021.
  26. [26] Y. Himeur et al., “Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey,” Sustain. Cities Soc., Vol.85, Article No.104064, 2022.
  27. [27] Q. Shi et al., “Deep learning enabled smart mats as a scalable floor monitoring system,” Nat. Commun., Vol.11, Article No.4609, 2020.
  28. [28] D. Helbing, “A fluid-dynamic model for the movement of pedestrians,” Complex Syst., Vol.6, No.5, pp. 391-415, 1992.
  29. [29] K. Nishinari, A. Kirchner, A. Namazi, and A. Schadschneider, “Extended floor field CA model for evacuation dynamics,” IEICE Trans. Inf. Syst., Vol.E87-D, No.3, pp. 726-732, 2004.
  30. [30] D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Phys. Rev. E, Vol.51, No.5, pp. 4282-4286, 1995.
  31. [31] G. Cui, D. Yanagisawa, and K. Nishinari, “A data driven approach to simulate pedestrian competitiveness using the social force model,” Collect. Dyn., Vol.6, pp. 1-15, 2022.
  32. [32] M. Shi, E. W. M. Lee, Y. Ma, W. Xie, and R. Cao, “The density-speed correlated mesoscopic model for the study of pedestrian flow,” Saf. Sci., Vol.133, Article No.105019, 2021.
  33. [33] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., Vol.9, No.8, pp. 1735-1780, 1997.
  34. [34] B. Steffen and A. Seyfried, “Methods for measuring pedestrian density, flow, speed and direction with minimal scatter,” Phys. A: Stat. Mech. Appl., Vol.389, No.9, pp. 1902-1910, 2010.

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

Last updated on Apr. 05, 2024