JDR Vol.19 No.2 pp. 293-302
doi: 10.20965/jdr.2024.p0293


Using Virtual Reality to Study the Effectiveness of Crowd Control Medium and Information

Shuhei Miyano

SECOM Intelligent System Laboratory
SECOM SC Center, 8-10-16 Shimorenjaku, Mitaka, Tokyo 181-8528, Japan

Corresponding author

October 6, 2023
November 16, 2023
April 1, 2024
crowd-control measures, control medium, control information, compliance behavior, virtual reality

When designing crowd control through simulations, the appropriate crowd-control medium (objects used to convey control information, e.g., signages or security guards) and information should be selected, considering the crowd’s compliance with control instructions. However, there is still scope for further research on the influence of control medium and information on compliance behavior. Therefore, in this study, we measured the effectiveness of medium and information in guiding participants’ route choices by conducting a crowd experiment using virtual reality. The experimental findings confirmed that in terms of control medium, the guidance proffered by security guards was more effective than signage, with the odds of compliance rate approximately 1.54 times greater. Regarding control information, Guide control (direct guidance instruction) was more effective and received approximately 1.22 times greater odds of compliance rate than Advise control (indirect guidance through information presentation). Crowd-control designers can use the results obtained in this study to evaluate the effectiveness of control measures in crowd simulations.

Cite this article as:
S. Miyano, “Using Virtual Reality to Study the Effectiveness of Crowd Control Medium and Information,” J. Disaster Res., Vol.19 No.2, pp. 293-302, 2024.
Data files:
  1. [1] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to crowd management: Managing crowds in the digital era: Theory and Practice,” Springer, 2021.
  2. [2] C. M. Mayr and G. Köster, “Guiding crowds when facing limited compliance: Simulating strategies,” PLoS One, Vol.17, No.11, Article No.e0276229, 2022.
  3. [3] Stichting NM Magazine, “Human factors in verkeersmanagement,” NM Magazine, Vol.1, pp. 18-21, 2013.
  4. [4] L. B. Zomer, W. Daamen, S. Meijer, and S. P. Hoogendoorn, “Managing crowds: The possibilities and limitations of crowd information during urban mass events,” “Planning Support Systems and Smart Cities, Springer,” S. Geertman, J. Ferreira, Jr., R. Goodspeed, and, J. Stillwell (Eds.), pp. 77-97, 2015.
  5. [5] N. Wijermans, C. Conrado, M. van Steen, C. Martella, and J. Li, “A landscape of crowd-management support: An integrative approach,” Saf. Sci., Vol.86, pp. 142-164, 2016.
  6. [6] H. Gayathri, P. M. Aparna, and A. Verma, “A review of studies on understanding crowd dynamics in the context of crowd safety in mass religious gatherings,” Int. J. Disaster Risk Reduct., Vol.25, pp. 82-91, 2017.
  7. [7] J. J. Fruin, “Pedestrian planning and design,” Metropolitan Association of Urban Designers and Environmental Planners, 1971.
  8. [8] L. Soomaroo and V. Murray, “Disasters at mass gatherings: Lessons from history,” PLoS Curr., Vol.4, Article No.RRN1301, 2012.
  9. [9] M. Haghani, M. Coughlan, B. Crabb, A. Dierickx, C. Feliciani, R. van Gelder, P. Geoerg, N. Hocaoglu, S. Laws, R. Lovreglio, Z. Miles, A. Nicolas, W. J. O’Toole, S. Schaap, T. Semmens, Z. Shahhoseini, R. Spaaij, A. Tatrai, J. Webster, and A. Wilson, “A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target,” Saf. Sci., Vol.168, Article No.106292, 2023.
  10. [10] A. Owaidah, D. Olaru, M. Bennamoun, F. Sohel, and N. Khan, “Review of modelling and simulating crowds at mass gathering events: Hajj as a case study,” J. Artif. Soc. Soc. Simul., Vol.22, No.2, Article No.9, 2019.
  11. [11] M. Gödel, R. Fischer, and G. Köster, “Sensitivity analysis for microscopic crowd simulation,” Algorithms, Vol.13, No.7, Article No.162, 2020.
  12. [12] 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.
  13. [13] D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, Vol.407, pp. 487-490, 2000.
  14. [14] S. P. Hoogendoorn and P. H. L. Bovy, “Pedestrian route-choice and activity scheduling theory and models,” Transp. Res. B: Methodol., Vol.38, No.2, pp. 169-190, 2004.
  15. [15] W. Daamen, “Modelling passenger flows in public transport facilities,” Ph.D. thesis, Delft University of Technology, 2002.
  16. [16] P. N. Seneviratne and J. F. Morrall, “Analysis of factors affecting the choice of route of pedestrians,” Transp. Plan. Technol., Vol.10, No.2, pp. 147-159, 1985.
  17. [17] X. Lu, H. Blanton, T. Gifford, A. Tucker, and N. Olderman, “Optimized guidance for building fires considering occupants’ route choices,” Phys. A: Stat. Mech., Vol.561, Article No.125247, 2021.
  18. [18] M. A. Lopez-Carmona and A. P. Garcia, “CellEVAC: An adaptive guidance system for crowd evacuation through behavioral optimization,” Saf. Sci., Vol.139, Article No.105215, 2021.
  19. [19] J. C. Chu, A. Y. Chen, and Y.-F. Lin, “Variable guidance for pedestrian evacuation considering congestion, hazard, and compliance behavior,” Transp. Res. C: Emerg. Technol., Vol.85, pp. 664-683, 2017.
  20. [20] M. Zhang, J. Ke, L. Tong, and X. Luo, “Investigating the influence of route turning angle on compliance behaviors and evacuation performance in a virtual-reality-based experiment,” Adv. Eng. Inform., Vol.48, Article No.101259, 2021.
  21. [21] J. Kubota, T. Sano, and E. Ronchi, “Assessing compliance with the direction indicated by emergency evacuation signage,” Saf. Sci., Vol.138, Article No.105210, 2021.
  22. [22] Y. Feng, D. C. Duives, and S. P. Hoogendoorn, “Using virtual reality to study pedestrian exit choice behavior during evacuations,” Saf. Sci., Vol.137, Article No.105158, 2021.
  23. [23] E. R. Galea, H. Xie, S. Deere, D. Cooney, and L. Filippidis, “Evaluating the effectiveness of an improved active dynamic signage system using full scale evacuation trials,” Fire Saf. J., Vol.91, pp. 908-917, 2017.
  24. [24] A. Draghici and M. van Steen, “A survey of techniques for automatically sensing the behavior of a crowd,” ACM Comput. Surv., Vol.51, Issue 1, Article No.21, 2018.
  25. [25] T. Li, H. Chang, M. Wang, B. Ni, R. Hong, and S. Yan, “Crowded scene analysis: A survey,” IEEE Trans. Circuits Syst. Video Technol., Vol.25, No.3, pp. 367-386, 2015.
  26. [26] M. Moussaïd, M. Kapadia, T. Thrash, R. W. Sumner, M. Gross, D. Helbing, and C. Hlscher, “Crowd behaviour during high-stress evacuations in an immersive virtual environment,” J. R. Soc. Interface, Vol.13, No.122, Article No.20160414, 2016.
  27. [27] K. W. Rio, G. C. Dachner, and W. H. Warren, “Local interactions underlying collective motion in human crowds,” Proc. R. Soc. B: Biol. Sci., Vol.285, No.1878, Article No.20180611, 2018.
  28. [28] R. Lovreglio, E. Dillies, E. Kuligowski, A. Rahouti, and M. Haghani, “Exit choice in built environment evacuation combining immersive virtual reality and discrete choice modeling,” Autom. Constr., Vol.141, Article No.104452, 2021.
  29. [29] J. Bruneau, A.-H. Olivier, and J. Pettré, “Going through, going around: A study on the individual avoidance of groups,” IEEE Trans. Vis. Comput. Graph., Vol.21, No.4, pp. 520-528, 2015.
  30. [30] A. Ríos and N. Pelechano, “Follower behavior under stress in immersive VR,” Virtual Real., Vol.24, No.4, pp. 683-694, 2020.
  31. [31] R. K. Dubey, W. P. Khoo, M. G. Morad, C. Hölscher, and M. Kapadia, “AUTOSIGN: A multi-criteria optimization approach to computer aided design of signage layouts in complex buildings,” Comput. Graph., Vol.88, pp. 13-23, 2020.
  32. [32] R. Lovreglio, A. Fonzone, and L. Dell’Olio, “A mixed logit model for predicting exit choice during building evacuations,” Transp. Res. A: Policy Pract., Vol.92, pp. 59-75, 2016.
  33. [33] Unity Technologies, “Unity – Manual: NavMesh Agent.” [Accessed November 9, 2023]
  34. [34] J. van den Berg, M. Lin, and D. Manocha, “Reciprocal velocity obstacles for real-time multi-agent navigation,” Proc. IEEE Int. Conf. Robot. Autom., pp. 1928-1935, 2008.
  35. [35] D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, Vol.47, No.2, pp. 263-292, 1979.

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

Last updated on Jul. 12, 2024