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JDR Vol.19 No.2 pp. 279-292
(2024)
doi: 10.20965/jdr.2024.p0279

Paper:

# Modeling and Questionnaire Survey for Effective Regulated Egress Based on Level of Discomfort

## Riku Miyagawa*1, Daichi Yanagisawa*1,*2,†, Xiaolu Jia*1, Yasushi Shoji*3, Tetsuya Aikoh*3, and Katsuhiro Nishinari*1,*2,*4

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

*2Mobility Innovation Collaborative Research Organization, The University of Tokyo
Kashiwa, Japan

*3Research Faculty of Agriculture, Hokkaido University
Sapporo, Japan

*4Research Center for Advanced Science and Technology, The University of Tokyo
Tokyo, Japan

Corresponding author

October 6, 2023
Accepted:
December 6, 2023
Published:
April 1, 2024
Keywords:
regulated egress, discomfort, event, model, questionnaire
Abstract

Regulated egress is often conducted after large events to avoid extreme congestion at stations around event venues. In regulated egress, people are divided into several groups and egress in order. By controlling the number of groups and the time interval between each group’s egress, managers can mitigate the congestion at the stations. In this study, a mathematical model was developed to identify the effective regulated egress. level of discomfort (LOD) was used to evaluate the performance of the regulated egress instead of the total egress time. LOD is the product of the function of density and duration of egress and represents the accumulated discomfort through the egress. A questionnaire survey was conducted to determine the LOD function parameters. Under the assumed conditions, the results of the calibrated model indicated that effective regulated egress could be conducted by dividing the people into two or three groups, which is presumable in terms of management in the real world. In addition to the main result for the effective number of groups, the robustness of the model was confirmed by comparing the results of the two types of LOD functions. In other words, the effective number of groups does not strongly depend on the detailed form of the LOD functions.

R. Miyagawa, D. Yanagisawa, X. Jia, Y. Shoji, T. Aikoh, and K. Nishinari, “Modeling and Questionnaire Survey for Effective Regulated Egress Based on Level of Discomfort,” J. Disaster Res., Vol.19 No.2, pp. 279-292, 2024.
Data files:
References
1. [1] A. Schadschneider, W. Klingsch, H. Klüpfel, T. Kretz, C. Rogsch, A. Seyfried, H. Kluepfel, T. Kretz, C. Rogsch, A. Seyfried, H. Klüpfel, T. Kretz, C. Rogsch, and A. Seyfried, “Evacuation Dynamics: Empirical Results, Modeling and Applications,” R. A. Meyers (Ed.), “Encyclopedia of Complexity and Systems Science,” Vol.50, pp. 3142-3176, Springer, 2009. https://doi.org/10.1007/978-0-387-30440-3_187
2. [2] J. F. Dickie, “Major crowd catastrophes,” Saf. Sci., Vol.18, No.4, pp. 309-320, 1995. https://doi.org/10.1016/0925-7535(94)00048-8
3. [3] D. Helbing and P. Mukerji, “Crowd disasters as systemic failures: Analysis of the Love Parade disaster,” EPJ Data Science, Vol.1, Article No.7, 2012. https://doi.org/10.1140/epjds7
4. [4] C. Feliciani, A. Corbetta, M. Haghani, and K. Nishinari, “Trends in crowd accidents based on an analysis of press reports,” Saf. Sci., Vol.164, Article No.106174, 2023. https://doi.org/10.1016/j.ssci.2023.106174
5. [5] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to Crowd Management,” Springer International Publishing, 2022. https://doi.org/10.1007/978-3-030-90012-0
6. [6] H. Murakami, C. Feliciani, K. Shimura, and K. Nishinari, “A system for efficient egress scheduling during mass events and small-scale experimental demonstration,” Royal Society Open Science, Vol.7, No.12, Article No.201465, 2020. https://doi.org/10.1098/rsos.201465
7. [7] M. Ohnishi, S. Shigenaka, and T. Yamashita, “Automatic Crowd Control for Large-scale Events,” J. of the Japanese Society for Artificial Intelligence, Vol.34, No.6, pp. 768-773, 2019 (in Japanese). https://doi.org/10.11517/jjsai.34.6_768
8. [8] C. Feliciani and K. Nishinari, “Measurement of congestion and intrinsic risk in pedestrian crowds,” Transp. Res. Part C: Emerg. Technol., Vol.91, pp. 124-155, 2018. https://doi.org/10.1016/j.trc.2018.03.027
9. [9] F. Zanlungo, C. Feliciani, Z. Yücel, X. Jia, K. Nishinari, and T. Kanda, “A pure number to assess “congestion” in pedestrian crowds,” Transp. Res. Part C: Emerg. Technol., Vol.148, Article No.104041, 2023. https://doi.org/10.1016/j.trc.2023.104041
10. [10] J. Cordes, A. Nicolas, and A. Schadschneider, “Dimensionless Numbers Reveal Distinct Regimes in the Structure and Dynamics of Pedestrian Crowds,” arXiv preprint, arXiv:2307.12786, 2023. https://doi.org/10.48550/arXiv.2307.12786
11. [11] R. Kawaguchi, D. Yanagisawa, C. Feliciani, S. Nozaki, Y. Abe, M. Mita, and K. Nishinari, “Modeling and controlling congestion caused by a bottleneck in an overcrowded aquarium,” Physica A: Statistical Mechanics and its Applications, Vol.615, Article No.128547, 2023. https://doi.org/10.1016/j.physa.2023.128547
12. [12] R. Miyagawa, D. Yanagisawa, X. Jia, and K. Nishinari, “Effective regulated egress at large event venues,” JSIAM Lett., Vol.15, pp. 113-116, 2023. https://doi.org/10.14495/jsiaml.15.113
13. [13] R. Miyagawa, “Evaluation and improvement of regulatory egress at large event venues,” Master’s thesis, The University of Tokyo, 2023.
14. [14] ““New Main Stand” at Todoroki Athletics Stadium (Vol.1),” 2014 (in Japanese). https://www.frontale.co.jp/info/2014/1117_15.html [Accessed September 20, 2023]
15. [15] M. C. Carrasco, M. C. Bernal, and R. Redolat, “Time estimation and aging: A comparison between young and elderly adults,” Int. J. Aging Hum. Dev., Vol.52, No.2, pp. 91-101, 2001. https://doi.org/10.2190/7NFL-CGCP-G9E1-P0H1
16. [16] J. Zhang, W. Klingsch, A. Schadschneider, and A. Seyfried, “Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram,” J. Stat. Mech: Theory Exp., Vol.2012, No.02, Article No.P02002, 2012. https://doi.org/10.1088/1742-5468/2012/02/P02002
17. [17] D. Yanagisawa, A. Tomoeda, and K. Nishinari, “Improvement of pedestrian flow by slow rhythm,” Physical Review E, Vol.85, No.1, Article No.016111, 2012. https://doi.org/10.1103/PhysRevE.85.016111
18. [18] X. Jia, C. Feliciani, H. Murakami, A. Nagahama, D. Yanagisawa, and K. Nishinari, “Revisiting the level-of-service framework for pedestrian comfortability: Velocity depicts more accurate perceived congestion than local density,” Transp. Res. Part F: Traffic Psychol. Behav., Vol.87, pp. 403-425, 2022. https://doi.org/10.1016/j.trf.2022.04.007
19. [19] C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz, “Simulation of pedestrian dynamics using a two-dimensional cellular automaton,” Physica A: Statistical Mechanics and its Applications, Vol.295, Nos.3-4, pp. 507-525, 2001. https://doi.org/10.1016/S0378-4371(01)00141-8
20. [20] D. Yanagisawa, A. Kimura, A. Tomoeda, R. Nishi, Y. Suma, K. Ohtsuka, and K. Nishinari, “Introduction of frictional and turning function for pedestrian outflow with an obstacle,” Physical Review E, Vol.80, No.3, Article No.036110, 2009. https://doi.org/10.1103/PhysRevE.80.036110
21. [21] M. Haghani and M. Sarvi, “Stated and revealed exit choices of pedestrian crowd evacuees,” Trans. Res. Part B: Methodol., Vol.95, pp. 238-259, 2017. https://doi.org/10.1016/j.trb.2016.10.019
22. [22] H. Kim, Y. Shoji, K. Mameno, T. Kubo, and T. Aikoh, “Changes in visits to green spaces due to the COVID-19 pandemic: Focusing on the proportion of repeat visitors and the distances between green spaces and visitors’ places of residences,” Urban For. Urban Greening, Vol.80, Article No.127828, 2023. https://doi.org/10.1016/j.ufug.2022.127828
23. [23] K. Park, P. A. Singleton, S. Brewer, and J. Zuban, “Pedestrians and the Built Environment during the COVID-19 Pandemic: Changing Relationships by the Pandemic Phases in Salt Lake County, Utah, U.S.A,” Transp. Res. Rec., Vol.2677, No.4, pp. 448-462, 2023. https://doi.org/10.1177/03611981221083606
24. [24] A. Angel, A. Cohen, S. Dalyot, and P. Plaut, “Impact of COVID-19 policies on pedestrian traffic and walking patterns,” Environment and Planning B: Urban Analytics and City Science, Vol.50, No.5, pp. 1178-1193, 2023. https://doi.org/10.1177/23998083221113332

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Last updated on Jul. 19, 2024