Crowd management at large-scale events and specific facilities is a critical issue from the perspectives of safety and service quality improvement. Traditional methods for crowd management often rely on empirical knowledge, which has limitations in quickly grasping the on-site situation and making decisions on the spot. In this study, we developed a real-time crowd flow prediction and visualization platform incorporating an agent-based crowd simulation and an advanced crowd management system called crowd management platform as a service. In a case study focused on the area around the Tokyo Dome, we demonstrated that capturing pedestrian flow allows for accurate predictions of congestion at the nearest train station up to 10 min in advance. Moreover, the time required to predict the situation 20 min ahead for 3,000 agents was 1 min and 35 s, confirming the feasibility of real-time processing. To enhance the accuracy and reliability of the simulation results, a sensitivity analysis considering errors in pedestrian flow measurement revealed that simple linear models cannot capture the complexity of crowd dynamics adequately. Notably, the agent-based simulation replicated stop-and-go wave patterns observed in actual measurements under specific crowd conditions, confirming the advantage of using agent-based simulations. Finally, we proposed a method that enables facility managers and security personnel to conduct a more comprehensive evaluation. This method integrates their existing experience with the aggregated display of multiple simulation results, which includes consideration of errors in pedestrian flow measurement through a visualization platform.

1. [1] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to Crowd Management: Managing Crowds in the Digital Era: Theory and Practice,” Springer, 2021. https://doi.org/10.1007/978-3-030-90012-0
2. [2] G. Sidiropoulos, C. Kiourt, and L. Moussiades, “Crowd simulation for crisis management: The outcomes of the last decade,” Machine Learning with Applications, Vol.2, Article No.100009, 2020. https://doi.org/10.1016/j.mlwa.2020.100009
3. [3] A. Simonov, A. Lebin, B. Shcherbak, A. Zagarskikh, and A. Karsakov, “Multi-agent crowd simulation on large areas with utility-based behavior models: Sochi Olympic Park Station use case,” Procedia Computer Science, Vol.136, pp. 453-462, 2018. https://doi.org/10.1016/j.procs.2018.08.266
4. [4] V. A. Sindagi and V. M. Patel, “A survey of recent advances in CNN-based single image crowd counting and density estimation,” Pattern Recognition Letters, Vol.107, pp. 3-16, 2018. https://doi.org/10.1016/j.patrec.2017.07.007
5. [5] A. Lesani, E. Nateghinia, and L. F. Miranda-Moreno, “Development and evaluation of a real-time pedestrian counting system for high-volume conditions based on 2D LiDAR,” Transportation Research Part C: Emerging Technologies, Vol.114, pp. 20-35, 2020. https://doi.org/10.1016/j.trc.2020.01.018
6. [6] M. Grieves, “Origins of the digital twin concept,” Florida Institute of Technology, 2016.
7. [7] K. M. Alam and A. El Saddik, “C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems,” IEEE Access, Vol.5, pp. 2050-2062, 2017. https://doi.org/10.1109/ACCESS.2017.2657006
8. [8] Ministry of Land, Infrastructure, Transport and Tourism, “Project PLATEAU” (in Japanese). https://www.mlit.go.jp/plateau [Accessed September 10, 2023]
9. [9] Tokyo Metropolitan Government, “Tokyo Digital Twin Project” (in Japanese). https://info.tokyo-digitaltwin.metro.tokyo.lg.jp [Accessed September 10, 2023]
10. [10] IoTwins. https://www.iotwins.eu [Accessed September 10, 2022]
11. [11] I. Meta et al., “The Camp Nou Stadium as a testbed for city physiology: A modular framework for urban digital twins,” Complexity, Vol.2021, Article No.9731180, 2021. https://doi.org/10.1155/2021/9731180
12. [12] Z. Lin, J. Feng, Z. Lu, Y. Li, and D. Jin, “DeepSTN+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis,” Proc. of the AAAI Conf. on Artificial Intelligence, Vol.33, No.1, pp. 1020-1027, 2019. https://doi.org/10.1609/aaai.v33i01.33011020
13. [13] M. Fu et al., “Fast crowd density estimation with convolutional neural networks,” Engineering Applications of Artificial Intelligence, Vol.43, pp. 81-88, 2015. https://doi.org/10.1016/j.engappai.2015.04.006
14. [14] K. Nagao, D. Yanagisawa, and K. Nishinari, “Estimation of crowd density applying wavelet transform and machine learning,” Physica A: Statistical Mechanics and its Applications, Vol.510, pp. 145-163, 2018. https://doi.org/10.1016/j.physa.2018.06.078
15. [15] V. Karbovskii et al., “Ensemble learning for large-scale crowd flow prediction,” Engineering Applications of Artificial Intelligence, Vol.106, Article No.104469, 2021. https://doi.org/10.1016/j.engappai.2021.104469
16. [16] D. Sharma, A. P. Bhondekar, A. K. Shukla, and C. Ghanshyam, “A review on technological advancements in crowd management,” J. of Ambient Intelligence and Humanized Computing, Vol.9, No.3, pp. 485-495, 2016. https://doi.org/10.1007/s12652-016-0432-x
17. [17] L. A. Toledo Diaz, I. R. Rivas, K. Rodriguez, and I. Rudomin, “Crowd data visualization and simulation,” Procedia Computer Science, Vol.139, pp. 622-629, 2018. https://doi.org/10.1016/j.procs.2018.10.201
18. [18] Thunderhead Engineering, “Pathfinder Technical Reference,” 2017. https://www.thunderheadeng.com/files/com/pathfinder/tech_ref.pdf [Accessed September 10, 2023]
19. [19] W. van Toll et al., “Comparing navigation meshes: Theoretical analysis and practical metrics,” Computers & Graphics, Vol.91, pp. 52-82, 2020. https://doi.org/10.1016/j.cag.2020.06.006
20. [20] P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. on Systems Science and Cybernetics. Vol.4, No.2, pp. 100-107, 1968. https://doi.org/10.1109/TSSC.1968.300136
21. [21] G. Johnson, “Smoothing a navigation mesh path,” S. Rabin (Ed.), “AI Game Programming Wisdom 3,” pp. 129-139, Charles River Media, 2006.
22. [22] Japan Science and Technology Agency (JST) Project Website (in Japanese). http://webpark5056.sakura.ne.jp/crowdmanagement [Accessed September 10, 2023]
23. [23] J. J. Fruin, “Pedestrian planning and design,” Metropolitan Association of Urban Designers and Environmental Planners, 1971.
24. [24] J. E. Nash and J. V. Sutcliffe, “River flow forecasting through conceptual models Part I – A discussion of principles,” J. of Hydrology, Vol.10, No.3, pp. 282-290, 1970. https://doi.org/10.1016/0022-1694(70)90255-6
25. [25] A. Portz and A. Seyfried, “Analyzing stop-and-go waves by experiment and modeling,” R. D. Peacock, E. D. Kuligowski, and J. D. Averill (Eds.), “Pedestrian and Evacuation Dynamics,” pp. 577-586, Springer, 2011. https://doi.org/10.48550/arXiv.1003.5446