Abstract
A successful crowd management strategy is based on a correct risk assessment. Although a growing number of tools and technologies are available to crowd managers and it is increasingly easier obtaining information on previous events or accessing reports on crowd accidents, risks are sometimes difficult to be quantified without a structured methodology. In particular, it is important to link consequence and likelihood associated to a particular risk to judge its relevance in relation to various scenarios. Also, the dynamic nature of crowds makes it difficult to reach a good balance between a focus on short-term risky events, possibly leading to accidents, and long-term safety goals. This chapter will introduce a framework based on international standards to be used specifically for crowd management and its applications and limitations will be discussed in detail.
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References
International Organization for Standardization: ISO 31000: Risk Management (2018)
Mitsubishi Electric Corporation: Mitsubishi Electric Develops World’s First Real-Time Crowd-Congestion Estimation System. http://www.mitsubishielectric.com/news/2016/pdf/0818-b.pdf (2016). (online; accessed 17 March 2017)
Molyneaux, N., Scarinci, R., Bierlaire, M.: Design and analysis of control strategies for pedestrian flows. Transportation, 1–41 (2020). https://doi.org/10.1007/s11116-020-10111-1
Lopez-Carmona, M.A., Garcia, A.P.: Cellevac: An adaptive guidance system for crowd evacuation through behavioral optimization. Safety Sci. 139, 105215 (2021). https://doi.org/10.1016/j.ssci.2021.105215
Thaeler, J.J., Patel, A.P.: Crowd Prediction and Attendance Forecasting (2014). US Patent 8,768,867
Anthony (Tony) Cox Jr, L.: What’s wrong with risk matrices? Risk Anal.: Int. J. 28(2), 497–512 (2008). https://doi.org/10.1111/j.1539-6924.2008.01030.x
Murakami, H., Feliciani, C., Shimura, K., Nishinari, K.: A system for efficient egress scheduling during mass events and small-scale experimental demonstration. Royal Soc. Open Sci. 7(12), 201465 (2020). https://doi.org/10.1098/rsos.201465
Zanlungo, F., Feliciani, C., Yucel, Z., Nishinari, K., Kanda, T.: A pure number to assess “congestion” in pedestrian crowds
Littlewood, B., Strigini, L.: Redundancy and diversity in security. In: European Symposium on Research in Computer Security, pp. 423–438. Springer (2004). https://doi.org/10.1007/978-3-540-30108-0_26
Blackman, H.S., Gertman, D.I., Boring, R.L.: Human error quantification using performance shaping factors in the spar-h method. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 52. SAGE Publications Sage CA, Los Angeles, CA, pp. 1733–1737 (2008). https://doi.org/10.1177/154193120805202109
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Feliciani, C., Shimura, K., Nishinari, K. (2021). Risk Management: From Situational Awareness to Crowd Control. In: Introduction to Crowd Management. Springer, Cham. https://doi.org/10.1007/978-3-030-90012-0_7
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DOI: https://doi.org/10.1007/978-3-030-90012-0_7
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