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From Crowd Dynamics To Crowd Safety: A Video-Based Analysis

Author

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  • ANDERS JOHANSSON

    (ETH Zurich, UNO D11, Universitätstrasse 41, CH-8092 Zurich, Switzerland)

  • DIRK HELBING

    (ETH Zurich, UNO D11, Universitätstrasse 41, CH-8092 Zurich, Switzerland)

  • HABIB Z. AL-ABIDEEN

    (Central Directorate for Holy Areas Development, Minstry of Municipal and Rural Affairs, Riyadh, Kingdom of Saudi Arabia)

  • SALIM AL-BOSTA

    (Central Directorate for Holy Areas Development, Minstry of Municipal and Rural Affairs, Riyadh, Kingdom of Saudi Arabia)

Abstract

The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbinget al.,Phys. Rev. Lett.97(2006) 168001] and a phenomenon called "crowd turbulence" can occur at high pedestrian densities [D. Helbinget al.,Phys. Rev. E75(2007) 046109]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah, Saudi-Arabia. Our results reveal previously unexpected pattern formation phenomena and show that the average individual speed does not go to zero even at local densities of 10 persons per square meter. Since the maximum density and flow are different from measurements in other countries, this has implications for the capacity assessment and dimensioning of facilities for mass events. When conditions become congested, the flow drops significantly, which can cause stop-and-go waves and a further increase of the density until critical crowd conditions are reached. Then, "crowd turbulence" sets in, which may trigger crowd disasters. For this reason, it is important to operate pedestrian facilities sufficiently below their maximum capacity and to take measures to improve crowd safety, some of which are discussed in the end.

Suggested Citation

  • Anders Johansson & Dirk Helbing & Habib Z. Al-Abideen & Salim Al-Bosta, 2008. "From Crowd Dynamics To Crowd Safety: A Video-Based Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 497-527.
  • Handle: RePEc:wsi:acsxxx:v:11:y:2008:i:04:n:s0219525908001854
    DOI: 10.1142/S0219525908001854
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    References listed on IDEAS

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    1. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, April.
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    Cited by:

    1. Huang, Lida & Chen, Tao & Wang, Yan & Yuan, Hongyong, 2015. "Congestion detection of pedestrians using the velocity entropy: A case study of Love Parade 2010 disaster," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 440(C), pages 200-209.
    2. Lian, Liping & Song, Weiguo & Richard, Yuen Kwok Kit & Ma, Jian & Telesca, Luciano, 2017. "Long-range dependence and time-clustering behavior in pedestrian movement patterns in stampedes: The Love Parade case-study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 265-274.
    3. Jiang, Yan-Qun & Zhang, Wei & Zhou, Shu-Guang, 2016. "Comparison study of the reactive and predictive dynamic models for pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 51-61.
    4. Amin Mazloumian & Nikolas Geroliminis & Dirk Helbing, "undated". "The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity," Working Papers CCSS-09-009, ETH Zurich, Chair of Systems Design.
    5. Abdelghany, Ahmed & Abdelghany, Khaled & Mahmassani, Hani, 2016. "A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 159-176.
    6. Syed, Ahmed & Thampi, Sumesh P. & Panchagnula, Mahesh V., 2022. "Order-stampede transitions in human crowds: The role of individualistic and cooperative forces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    7. Subramanian, Gayathri Harihara & Choubey, Nipun & Verma, Ashish, 2022. "Modelling and simulating serpentine group behaviour in crowds using modified social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    8. Liu, Yixue & Mao, Zhanli, 2022. "An experimental study on the critical state of herd behavior in decision-making of the crowd evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    9. Li, Xudong & Telesca, Luciano & Lovallo, Michele & Xu, Xuan & Zhang, Jun & Song, Weiguo, 2020. "Spectral and informational analysis of pedestrian contact force in simulated overcrowding conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    10. Xiaolin Yang & Zhongliang Wu, 2013. "Civilian monitoring video records for earthquake intensity: a potentially unbiased online information source of macro-seismology," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1765-1781, February.
    11. Femke van Wageningen-Kessels & Winnie Daamen & Serge P. Hoogendoorn, 2018. "Two-Dimensional Approximate Godunov Scheme and What It Means For Continuum Pedestrian Flow Models," Transportation Science, INFORMS, vol. 52(3), pages 547-563, June.
    12. Mavrodiev, Pavlin & Schweitzer, Frank, 2021. "The ambigous role of social influence on the wisdom of crowds: An analytic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    13. Liang, Haoyang & Du, Jie & Wong, S.C., 2021. "A Continuum model for pedestrian flow with explicit consideration of crowd force and panic effects," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 100-117.
    14. Liu, Shang & Li, Peiyu, 2020. "Nonlinear analysis of pedestrian flow Reynolds number in video scenes," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    15. Jonas L. Juul & Laura Alessandretti & Jesper Dammeyer & Ingo Zettler & Sune Lehmann & Joachim Mathiesen, 2023. "Group-specific behavior change following terror attacks," Journal of Computational Social Science, Springer, vol. 6(1), pages 1-18, April.
    16. Cristiani, E. & Menci, M. & Malagnino, A. & Amaro, G.G., 2023. "An all-densities pedestrian simulator based on a dynamic evaluation of the interpersonal distances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    17. Wang, W.L. & Tsui, K.L. & Lo, S.M. & Liu, S.B., 2018. "Computational modeling and statistical analyses on individual contact rate and exposure to disease in complex and confined transportation hubs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1461-1470.
    18. Knut Haase & Habib Zain Al Abideen & Salim Al-Bosta & Mathias Kasper & Matthes Koch & Sven Müller & Dirk Helbing, 2016. "Improving Pilgrim Safety During the Hajj: An Analytical and Operational Research Approach," Interfaces, INFORMS, vol. 46(1), pages 74-90, February.

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