IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v431y2015icp84-93.html
   My bibliography  Save this article

Crowd macro state detection using entropy model

Author

Listed:
  • Zhao, Ying
  • Yuan, Mengqi
  • Su, Guofeng
  • Chen, Tao

Abstract

In the crowd security research area a primary concern is to identify the macro state of crowd behaviors to prevent disasters and to supervise the crowd behaviors. The entropy is used to describe the macro state of a self-organization system in physics. The entropy change indicates the system macro state change. This paper provides a method to construct crowd behavior microstates and the corresponded probability distribution using the individuals’ velocity information (magnitude and direction). Then an entropy model was built up to describe the crowd behavior macro state. Simulation experiments and video detection experiments were conducted. It was verified that in the disordered state, the crowd behavior entropy is close to the theoretical maximum entropy; while in ordered state, the entropy is much lower than half of the theoretical maximum entropy. The crowd behavior macro state sudden change leads to the entropy change. The proposed entropy model is more applicable than the order parameter model in crowd behavior detection. By recognizing the entropy mutation, it is possible to detect the crowd behavior macro state automatically by utilizing cameras. Results will provide data support on crowd emergency prevention and on emergency manual intervention.

Suggested Citation

  • Zhao, Ying & Yuan, Mengqi & Su, Guofeng & Chen, Tao, 2015. "Crowd macro state detection using entropy model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 431(C), pages 84-93.
  • Handle: RePEc:eee:phsmap:v:431:y:2015:i:c:p:84-93
    DOI: 10.1016/j.physa.2015.02.068
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115001910
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2015.02.068?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dirk Helbing & Illés Farkas & Tamás Vicsek, 2000. "Simulating dynamical features of escape panic," Nature, Nature, vol. 407(6803), pages 487-490, September.
    2. Zhang, X.L. & Weng, W.G. & Yuan, H.Y. & Chen, J.G., 2013. "Empirical study of a unidirectional dense crowd during a real mass event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2781-2791.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rangel-Huerta, A. & Ballinas-Hernández, A.L. & Muñoz-Meléndez, A., 2017. "An entropy model to measure heterogeneity of pedestrian crowds using self-propelled agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 213-224.
    2. Zhang, Xuguang & Shu, Xiaohu & He, Zhen, 2019. "Crowd panic state detection using entropy of the distribution of enthalpy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 935-945.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yue, Hao & Zhang, Junyao & Chen, Wenxin & Wu, Xinsen & Zhang, Xu & Shao, Chunfu, 2021. "Simulation of the influence of spatial obstacles on evacuation pedestrian flow in walking facilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    2. Cui, Geng & Yanagisawa, Daichi & Nishinari, Katsuhiro, 2023. "Learning from experimental data to simulate pedestrian dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    3. Wang, Jia & Ni, Shunjiang & Shen, Shifei & Li, Shuying, 2019. "Empirical study of crowd dynamic in public gathering places during a terrorist attack event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1-9.
    4. 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.
    5. 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).
    6. Rozan, E.A. & Frank, G.A. & Cornes, F.E. & Sticco, I.M. & Dorso, C.O., 2022. "Microscopic dynamics of escaping groups through an exit and a corridor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    7. 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.
    8. Stock, Eduardo Velasco & da Silva, Roberto, 2023. "Lattice gas model to describe a nightclub dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    9. Varas, A. & Cornejo, M.D. & Mainemer, D. & Toledo, B. & Rogan, J. & Muñoz, V. & Valdivia, J.A., 2007. "Cellular automaton model for evacuation process with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 631-642.
    10. Murilo S Baptista & Hai-Peng Ren & Johen C M Swarts & Rodrigo Carareto & Henk Nijmeijer & Celso Grebogi, 2012. "Collective Almost Synchronisation in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-11, November.
    11. Xianing Wang & Zhan Zhang & Ying Wang & Jun Yang & Linjun Lu, 2022. "A Study on Safety Evaluation of Pedestrian Flows Based on Partial Impact Dynamics by Real-Time Data in Subway Stations," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    12. Chen, Changkun & Sun, Huakai & Lei, Peng & Zhao, Dongyue & Shi, Congling, 2021. "An extended model for crowd evacuation considering pedestrian panic in artificial attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    13. Michael Batty & Jake Desyllas & Elspeth Duxbury, 2003. "Safety in Numbers? Modelling Crowds and Designing Control for the Notting Hill Carnival," Urban Studies, Urban Studies Journal Limited, vol. 40(8), pages 1573-1590, July.
    14. Ma, Jian & Song, Wei-guo & Zhang, Jun & Lo, Siu-ming & Liao, Guang-xuan, 2010. "k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2101-2117.
    15. Illés J Farkas & Shuohong Wang, 2018. "Spatial flocking: Control by speed, distance, noise and delay," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-12, May.
    16. Zheng, Yaochen & Chen, Jianqiao & Wei, Junhong & Guo, Xiwei, 2012. "Modeling of pedestrian evacuation based on the particle swarm optimization algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4225-4233.
    17. Sungryong Bae & Jun-Ho Choi & Hong Sun Ryou, 2020. "Modification of Interaction Forces between Smoke and Evacuees," Energies, MDPI, vol. 13(16), pages 1-10, August.
    18. Lasse Pedersen, 2009. "When Everyone Runs for the Exit," International Journal of Central Banking, International Journal of Central Banking, vol. 5(4), pages 177-199, December.
    19. Shiwakoti, Nirajan & Sarvi, Majid, 2013. "Understanding pedestrian crowd panic: a review on model organisms approach," Journal of Transport Geography, Elsevier, vol. 26(C), pages 12-17.
    20. Ofer Tchernichovski & Marissa King & Peter Brinkmann & Xanadu Halkias & Daniel Fimiarz & Laurent Mars & Dalton Conley, 2017. "Tradeoff Between Distributed Social Learning and Herding Effect in Online Rating Systems," SAGE Open, , vol. 7(1), pages 21582440176, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:431:y:2015:i:c:p:84-93. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.