IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v440y2015icp200-209.html
   My bibliography  Save this article

Congestion detection of pedestrians using the velocity entropy: A case study of Love Parade 2010 disaster

Author

Listed:
  • Huang, Lida
  • Chen, Tao
  • Wang, Yan
  • Yuan, Hongyong

Abstract

Gatherings of large human crowds often result in crowd disasters such as the Love Parade Disaster in Duisburg, Germany on July 24, 2010. To avoid these tragedies, video surveillance and early warning are becoming more and more significant. In this paper, the velocity entropy is first defined as the criterion for congestion detection, which represents the motion magnitude distribution and the motion direction distribution simultaneously. Then the detection method is verified by the simulation data based on AnyLogic software. To test the generalization performance of this method, video recordings of a real-world case, the Love Parade disaster, are also used in the experiments. The velocity histograms of the foreground object in the videos are extracted by the Gaussian Mixture Model (GMM) and optical flow computation. With a sequential change-point detection algorithm, the velocity entropy can be applied to detect congestions of the Love Parade festival. It turned out that without recognizing and tracking individual pedestrian, our method can detect abnormal crowd behaviors in real-time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:440:y:2015:i:c:p:200-209
    DOI: 10.1016/j.physa.2015.08.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115006688
    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.2015.08.013?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. 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.
    2. Armin Seyfried & Oliver Passon & Bernhard Steffen & Maik Boltes & Tobias Rupprecht & Wolfram Klingsch, 2009. "New Insights into Pedestrian Flow Through Bottlenecks," Transportation Science, INFORMS, vol. 43(3), pages 395-406, August.
    3. 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.
    4. Dirk Helbing & Pratik Mukerji, "undated". "Crowd Disasters as Systemic Failures: Analysis of the Love Parade Disaster," Working Papers ETH-RC-12-010, ETH Zurich, Chair of Systems Design.
    5. Liu, Xuan & Song, Weiguo & Zhang, Jun, 2009. "Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2717-2726.
    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. Zheng, Zhongxing & Wang, Heng & Liu, Weiming & Peng, Liru, 2023. "Toward real-time congestion measurement of passenger flow on platform screen doors based on surveillance videos analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    2. Zhang, Yijing & Lu, Linjun & Liu, Qiujia & Hu, Miaoqing, 2023. "Modeling of low-risk behavior of pedestrian movement based on dynamic data analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(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. Zeng, Yiping & Ye, Rui & Song, Weiguo & Luo, Shengfeng & Meng, Fanyu & Vizzari, Giuseppe, 2021. "Entropy analysis of the laminar movement in bidirectional pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

    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. Wang, Jiayue & Boltes, Maik & Seyfried, Armin & Zhang, Jun & Ziemer, Verena & Weng, Wenguo, 2018. "Linking pedestrian flow characteristics with stepping locomotion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 106-120.
    2. 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.
    3. 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.
    4. 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.
    5. Cao, Shuchao & Lian, Liping & Chen, Mingyi & Yao, Ming & Song, Weiguo & Fang, Zhiming, 2018. "Investigation of difference of fundamental diagrams in pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 661-670.
    6. Lian, Liping & Song, Weiguo & Yuen, Kwok Kit Richard & Telesca, Luciano, 2018. "Investigating the time evolution of some parameters describing inflow processes of pedestrians in a room," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 77-88.
    7. Hu, Yanghui & Zhang, Jun & Song, Weiguo, 2019. "Experimental study on the movement strategies of individuals in multidirectional flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    8. Milad Haghani & Majid Sarvi & Zahra Shahhoseini & Maik Boltes, 2016. "How Simple Hypothetical-Choice Experiments Can Be Utilized to Learn Humans’ Navigational Escape Decisions in Emergencies," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-24, November.
    9. Sticco, I.M. & Frank, G.A. & Dorso, C.O., 2021. "Social Force Model parameter testing and optimization using a high stress real-life situation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(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. Huang, Shenshi & Zhang, Teng & Lo, Siuming & Lu, Shouxiang & Li, Changhai, 2018. "Experimental study of individual and single-file pedestrian movement in narrow seat aisle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1023-1033.
    12. 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.
    13. Yi, Ruolong & Du, Mingyu & Song, Weiguo & Zhang, Jun, 2024. "Fast trajectory extraction and pedestrian dynamics analysis using deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    14. Haghani, Milad & Sarvi, Majid, 2018. "Crowd behaviour and motion: Empirical methods," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 253-294.
    15. 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).
    16. Nicolas, Alexandre & Bouzat, Sebastián & Kuperman, Marcelo N., 2017. "Pedestrian flows through a narrow doorway: Effect of individual behaviours on the global flow and microscopic dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 30-43.
    17. Zeng, Tian & Wei, Yidong & Hu, Zuoan & Ma, Yi, 2023. "Comparison study in single-file pedestrian flow dynamics: Foot motion perspective versus head motion perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    18. Haghani, Milad, 2021. "The knowledge domain of crowd dynamics: Anatomy of the field, pioneering studies, temporal trends, influential entities and outside-domain impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    19. Shi, Xiaomeng & Ye, Zhirui & Shiwakoti, Nirajan & Tang, Dounan & Lin, Junkai, 2019. "Examining effect of architectural adjustment on pedestrian crowd flow at bottleneck," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 350-364.
    20. 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).

    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:440:y:2015:i:c:p:200-209. 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.