IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v427y2015icp162-180.html
   My bibliography  Save this article

Quantification of the level of crowdedness for pedestrian movements

Author

Listed:
  • Duives, Dorine C.
  • Daamen, Winnie
  • Hoogendoorn, Serge P.

Abstract

Within the realm of pedestrian research numerous measures have been proposed to estimate the level of crowdedness experienced by pedestrians. However, within the field of pedestrian traffic flow modelling there does not seem to be consensus on the question which of these measures performs best. This paper shows that the shape and scatter within the resulting fundamental diagrams differs a lot depending on the measure of crowdedness used. The main aim of the paper is to establish the advantages and disadvantages of the currently existing measures to quantify crowdedness in order to evaluate which measures provide both accurate and consistent results. The assessment is not only based on the theoretical differences, but also on the qualitative and quantitative differences between the resulting fundamental diagrams computed using the crowdedness measures on one and the same data set. The qualitative and quantitative functioning of the classical Grid-based measure is compared to with the X-T measure, an Exponentially Weighted Distance measure, and a Voronoi-Diagram measure. The consistency of relating these measures for crowdedness to the two macroscopic flow variables velocity and flow, the computational efficiency and the amount of scatter present within the fundamental diagrams produced by the implementation of the different measures are reviewed. It is found that the Voronoi-Diagram and X-T measure are the most efficient and consistent measures for crowdedness.

Suggested Citation

  • Duives, Dorine C. & Daamen, Winnie & Hoogendoorn, Serge P., 2015. "Quantification of the level of crowdedness for pedestrian movements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 162-180.
    • Handle: RePEc:eee:phsmap:v:427:y:2015:i:c:p:162-180
    DOI: 10.1016/j.physa.2014.11.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114010164
    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.2014.11.054?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. Anders Johansson & Dirk Helbing & Pradyumn K. Shukla, 2007. "Specification Of The Social Force Pedestrian Model By Evolutionary Adjustment To Video Tracking Data," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(supp0), pages 271-288.
    2. Dirk Helbing & Lubos Buzna & Anders Johansson & Torsten Werner, 2005. "Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions," Transportation Science, INFORMS, vol. 39(1), pages 1-24, February.
    3. Steffen, B. & Seyfried, A., 2010. "Methods for measuring pedestrian density, flow, speed and direction with minimal scatter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1902-1910.
    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. Sun, Cheng & Sun, Shi & Qu, Dagang & Zhu, Xun & Liu, Ying, 2023. "Modeling of pedestrian turning behavior and prediction of pedestrian density distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. V. X. Gong & W. Daamen & A. Bozzon & S. P. Hoogendoorn, 2021. "Counting people in the crowd using social media images for crowd management in city events," Transportation, Springer, vol. 48(6), pages 3085-3119, December.
    3. Nagahama, Akihito & Wada, Takahiro & Yanagisawa, Daichi & Nishinari, Katsuhiro, 2021. "Detection of leader–follower combinations frequently observed in mixed traffic with weak lane-discipline," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    4. Tang, Tie-Qiao & Zhang, Bo-Tao & Zhang, Jian & Wang, Tao, 2019. "Statistical analysis and modeling of pedestrian flow in university canteen during peak period," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 29-40.
    5. Shereen Wael & Abeer Elshater & Samy Afifi, 2022. "Mapping User Experiences around Transit Stops Using Computer Vision Technology: Action Priorities from Cairo," Sustainability, MDPI, vol. 14(17), pages 1-20, September.

    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. Zhao, Yongxiang & Li, Meifang & Lu, Xin & Tian, Lijun & Yu, Zhiyong & Huang, Kai & Wang, Yana & Li, Ting, 2017. "Optimal layout design of obstacles for panic evacuation using differential evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 175-194.
    3. Flötteröd, Gunnar & Lämmel, Gregor, 2015. "Bidirectional pedestrian fundamental diagram," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 194-212.
    4. Saberi, Meead & Aghabayk, Kayvan & Sobhani, Amir, 2015. "Spatial fluctuations of pedestrian velocities in bidirectional streams: Exploring the effects of self-organization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 120-128.
    5. Ma, Liang & Chen, Bin & Wang, Xiaodong & Zhu, Zhengqiu & Wang, Rongxiao & Qiu, Xiaogang, 2019. "The analysis on the desired speed in social force model using a data driven approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 894-911.
    6. Qingyan Ning & Maosheng Li, 2022. "Modeling Pedestrian Detour Behavior By-Passing Conflict Areas," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    7. Ziyou Gao & Yunchao Qu & Xingang Li & Jiancheng Long & Hai-Jun Huang, 2014. "Simulating the Dynamic Escape Process in Large Public Places," Operations Research, INFORMS, vol. 62(6), pages 1344-1357, December.
    8. Zhou, Zi-Xuan & Nakanishi, Wataru & Asakura, Yasuo, 2021. "Data-driven framework for the adaptive exit selection problem in pedestrian flow: Visual information based heuristics approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    9. Feliciani, Claudio & Jia, Xiaolu & Murakami, Hisashi & Ohtsuka, Kazumichi & Vizzari, Giuseppe & Nishinari, Katsuhiro, 2023. "Social groups in pedestrian crowds as physical and cognitive entities: Extent of modeling and motion prediction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    10. Hänseler, Flurin S. & Bierlaire, Michel & Farooq, Bilal & Mühlematter, Thomas, 2014. "A macroscopic loading model for time-varying pedestrian flows in public walking areas," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 60-80.
    11. Wang, Lei & Zhang, Qian & Cai, Yun & Zhang, Jianlin & Ma, Qingguo, 2013. "Simulation study of pedestrian flow in a station hall during the Spring Festival travel rush," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2470-2478.
    12. Johansson, Fredrik & Peterson, Anders & Tapani, Andreas, 2015. "Waiting pedestrians in the social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 95-107.
    13. Li, Maosheng & Shu, Panpan & Xiao, Yao & Wang, Pu, 2021. "Modeling detour decision combined the tactical and operational layer based on perceived density," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. 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.
    15. Kang, Zengxin & Zhang, Lei & Li, Kun, 2019. "An improved social force model for pedestrian dynamics in shipwrecks," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 355-362.
    16. Marija Nikolić & Michel Bierlaire & Matthieu de Lapparent & Riccardo Scarinci, 2019. "Multiclass Speed-Density Relationship for Pedestrian Traffic," Transportation Science, INFORMS, vol. 53(3), pages 642-664, May.
    17. Hu, Yanghui & Bi, Yubo & Ren, Xiangxia & Huang, Shenshi & Gao, Wei, 2023. "Experimental study on the impact of a stationary pedestrian obstacle at the exit on evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    18. Shi, Xiaomeng & Xue, Shuqi & Feliciani, Claudio & Shiwakoti, Nirajan & Lin, Junkai & Li, Dawei & Ye, Zhirui, 2021. "Verifying the applicability of a pedestrian simulation model to reproduce the effect of exit design on egress flow under normal and emergency conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    19. Constanza Flores & Han Soo Lee & Erick Mas, 2024. "Understanding Tsunami Evacuation via a Social Force Model While Considering Stress Levels Using Agent-Based Modelling," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    20. Tian, Xiaoyong & Li, Kun & Kang, Zengxin & Peng, Yun & Cui, Hongjun, 2020. "Simulating the dynamical features of evacuation governed by periodic vibrations," Chaos, Solitons & Fractals, Elsevier, vol. 139(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:427:y:2015:i:c:p:162-180. 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.