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Quantification of the level of crowdedness for pedestrian movements

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  • 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
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    References listed on IDEAS

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    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.
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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).
    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.

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