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Influence of walkway slope on single-file pedestrian flow dynamics: Results from an experimental study

Author

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  • Wei, Yidong
  • Hu, Zuoan
  • Zeng, Tian
  • Xie, Wei
  • Ma, Yi

Abstract

Sloping walkways are common pedestrian traffic facilities in modern cities, especially, in some cities located in mountainous terrain, such as Hong Kong, and Zurich. Compared with level walkways, sloping walkways may lead to the decrease in pedestrian flow efficiency, and are more prone to trigger crowd disasters at extreme crowd densities, for example, recent crowd stampede event in Itaewon, South Korea, did happen on a narrow ramp with an extreme crowd density. Therefore, it is important to study pedestrian flow dynamics on ramps. In this paper, we conducted single-file pedestrian flow experiments in 0°, 3°, 5°, 7°, 9°, 12°, 17°, 22°, 27° uphill and downhill conditions, and analyzed contrastively the influence of walkway slope on pedestrian flow dynamics. Our main findings are as follows. (i) Pedestrian flow efficiency is the highest in 7° downhill condition, indicating that a small downhill slope is advantageous to pedestrian flowing. (ii) Whether it is uphill or downhill, the ramp steeper than 7° will significantly decrease pedestrian flow efficiency, therefore is disadvantageous to pedestrian flowing. (iii) For a ramp, uphill and downhill pedestrian flow efficiencies are not equal. Downhill pedestrian flow efficiency is always larger than uphill pedestrian flow efficiency. These findings provide important guidance for the design of sloping walkways and the safety management of pedestrian crowds on sloping walkways.

Suggested Citation

  • Wei, Yidong & Hu, Zuoan & Zeng, Tian & Xie, Wei & Ma, Yi, 2023. "Influence of walkway slope on single-file pedestrian flow dynamics: Results from an experimental study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    • Handle: RePEc:eee:phsmap:v:630:y:2023:i:c:s0378437123007951
    DOI: 10.1016/j.physa.2023.129240
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    References listed on IDEAS

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    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. Fu, Zhijian & Li, Tao & Deng, Qiangqiang & Schadschneider, Andreas & Luo, Lin & Ma, Jian, 2021. "Effect of turning curvature on the single-file dynamics of pedestrian flow: An experimental study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    3. Zhao, Yongxiang & Zhang, H.M., 2017. "A unified follow-the-leader model for vehicle, bicycle and pedestrian traffic," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 315-327.
    4. 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.
    5. Yi Ma & Eric Wai Ming Lee & Meng Shi & Richard Kwok Kit Yuen, 2021. "Spontaneous synchronization of motion in pedestrian crowds of different densities," Nature Human Behaviour, Nature, vol. 5(4), pages 447-457, April.
    6. Haghani, Milad & Sarvi, Majid, 2018. "Crowd behaviour and motion: Empirical methods," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 253-294.
    7. Zeng, Guang & Cao, Shuchao & Liu, Chi & Song, Weiguo, 2018. "Experimental and modeling study on relation of pedestrian step length and frequency under different headways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 237-248.
    8. 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.
    9. Jin, Cheng-Jie & Jiang, Rui & Li, Ruiwen & Li, Dawei, 2019. "Single-file pedestrian flow experiments under high-density conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    10. 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.
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