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Experimental Study on Single-File Movement with Different Stop Distances

  • Qiao Wang
  • Weiguo SongEmail author
  • Jun Zhang
  • Liping Lian
  • Siuming Lo
Conference paper

Abstract

The single-file movement formed at exit, passageway, and stairway is a common and fundamental phenomenon in building evacuation when fire happened. In order to investigate single-file movement, the controlled experiments contained three parts: acceleration, steady state, and deceleration are effective research methods. In this paper, we conducted single-file movement experiments with two different commands in decelerating phase: (1) normal stop, (2) close stop, i.e., stop at the place as close as possible to the predecessor, in which participants move at different free moving speed. Through rescaling the speed, there is no influence for different free moving speed on speed–distance headway relationship. The linear fitting curve is executed to obtain a quantitative description of the speed–distance headway relation, and the slope in the close stop experiment is larger than that in the normal stop. It is found that there is a little difference in the decelerating phase for different participants and the average close stop distance is 0.34 m, which is a little bigger than average chest width 0.3 m. For the normal stop, the comfortable stop distance is dependent on individual proxemics. In the relation of speed–time, it was divided into two stages, which is fitted using linear regression. The value of negative acceleration in stage I is greater than stage II. Similar to the fitting result of speed–distance headway, participants in close stop experiments have bigger negative acceleration in stage I. However, in stage II, due to the uncertainty of close stop distance for different participants, the value of negative acceleration in close stop is smaller. Actually, in normal conditions, participants stopped with their comfortable stop distance when the predecessor stopped. However, in emergency or hurried conditions, the stop distance would be smaller. Therefore, it is useful to investigate the movement behavior in an emergency (such as fire and earthquake) and hurried conditions when the predecessor stopped suddenly.

Keywords

Single-file movement Stop distance Speed-headway relation Speed–time relation 

Nomenclature

hi(t)

Distance headway of pedestrian i at time t

xi(t)

X-axis coordinate of pedestrian’s head at time t

vi(t)

Speed of pedestrian i at time t

∆t

Time interval

a

Slope of linear fitting

b

Intercept of linear fitting

R2

Coefficient of determination

Subscripts

i

Pedestrian

Notes

Acknowledgements

This research was supported by Key Research and Development Program of China (2016YFC0802508), National Natural Science Foundation of China (51120165001), the National Basic Research Program of China (2012CB719705), Specialized Research Fund for the Doctoral Program of Higher Education of China (20133402110009), and Fundamental Research Funds for the Central Universities (WK2320000035). All authors carried out the research together. Qiao Wang, Weiguo Song and Jun Zhang had the original idea for the research. Qiao Wang was responsible for organization of the research participants, data analyses and the manuscript drafted. Lo Siuming and Liping Lian provided research methodology assistance and language help. All authors read, revised the manuscript, and approved the final manuscript.

References

  1. 1.
    Cao, S., Song, W., Lv, W., & Fang, Z. (2015). A multi-grid model for pedestrian evacuation in a room without visibility. Physica A: Statistical Mechanics and its Applications, 45–61.Google Scholar
  2. 2.
    Tian, W., Song, W., Lü, W., & Fang, Z. (2011). Experiment and analysis on microscopic characteristics of pedestrian movement in building bottleneck. Science China Technological Sciences, 1730–1736.Google Scholar
  3. 3.
    Seyfried, A., Boltes, M., Kähler, J., Klingsch, W., Portz, A., Rupprecht, T., Schadschneider, A., Steffen, B., Winkens, A. (2010). Enhanced empirical data for the fundamental diagram and the flow through bottlenecks. In Pedestrian and Evacuation Dynamics 2008, Springer, pp. 145–156.Google Scholar
  4. 4.
    Zhang, J., Song, W., & Xu, X. (2008) Experiment and multi-grid modeling of evacuation from a classroom. Physica A: Statistical Mechanics and its Applications, 5901–5909.Google Scholar
  5. 5.
    Seyfried, A., Steffen, B., Klingsch, W., & Boltes, M. (2005) The fundamental diagram of pedestrian movement revisited. Journal of Statistical Mechanics: Theory and Experiment, P10002.Google Scholar
  6. 6.
    Fang, Z.-M., Song, W.-G., Liu, X., Lv, W., Ma, J., & Xiao, X. (2012). A continuous distance model (CDM) for the single-file pedestrian movement considering step frequency and length. Physica A: Statistical Mechanics and its Applications, 307–316.Google Scholar
  7. 7.
    Lv, W., Fang, Z., Wei, X., Song, W., & Liu, X. (2013). Experiment and modelling for pedestrian following behavior using velocity-headway relation. Procedia Engineering, 525–531.Google Scholar
  8. 8.
    Cao, S., Zhang, J., Salden, D., Ma, J., Shi, C., & Zhang, R. (2016). Pedestrian dynamics in single-file movement of crowd with different age compositions. Physical Review E, 012312.Google Scholar
  9. 9.
    Zhao, Y., & Zhang, H. (2017). A unified follow-the-leader model for vehicle, bicycle and pedestrian traffic. Transportation Research Part B: Methodological, 315–327.Google Scholar
  10. 10.
    Tadaki, S., Kikuchi, M., Fukui, M., Nakayama, A., Nishinari, K., Shibata, A., Sugiyama, Y., Yosida, T., & Yukawa, S. (2013). Phase transition in traffic jam experiment on a circuit. New Journal of Physics, 103034.Google Scholar
  11. 11.
    Liu, X., Song, W., & Zhang, J. (2009). Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing. Physica A: Statistical Mechanics and its Applications, 2717–2726.Google Scholar
  12. 12.
    Buchin, K., Buchin, M., & Gudmundsson, J. (2008). Detecting single file movement. In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2008 (p. 33).Google Scholar
  13. 13.
    Chattaraj, U., Seyfried, A., Chakroborty, P., & Biswal, M. K. (2013). Modelling single file pedestrian motion across cultures. Procedia-Social and Behavioral Sciences, 698–707.Google Scholar
  14. 14.
    Wang, S., Lv, W., & Song, W. (2015). Behavior of ants escaping from a single-exit room. Plos One.Google Scholar
  15. 15.
    Jelić, A., Appert-Rolland, C., Lemercier, S., & Pettré, J. (2012). Properties of pedestrians walking in line: Fundamental diagrams. Physical review E, 036111.Google Scholar
  16. 16.
    Jelić, A., Appert-Rolland, C., Lemercier, S., & Pettré, J. (2012). Properties of pedestrians walking in line. II. Stepping behavior. Physical Review E, 046111.Google Scholar
  17. 17.
    Chattaraj, U., Seyfried, A., & Chakroborty, P. (2009 Comparison of pedestrian fundamental diagram across cultures. Advances in Complex Systems, 393–405.Google Scholar
  18. 18.
    Sun, J., Lu, S., Lo, S., Ma, J., & Xie, Q. (2018). Moving characteristics of single file passengers considering the effect of ship trim and heeling. Physica A: Statistical Mechanics and its Applications, 476–487.Google Scholar
  19. 19.
    Schadschneider, A. (1999). The nagel-schreckenberg model revisited. The European Physical Journal B-Condensed Matter and Complex Systems, 573–582.CrossRefGoogle Scholar
  20. 20.
    Gazis, D. C., Herman, R., & Rothery, R. W. (1961). Nonlinear follow-the-leader models of traffic flow. Operations Research, 545–567.Google Scholar
  21. 21.
    Davis, L. (2003). Modifications of the optimal velocity traffic model to include delay due to driver reaction time. Physica A: Statistical Mechanics and its Applications, 557–567.CrossRefGoogle Scholar
  22. 22.
    Hall, E. T. (1966). The hidden dimension. Google Scholar
  23. 23.
    Helbing, D., Farkas, I. J., Vicsek, T., (2000). Simulating dynamical features of escape panic. Nature, 487–490.CrossRefGoogle Scholar
  24. 24.
    Boltes, M., & Seyfried, A. (2013). Collecting pedestrian trajectories, Neurocomputing, 127–133.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Qiao Wang
    • 1
    • 2
  • Weiguo Song
    • 1
    Email author
  • Jun Zhang
    • 1
  • Liping Lian
    • 1
    • 2
  • Siuming Lo
    • 2
  1. 1.State Key Laboratory of Fire ScienceUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of Civil and Architectural EngineeringCity University of Hong KongKowloonHong Kong

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