Compression of Pedestrian Crowd in Corner Turning Subject experiment-based analysis of walking trajectories
DOI:
https://doi.org/10.17815/CD.2020.33Keywords:
experiment, crowd dynamics, trajectory, data-setAbstract
In this study, pedestrian crowd dynamics at corner turns were investigated by analyzing pedestrian trajectories in a subject experiment for building more reliable, general-purpose, pedestrian simulation models. An experiment under laboratory conditions was conducted wherein a pedestrian crowd walked straight for a short distance before turning into a right-angled corner built with partition walls; the opposite sides were unwalled. Trials were performed with different widths and densities of initial participant positions. Finally, the trajectories of the pedestrians were extracted from a video through computer image analysis. The results demonstrated that pedestrian behavior at corner turns depends on lane position, lane distance (from the wall), and crowd density.References
M. Chraibi, M. Freialdenhoven, A. Schadschneider and A. Seyfried, “Modeling the desired direction
in a force-based model for pedestrian dynamics,” in Traffic and Granular Flow ’11, V. Kozlov, A.
Buslaev, A. Bugaev, M. Yashina, A. Schadschneider, M. Schreckenberg, Ed. Berlin: Springer, 2013.
C. Dias, M. Sarvi, N. Shiwakoti, and O. Ejtemai, “Experimental Study on Pedestrian Walking
Characteristics through Angled Corridors,” in Proceedings of 2013 Australasian Transport Research
Forum, 2013, pp.1–11.
C. Dias, O. Ejtemai, M. Sarvi, and M. Burd, “Exploring pedestrian walking through angled corridors,”
Transportation Research Procedia, 2014, vol. 2, pp.19–25.
M. Tange, M. Imanishi, T. Sano, and Y. Ohmiya, “Pedestrian Tracking with Two Different Color
Labels for Large Scale Evacuation Experiments,” in 12th International Symposium of Fire Safety
Science: Book of abstracts posters, Art. P109, June 2017.
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