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Simulation of bi-direction pedestrian movement in corridor

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  • Jian, Li
  • Lizhong, Yang
  • Daoliang, Zhao

Abstract

The pedestrian movement is more complex than vehicular flow for the reason that people are more flexible and intelligent than cars. Without the limit of “lanes” pedestrian movement is loose and free. Furthermore, they are easily affected by other walkers as well as the environment around. In this paper, a special technique is introduced considering human behavior to make the rules more reasonable. By simulating the two-dimension pedestrian movement in corridor, the phase transition phenomena of pedestrian movement, including the walkers moving bottom-up and top-down, are presented. Studying on the effect of possibility of exchange position between face-to-face pedestrians propose that the considerable exchange possibility is about 0.20 in the scope studied in this paper.

Suggested Citation

  • Jian, Li & Lizhong, Yang & Daoliang, Zhao, 2005. "Simulation of bi-direction pedestrian movement in corridor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 619-628.
    • Handle: RePEc:eee:phsmap:v:354:y:2005:i:c:p:619-628
    DOI: 10.1016/j.physa.2005.03.007
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    References listed on IDEAS

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    Cited by:

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    2. Feliciani, Claudio & Nishinari, Katsuhiro, 2016. "An improved Cellular Automata model to simulate the behavior of high density crowd and validation by experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 135-148.
    3. Yue, Hao & Guan, Hongzhi & Zhang, Juan & Shao, Chunfu, 2010. "Study on bi-direction pedestrian flow using cellular automata simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 527-539.
    4. Goldsztein, Guillermo H., 2017. "Crowd of individuals walking in opposite directions. A toy model to study the segregation of the group into lanes of individuals moving in the same direction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 162-173.
    5. Ma, Jian & Song, Wei-guo & Zhang, Jun & Lo, Siu-ming & Liao, Guang-xuan, 2010. "k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2101-2117.
    6. Yue, Hao & Hao, Herui & Chen, Xiaoming & Shao, Chunfu, 2007. "Simulation of pedestrian flow on square lattice based on cellular automata model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 567-588.
    7. Fang, Jun & Qin, Zheng & Hu, Hao & Xu, Zhaohui & Li, Huan, 2012. "The fundamental diagram of pedestrian model with slow reaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6112-6120.
    8. Yamamoto, Kazuhiro & Kokubo, Satoshi & Nishinari, Katsuhiro, 2007. "Simulation for pedestrian dynamics by real-coded cellular automata (RCA)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 654-660.
    9. Moonsoo Ko & Taewan Kim & Keemin Sohn, 2013. "Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation," Transportation, Springer, vol. 40(1), pages 91-107, January.
    10. Zhang, Dawei & Zhu, Haitao & Hostikka, Simo & Qiu, Shi, 2019. "Pedestrian dynamics in a heterogeneous bidirectional flow: Overtaking behaviour and lane formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 72-84.
    11. Tipakornkiat, Chalat & Limanond, Thirayoot & Kim, Hyunmyung, 2012. "Determining an influencing area affecting walking speed on footpath: A case study of a footpath in CBD Bangkok, Thailand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5453-5464.
    12. Zhou, Xuemei & Hu, Jingjie & Ji, Xiangfeng & Xiao, Xiongziyan, 2019. "Cellular automaton simulation of pedestrian flow considering vision and multi-velocity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 982-992.

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