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A behavior-based model for pedestrian counter flow

Author

Listed:
  • Weng, W.G.
  • Shen, S.F.
  • Yuan, H.Y.
  • Fan, W.C.

Abstract

A behavior-based lattice-gas model for pedestrian dynamics is presented. This model adopts the behaviorism for mobile robot, and the walk task of pedestrian can be divided into three basic behaviors, i.e., “move”, “avoid”, and “swirl” basic behaviors. The walk direction is determined from the walk weight, which is the sum of the product of each vector of basic behavior multiplied by the weight in the corresponding direction. This model can simulate pedestrian movement with different walk velocities through the update at different time-step intervals. The periodic boundary for pedestrian counter flow with six simulation conditions in the channel is considered, and the dynamical characteristics are discussed. Simulation results show this presented behavior-based model can simulate some characteristics of pedestrian counter flow, e.g., lane formation and jammed configuration, etc. In addition, the different simulation conditions result in the different numbers of phases and their different critical total densities. In general, the mean flow 〈J〉 is always high if the corresponding mean velocity 〈V〉 is high, and their phases also turn at the same critical total density.

Suggested Citation

  • Weng, W.G. & Shen, S.F. & Yuan, H.Y. & Fan, W.C., 2007. "A behavior-based model for pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 668-678.
    • Handle: RePEc:eee:phsmap:v:375:y:2007:i:2:p:668-678
    DOI: 10.1016/j.physa.2006.09.028
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    Citations

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    Cited by:

    1. 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.
    2. Guo, Wei & Wang, Xiaolu & Zheng, Xiaoping, 2015. "Lane formation in pedestrian counterflows driven by a potential field considering following and avoidance behaviours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 87-101.
    3. Zeng, Yiping & Ye, Rui & Song, Weiguo & Luo, Shengfeng & Meng, Fanyu & Vizzari, Giuseppe, 2021. "Entropy analysis of the laminar movement in bidirectional pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    4. Guo, Xiwei & Chen, Jianqiao & Zheng, Yaochen & Wei, Junhong, 2012. "A heterogeneous lattice gas model for simulating pedestrian evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 582-592.
    5. 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.
    6. Rangel-Huerta, A. & Ballinas-Hernández, A.L. & Muñoz-Meléndez, A., 2017. "An entropy model to measure heterogeneity of pedestrian crowds using self-propelled agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 213-224.
    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.

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