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A cellular automaton model for evacuation flow using game theory

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  • Guan, Junbiao
  • Wang, Kaihua
  • Chen, Fangyue

Abstract

Game theory serves as a good tool to explore crowd dynamic conflicts during evacuation processes. The purpose of this study is to simulate the complicated interaction behavior among the conflicting pedestrians in an evacuation flow. Two types of pedestrians, namely, defectors and cooperators, are considered, and two important factors including fear index and cost coefficient are taken into account. By combining the snowdrift game theory with a cellular automaton (CA) model, it is shown that the increase of fear index and cost coefficient will lengthen the evacuation time, which is more apparent for large values of cost coefficient. Meanwhile, it is found that the defectors to cooperators ratio could always tend to consistent states despite different values of parameters, largely owing to self-organization effects.

Suggested Citation

  • Guan, Junbiao & Wang, Kaihua & Chen, Fangyue, 2016. "A cellular automaton model for evacuation flow using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 655-661.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:655-661
    DOI: 10.1016/j.physa.2016.05.062
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    References listed on IDEAS

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    Cited by:

    1. Li, Xingli & Guo, Fang & Kuang, Hua & Geng, Zhongfei & Fan, Yanhong, 2019. "An extended cost potential field cellular automaton model for pedestrian evacuation considering the restriction of visual field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 47-56.
    2. Guan, Junbiao & Wang, Kaihua, 2019. "Towards pedestrian room evacuation with a spatial game," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 492-501.
    3. Chaoyu Zheng & Benhong Peng & Xin Sheng & Anxia Wan, 2021. "Haze risk: information diffusion based on cellular automata," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2605-2623, July.
    4. Geng, Zhongfei & Li, Xingli & Kuang, Hua & Bai, Xuecen & Fan, Yanhong, 2019. "Effect of uncertain information on pedestrian dynamics under adverse sight conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 681-691.
    5. Can Liao & Kejun Zhu & Haixiang Guo & Jian Tang, 2019. "Simulation Research on Safe Flow Rate of Bidirectional Crowds Using Bayesian-Nash Equilibrium," Complexity, Hindawi, vol. 2019, pages 1-15, January.
    6. Cui, Geng & Yanagisawa, Daichi & Nishinari, Katsuhiro, 2021. "Incorporating genetic algorithm to optimise initial condition of pedestrian evacuation based on agent aggressiveness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    7. Tian, Huan-huan & Wei, Yan-fang & Dong, Li-yun & Xue, Yu & Zheng, Rong-sen, 2018. "Resolution of conflicts in cellular automaton evacuation model with the game-theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 991-1006.
    8. Li, Shuang & Yu, Xiaohui & Zhang, Yanjuan & Zhai, Changhai, 2018. "A numerical simulation strategy on occupant evacuation behaviors and casualty prediction in a building during earthquakes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1238-1250.
    9. Guan, Junbiao & Wang, Kaihua, 2020. "Cooperative evolution in pedestrian room evacuation considering different individual behaviors," Applied Mathematics and Computation, Elsevier, vol. 369(C).

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