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Acquisition of Local Neighbor Rules in the Simulation of Pedestrian Flow by Cellular Automata

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Cellular Automata (ACRI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3305))

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Abstract

Cellular Automata is applied to model the pedestrian flow, in which the local neighbor rules implemented to each person in the crowd are determined automatically in the process of simulation. The collision patterns in the visible area settled around each person is introduced, and each person walks around just avoiding the collision patterns. As the simulation proceeds along the time step, various collision patterns are observed. By being informed the collision patterns to all the pedestrians simultaneously, the pedestrian begins to flow quite smoothly and the column formation is observed.

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Author information

Authors and Affiliations

  1. Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan

    Katsutoshi Narimatsu, Toshihiko Shiraishi & Shin Morishita

Authors
  1. Katsutoshi Narimatsu
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  2. Toshihiko Shiraishi
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  3. Shin Morishita
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Editor information

Editors and Affiliations

  1. Faculty of Sciences, Section of Computational Science, University of Amsterdam, Kruislaan 403, 1098 SJ, Amsterdam, The Netherlands

    Peter M. A. Sloot

  2. Computer Science Department, University of Geneva, Switzerland

    Bastien Chopard

  3. Section Computational Science, University of Amsterdam, The Netherlands

    Alfons G. Hoekstra

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© 2004 Springer-Verlag Berlin Heidelberg

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Narimatsu, K., Shiraishi, T., Morishita, S. (2004). Acquisition of Local Neighbor Rules in the Simulation of Pedestrian Flow by Cellular Automata. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds) Cellular Automata. ACRI 2004. Lecture Notes in Computer Science, vol 3305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30479-1_22

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  • DOI: https://doi.org/10.1007/978-3-540-30479-1_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23596-5

  • Online ISBN: 978-3-540-30479-1

  • eBook Packages: Springer Book Archive

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