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Replacing Method for Multi-Agent Crowd Simulation by Convolutional Neural Network

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Multi-Agent-Based Simulation XXIII (MABS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13743))

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Abstract

Multi-agent crowd simulations are used to analyze crowd flows. However, there is a critical problem that the computational time increases with the number of agents because these simulations are based on the interaction of the agents. The approach of using deep neural networks is effective in some applications, such as fluid dynamics simulations. We propose a method of using convolutional neural networks to estimate simulation results from the conditions of each agent and the initial arrangement of a crowd. We evaluated our proposed method through evacuation simulation and demonstrated that our proposed method could obtain evacuation simulation results with high speed.

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Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Yu Yamashita, Shunki Takami, Shusuke Shigenaka & Atsuyuki Morishima

  2. National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

    Yu Yamashita, Shunki Takami, Shusuke Shigenaka & Masaki Onishi

Authors
  1. Yu Yamashita
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  2. Shunki Takami
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  3. Shusuke Shigenaka
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  4. Masaki Onishi
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  5. Atsuyuki Morishima
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Corresponding author

Correspondence to Yu Yamashita .

Editor information

Editors and Affiliations

  1. Malmö University, Malmö, Sweden

    Fabian Lorig

  2. University of Sheffield, Sheffield, UK

    Emma Norling

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Yamashita, Y., Takami, S., Shigenaka, S., Onishi, M., Morishima, A. (2023). Replacing Method for Multi-Agent Crowd Simulation by Convolutional Neural Network. In: Lorig, F., Norling, E. (eds) Multi-Agent-Based Simulation XXIII. MABS 2022. Lecture Notes in Computer Science(), vol 13743. Springer, Cham. https://doi.org/10.1007/978-3-031-22947-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-22947-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22946-6

  • Online ISBN: 978-3-031-22947-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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