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Combining Avoidance and Imitation to Improve Multi-agent Pedestrian Simulation

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AI*IA 2016 Advances in Artificial Intelligence (AI*IA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10037))

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Abstract

Simulation of pedestrian and crowd dynamics is a consolidated application of agent-based models but it still presents room for improvement. Wayfinding, for instance, is a fundamental task for the application of such models on complex environments, but it still requires both empirical evidences as well as models better reflecting them. In this paper, a novel model for the simulation of pedestrian wayfinding is discussed: it is aimed at providing general mechanisms that can be calibrated to reproduce specific empirical evidences like a proxemic tendency to avoid congestion, but also an imitation mechanism to stimulate the exploitation of longer but less congested paths explored by emerging leaders. A demonstration of the simulated dynamics on a large scale scenario will be illustrated in the paper and the achieved results will show the achieved improvements compared to a basic floor field Cellular Automata model.

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Notes

  1. 1.

    The travel time that the agent can employ without encountering any congestion in the path, thus moving at its free flow speed.

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Authors and Affiliations

  1. Complex Systems and Artificial Intelligence Research Centre, University of Milano-Bicocca, Viale Sarca 336/14, 20126, Milan, Italy

    Luca Crociani, Giuseppe Vizzari & Stefania Bandini

  2. Researche Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

    Stefania Bandini

Authors
  1. Luca Crociani
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  2. Giuseppe Vizzari
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  3. Stefania Bandini
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Corresponding author

Correspondence to Giuseppe Vizzari .

Editor information

Editors and Affiliations

  1. University of Genoa , Genova, Italy

    Giovanni Adorni

  2. University of Parma , Parma, Italy

    Stefano Cagnoni

  3. University of Siena , Siena, Italy

    Marco Gori

  4. University of Genova , Genova, Italy

    Marco Maratea

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Crociani, L., Vizzari, G., Bandini, S. (2016). Combining Avoidance and Imitation to Improve Multi-agent Pedestrian Simulation. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-49130-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49129-5

  • Online ISBN: 978-3-319-49130-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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