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Detection of Primitive Collective Behaviours in a Crowd Panic Simulation Based on Multi-Agent Approach

Author

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  • Jérémy Patrix

    (CASSIDIAN SAS & University of Caen-Basse-Normandie, France)

  • Abdel-Illah Mouaddib

    (GREYC (UMR 6072), University of Caen-Basse-Normandie, France)

  • Sylvain Gatepaille

    (CASSIDIAN SAS, IPCC department of Val-de-Reuil, France)

Abstract

In case of emergency and evacuation, it is often impossible to interpret manually the complex behaviour of a crowd, essentially due to the lack of staff and time needed to understand a situation. In the literature, a monitored system using data fusion methods makes it possible to perform automatic situation awareness. Using Swarm Intelligence domain, the authors propose an approach based on multi-agent system to simulate and detect primitive collective behaviours emerging from a crowd panic. It enables anticipating collective behaviours in real-time as well as their anomalies according to specific scenarios. Detection is the possibility to learn, recognize and anticipate different behaviours by a probabilistic model. The collective behaviour detection of a crowd panic in real-time is based on a learning method on an extended model of Hidden Markov Model. This paper presents experiments of simulation and detection using an implementation of a virtual environment.

Suggested Citation

  • Jérémy Patrix & Abdel-Illah Mouaddib & Sylvain Gatepaille, 2012. "Detection of Primitive Collective Behaviours in a Crowd Panic Simulation Based on Multi-Agent Approach," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 3(3), pages 50-65, July.
    • Handle: RePEc:igg:jsir00:v:3:y:2012:i:3:p:50-65
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    Cited by:

    1. Qing Yang & Ying Sun & Xingxing Liu & Jinmei Wang, 2020. "MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China," Sustainability, MDPI, vol. 12(2), pages 1-19, January.

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