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Towards real-time human participation in virtual evacuation through a validated simulation tool

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  • Gabriele Montecchiari
  • Gabriele Bulian
  • Paolo Gallina

Abstract

The analysis of the ship layout from the point of view of safe and orderly evacuation represents an important step in ship design, which can be carried out through agent-based evacuation simulation tools, typically run in batch mode. Introducing the possibility for humans to interactively participate in a simulated evacuation process together with computer-controlled agents can open a series of interesting possibilities for design, research and development. To this aim, this article presents the development of a validated agent-based evacuation simulation tool which allows real-time human participation through immersive virtual reality. The main characteristics of the underlying social-force-based modelling technique are described. The tool is verified and validated by making reference to International Maritime Organization test cases, experimental data and FDS + Evac simulations. The first approach for supporting real-time human participation is then presented. An initial experiment embedding immersive virtual reality human participation is described, together with outcomes regarding comparisons between human-controlled avatars and computer-controlled agents. Results from this initial testing are encouraging in pursuing the use of virtual reality as a tool to obtain information on human behaviour during evacuation.

Suggested Citation

  • Gabriele Montecchiari & Gabriele Bulian & Paolo Gallina, 2018. "Towards real-time human participation in virtual evacuation through a validated simulation tool," Journal of Risk and Reliability, , vol. 232(5), pages 476-490, October.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:5:p:476-490
    DOI: 10.1177/1748006X17705046
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    References listed on IDEAS

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    1. Ujjal Chattaraj & Armin Seyfried & Partha Chakroborty, 2009. "Comparison Of Pedestrian Fundamental Diagram Across Cultures," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 393-405.
    2. Yang, Lizhong & Li, Jian & Liu, Shaobo, 2008. "Simulation of pedestrian counter-flow with right-moving preference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3281-3289.
    3. Isobe, Motoshige & Adachi, Taku & Nagatani, Takashi, 2004. "Experiment and simulation of pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 638-650.
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