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Studying the Impact of Trained Staff on Evacuation Scenarios by Agent-Based Simulation

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Abstract

Human evacuation experiments can trigger distress, be unethical and present high costs. As a solution, computer simulations can predict the effectiveness of new emergency management procedures. This paper applies multi-agent simulation to measure the influence of staff members with diverse training levels on evacuation time. A previously developed and validated model was extended with explicit mechanisms to simulate staff members helping people to egress. The majority of parameter settings have been based on empirical data acquired in earlier studies. Therefore, simulation results are expected to be realistic. Results show that staff are more effective in complex environments, especially when trained. Not only specialised security professionals but, especially, regular workers of shopping facilities and offices play a significant role in evacuation processes when adequately trained. These results can inform policy makers and crowd managers on new emergency management procedures.

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Notes

  1. 1.

    https://www.cbsnews.com/news/major-soccer-stadium-disasters/.

  2. 2.

    http://news.bbc.co.uk/1/hi/world/europe/356828.stm.

  3. 3.

    https://en.wikipedia.org/wiki/The_Station_nightclub_fire.

  4. 4.

    Source code available to download in: (https://formolo.wixsite.com/impact-project).

  5. 5.

    http://fseg.gre.ac.uk/exodus/index.html.

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Acknowledgments

This project has received funding from the European Union’s Horizon 2020; innovation programme under the Marie Skłodowska-Curie grant agreement No. 748647 and Brazilian government - Science without Borders – CNPq (No: 233883/2014-2). We would like to thank our Consortium Partners and stakeholders for their input.

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Correspondence to Daniel Formolo .

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Formolo, D., Bosse, T., van der Wal, N. (2018). Studying the Impact of Trained Staff on Evacuation Scenarios by Agent-Based Simulation. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11186. Springer, Cham. https://doi.org/10.1007/978-3-030-01159-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-01159-8_8

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