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Analysis of evacuation simulation considering crowd density and the effect of a fallen person

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Abstract

Simulation methods that can overcome the limitations of theory and experiment are widely used to predict disaster situations and analyze evacuation safety. To obtain more accurate and realistic simulation results, various situations that arise when people evacuate must be reflected. In this research, regression analysis is applied to existing research on walking speed changes according to crowd density, and we suggest a new equation for walking speed changes of people during evacuation. The suggested equation is applied to crowd evacuation simulations based on the actual viewing angle of a person. In addition, we developed a fallen person algorithm and applied it to examples from the Society of Fire Protection Engineers (SFPE) to analyze the effect a fallen person has on evacuation. Through this research, evacuation safety analysis can be performed for cases involving falling people, and it will be possible to design buildings that can minimize risk and evacuation time in case of an actual emergency situation by analyzing the optimum measures more strictly.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018025409) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20168520021200).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

    Hyuncheol Kim & Soonhung Han

  2. Korea Advanced Institute of Science and Technology, Initiative for Disaster Studies, Daejeon, Republic of Korea

    Jaeho Han

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  1. Hyuncheol Kim
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  2. Jaeho Han
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  3. Soonhung Han
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Correspondence to Hyuncheol Kim.

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Kim, H., Han, J. & Han, S. Analysis of evacuation simulation considering crowd density and the effect of a fallen person. J Ambient Intell Human Comput 10, 4869–4879 (2019). https://doi.org/10.1007/s12652-019-01184-7

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