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Ex-ante online risk assessment for building emergency evacuation through multimedia data

Author

Listed:
  • Haoran Zhang
  • Xuan Song
  • Xiaoya Song
  • Dou Huang
  • Ning Xu
  • Ryosuke Shibasaki
  • Yongtu Liang

Abstract

Ex-ante online risk assessment for building emergency evacuation is essential to protect human life and property. Current risk assessment methods are limited by the tradeoff between accuracy and efficiency. In this paper, we propose an online method that overcomes this tradeoff based on multimedia data (e.g. videos data from surveillance cameras) and deep learning. The method consists of two parts. The first estimates the evacuee position as input for training the assessment model to then perform risk assessment in real scenarios. The second considers a social force model based on the evacuation simulation for the output of training model. We verify the proposed method in simulation and real scenarios. Model sensitivity analyses and large-scale tests demonstrate the usability and superiority of the proposed method. By the method, the computation time of risk assessment could be decreased from 10 minutes (by traditional simulation method) to 2.18 s.

Suggested Citation

  • Haoran Zhang & Xuan Song & Xiaoya Song & Dou Huang & Ning Xu & Ryosuke Shibasaki & Yongtu Liang, 2019. "Ex-ante online risk assessment for building emergency evacuation through multimedia data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    • Handle: RePEc:plo:pone00:0215149
    DOI: 10.1371/journal.pone.0215149
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    References listed on IDEAS

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    1. Ning Ding & Hui Zhang & Tao Chen, 2017. "Simulation-based optimization of emergency evacuation strategy in ultra-high-rise buildings," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1167-1184, December.
    2. Xiujuan Zhao & Wei Xu & Yunjia Ma & Fuyu Hu, 2015. "Scenario-Based Multi-Objective Optimum Allocation Model for Earthquake Emergency Shelters Using a Modified Particle Swarm Optimization Algorithm: A Case Study in Chaoyang District, Beijing, China," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
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