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Modeling emergency evacuation for major hazard industrial sites

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  • Georgiadou, Paraskevi S.
  • Papazoglou, Ioannis A.
  • Kiranoudis, Chris T.
  • Markatos, Nikolaos C.

Abstract

A model providing the temporal and spatial distribution of the population under evacuation around a major hazard facility is developed. A discrete state stochastic Markov process simulates the movement of the evacuees. The area around the hazardous facility is divided into nodes connected among themselves with links representing the road system of the area. Transition from node-to-node is simulated as a random process where the probability of transition depends on the dynamically changed states of the destination and origin nodes and on the link between them. Solution of the Markov process provides the expected distribution of the evacuees in the nodes of the area as a function of time. A Monte Carlo solution of the model provides in addition a sample of actual trajectories of the evacuees. This information coupled with an accident analysis which provides the spatial and temporal distribution of the extreme phenomenon following an accident, determines a sample of the actual doses received by the evacuees. Both the average dose and the actual distribution of doses are then used as measures in evaluating alternative emergency response strategies. It is shown that in some cases the estimation of the health consequences by the average dose might be either too conservative or too non-conservative relative to the one corresponding to the distribution of the received dose and hence not a suitable measure to evaluate alternative evacuation strategies.

Suggested Citation

  • Georgiadou, Paraskevi S. & Papazoglou, Ioannis A. & Kiranoudis, Chris T. & Markatos, Nikolaos C., 2007. "Modeling emergency evacuation for major hazard industrial sites," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1388-1402.
  • Handle: RePEc:eee:reensy:v:92:y:2007:i:10:p:1388-1402
    DOI: 10.1016/j.ress.2006.09.009
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    Citations

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    Cited by:

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    6. Seo, Seung-Kwon & Yoon, Young-Gak & Lee, Ju-sung & Na, Jonggeol & Lee, Chul-Jin, 2022. "Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Liu, Yu & Wang, Weijie & Huang, Hong-Zhong & Li, Yanfeng & Yang, Yuanjian, 2014. "A new simulation model for assessing aircraft emergency evacuation considering passenger physical characteristics," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 187-197.
    8. Zheng, Xiaoping & Liu, Mengting, 2010. "Forecasting model for pedestrian distribution under emergency evacuation," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1186-1192.
    9. Gai, Wen-mei & Deng, Yun-feng & Jiang, Zhong-an & Li, Jing & Du, Yan, 2017. "Multi-objective evacuation routing optimization for toxic cloud releases," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 58-68.
    10. Lovreglio, Ruggiero & Spearpoint, Michael & Girault, Mathilde, 2019. "The impact of sampling methods on evacuation model convergence and egress time," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 24-34.
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