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Forecasting model for pedestrian distribution under emergency evacuation

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  • Zheng, Xiaoping
  • Liu, Mengting

Abstract

Pedestrian distribution forecasting on the road network is developed to support the evacuation decision-making. The numbers of evacuees distributed on each road link are stochastic, uncertain and multi-dependent. Therefore, a Gaussian Bayesian networks (GBN) based forecasting model is presented, considering the pedestrian flow characteristics, optimization of evacuation route and evacuation decision-making. In the forecasting model, the route choice probabilities obtained by minimizing evacuation time are applied to correct the regression coefficients of GBN. Finally, an example is provided to illustrate the usefulness of this model. Research shows that this model not only reflects the complexity and dynamics of evacuation process but also performs an accurate forecasting on the time development of the pedestrian distributed in the evacuation space.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:11:p:1186-1192
    DOI: 10.1016/j.ress.2010.07.005
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    References listed on IDEAS

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    1. Hoogendoorn, Serge P. & Bovy, Piet H. L., 2004. "Dynamic user-optimal assignment in continuous time and space," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 571-592, August.
    2. 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.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. Fang, Zhi-Ming & Lv, Wei & Jiang, Li-Xue & Xu, Qing-Feng & Song, Wei-Guo, 2016. "Modeling and assessment of civil aircraft evacuation based on finer-grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 102-112.
    5. Lv, Y. & Yan, X.D. & Sun, W. & Gao, Z.Y., 2015. "A risk-based method for planning of bus–subway corridor evacuation under hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 188-199.
    6. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Teichmann, Dusan & Dorda, Michal & Sousek, Radovan, 2021. "Creation of preventive mass evacuation plan with the use of public transport," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    8. MacGregor Smith, J. & Cruz, F.R.B., 2014. "M/G/c/c state dependent travel time models and properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 560-579.

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