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Chinese Science Bulletin

, Volume 55, Issue 10, pp 1000–1006 | Cite as

An evacuation risk assessment model for emergency traffic with consideration of urban hazard installations

  • Qiang Li
  • Xiang Chen
  • Jin ChenEmail author
  • Qiao Tang
Articles Mechanical Engineering

Abstract

The Critical Cluster Model (CCM) is an optimization model assessing the evacuation risk on the scale of neighborhoods. The static evacuation risk in the CCM is measured by Bulk Lane Demand (BLD) — an index that solely depends on network structure and population of evacuees. The advantage of the CCM is having less input parameters and with relatively smaller computational cost. Moreover, the process of risk assessment by the CCM is a global optimization process. For this reason, the CCM provides a relatively ideal solution for planning emergency traffic evacuation in open spaces. Considering that hazard installations in urban areas are becoming an increasing threat to urban safety, in the paper we proposed an evacuation risk assessment model with consideration of such installations. This model was developed on the basis of the CCM by introducing two important factors: the accident risk impact factor which was negatively correlated with distance, representing the impact of hazard installation; the evacuation direction which was under the consideration of evacuating away from the hazard installation, providing feasible evacuation routes. Finally, an application of the new model was presented for Beijing, China with the support of Geographical Information System (GIS).

Keywords

urban public safety Critical Cluster Model (CCM) hazard installation traffic evacuation risk Geographical Information System (GIS) 

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References

  1. 1.
    Zeng X Y, Li L P, Deng S G. Research on urban public security and disaster reduction countermeasures. Work Safety & Supervision, 2006: 44–46Google Scholar
  2. 2.
    Li T. Risk management of urban public security. China Saf Sci J, 2008, 18: 65–72Google Scholar
  3. 3.
    Kisko T M, Francis R L. EVACNET+: A computer program to determine optimal evacuation plans. Fire Saf J, 1985, 9: 211–220CrossRefGoogle Scholar
  4. 4.
    Ketchell N, Cole S S, Webber D M. The EGRESS code for human movement and behavior in emergency evacuation. In: Smith R A, Dickie J F, eds. Engineering for Crowd Safety. New York: Elsevier, 1993. 361–370Google Scholar
  5. 5.
    Thompson P, Marchant E. A computer model for the evacuation of large building populations. Fire Saf J, 1995, 24: 131–148CrossRefGoogle Scholar
  6. 6.
    Owen M, Galea E R, Lawrence P J. The Exodus evacuation model applied to building evacuation scenarios. J Fire Prot Eng, 1996, 8: 65–86CrossRefGoogle Scholar
  7. 7.
    Fang Z, Lu S M. A spatial grid model for emergency evacuation from building. China Saf Sci J, 2001, 11: 10–13Google Scholar
  8. 8.
    Yang L Z, Fang W F, Huang R, et al. Study on cellular automaton model of occupant evacuation in fire disaster. Chinese Sci Bull, 2002, 47: 1143–1147Google Scholar
  9. 9.
    Cui X H, Li Q, Chen J, et al. Study on MA-based model of occupant evacuation in public facility. J Syst Simu, 2008, 20: 1006–1023Google Scholar
  10. 10.
    Fulkerson D R, Weinberger D B. Blocking pairs of polyhedra arising from network flows. J Comb Theory B, 1975, 18: 265–283CrossRefGoogle Scholar
  11. 11.
    Newton C, Mussa R N, Sadalla E K, et al. Evaluation of all alternative traffic light change anticipation system. Accident Anal Prev, 1997, 29: 201–209CrossRefGoogle Scholar
  12. 12.
    Yamada T. A network flow approach to a city emergency evacuation planning. Int J Syst Sci, 1996, 27: 931–936CrossRefGoogle Scholar
  13. 13.
    Cova T J, Johnson J P. A network flow model for lane-based evacuation routing. Trans Res Pt A, 2003, 37: 579–604Google Scholar
  14. 14.
    Cova T J, Church R L. Modelling community evacuation vulnerability using GIS. Int J Geogr Inf Sci, 1997, 11: 763–784CrossRefGoogle Scholar
  15. 15.
    Church R L, Cova T J. Mapping evacuation risk on transportation networks using a spatial optimization model. Trans Res Pt C, 2000, 8: 321–336CrossRefGoogle Scholar
  16. 16.
    Myung Y S, Kim H. A cutting plane algorithm for computing k-edge survivability of a network. Eur J Oper Res, 2004, 156: 579–589CrossRefGoogle Scholar
  17. 17.
    Wei Y, Linet Z. A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur J Oper Res, 2007, 179: 1177–1193CrossRefGoogle Scholar
  18. 18.
    Hamza-Lup G L, Hua K A, Peng R. Leveraging e-transportation in real-time traffic evacuation management. Elec Commer Res Appl, 2007, 6: 413–424CrossRefGoogle Scholar
  19. 19.
    Wu Z Z. Study on methods and contents for land use safety planning. J Safety Envir, 2004, 4: 86–90Google Scholar
  20. 20.
    Weng T, Zhu J P, Ma M G, et al. A study on regional assessment of risk of urban major hazard. Engi Sci, 2006, 8: 80–84Google Scholar
  21. 21.
    Wu Z Z, Duo Y Q, Wei L J, et al. Quantitative area risk assessment method and its application in land use safety planning for major hazard installations. Engineering Science, 2006, 8: 46–49Google Scholar
  22. 22.
    Turner J R. The hand book of project-based management. Maiden Bead: McGraw-Hill Book Company, 1992Google Scholar
  23. 23.
    Sorensen J H, Vogt B M, Mileti D S. Evacuation: an assessment of planning and research. Washington DC: Oak Ridge National Laboratory, 1987Google Scholar
  24. 24.
    Geng S Y, Qu W L, Zhang L A. Discrete mathematics, 3rd ed. Beijing: Tsinghua University Press, 2004Google Scholar
  25. 25.
    Zhuo L, Chen J, Shi P J, et al. Modeling population density of China in 1998 based on DMSP/OLS nighttime light image. Acta Geogr Sinica, 2005, 60: 266–276Google Scholar
  26. 26.
    Flowerdew R, Green M. Developments in the area interpolation methods and GIS. Ann Reg Sci, 1992, 26: 67–78CrossRefGoogle Scholar

Copyright information

© Science in China Press and Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.College of Resources Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.Academy of Disaster Reduction and Emergency ManagementBeijing Normal UniversityBeijingChina
  3. 3.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina

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