A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning

  • Ole-Christoffer Granmo
  • Jaziar Radianti
  • Morten Goodwin
  • Julie Dugdale
  • Parvaneh Sarshar
  • Sondre Glimsdal
  • Jose J. Gonzalez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7906)

Abstract

Managing the uncertainties that arise in disasters – such as ship fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd with hazard dynamics, using a ship fire as a proof-of-concept scenario. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior – both descriptive and normative (optimal). Descriptive modeling is based on studies of physical fire models, crowd psychology models, and corresponding flow models, while we identify optimal behavior using Ant-Based Colony Optimization (ACO). Simulation results demonstrate that the DNB model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Furthermore, the ACO provides safe paths, dynamically responding to current threats.

Keywords

Dynamic Bayesian Networks Ant Based Colony Optimization Evacuation Planning Crowd Modeling Hazard Modeling 

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References

  1. 1.
    World Fire Statistics Center.: World fire statistics bulletin. World Fire Statistics Bulletin, Valéria Pacella, Geneva (2012)Google Scholar
  2. 2.
    Murphy, K.P.: Dynamic Bayesian Networks: Representation, inference and learning. PhD Dissertation, University of California, Berkeley (2002)Google Scholar
  3. 3.
    Charniak, E.: Bayesian networks without tears. AI Magazine 12(4), 50–63 (1991)Google Scholar
  4. 4.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo (1988)Google Scholar
  5. 5.
    Eleye-Datubo, A.G., Wall, A., Saajedi, A., Wang, J.: Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Analysis 26(3), 695–721 (2006)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization - artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)Google Scholar
  7. 7.
    Jian, C., Marek, J.D.: AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks. Journal of Artificial Intelligence Research 13, 155–188 (2000)MathSciNetGoogle Scholar
  8. 8.
    Thompson, P.A., Marchant, E.W.: A computer model for the evacuation of large building populations. Fire Safety Journal 24(2), 131–148 (1995)CrossRefGoogle Scholar
  9. 9.
    Thompson, P.A., Marchant, E.W.: Testing and application of the computer model simulex. Fire Safety Journal 24(2), 149–166 (1995)CrossRefGoogle Scholar
  10. 10.
    Hamacher, H., Tjandra, S.: Mathematical modelling of evacuation problems – a state of the art. Pedestrian and Evacuation Dynamics 2002, 227–266 (2002)Google Scholar
  11. 11.
    Kim, D., Shin, S.: Local path planning using a new artificial potential function composition and its analytical design guidelines. Advanced Robotics 20(1), 115–135 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Braun, A., Bodmann, B., Musse, S.: Simulating virtual crowds in emergency situations. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 244–252. ACM (2005)Google Scholar
  13. 13.
    Liu, S., Yang, L., Fang, T., Li, J.: Evacuation from a classroom considering the occupant density around exits. Physica A: Statistical Mechanics and its Applications 388(9), 1921–1928 (2009)CrossRefGoogle Scholar
  14. 14.
    Wang, J.H., Lo, S.M., Sun, J.H., Wang, Q.S., Mu, H.L.: Qualitative simulation of the panic spread in large-scale evacuation. Simulations 88, 1465–1474 (2012)CrossRefGoogle Scholar
  15. 15.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ole-Christoffer Granmo
    • 1
  • Jaziar Radianti
    • 1
  • Morten Goodwin
    • 1
  • Julie Dugdale
    • 1
    • 2
  • Parvaneh Sarshar
    • 1
  • Sondre Glimsdal
    • 1
  • Jose J. Gonzalez
    • 1
  1. 1.Centre for Integrated Emergency ManagementUniversity of Agder GrimstadNorway
  2. 2.Grenoble Informatics Laboratory (LIG)Grenoble 2 UniversityFrance

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