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A Decision Support System for Real-Time Evacuation Management and Rescue Team Planning during Hazardous Events in Public Infrastructures

  • Iraklis Tsekourakis
  • Christos Orlis
  • Dimosthenis Ioannidis
  • Dimitrios Tzovaras
Part of the Communications in Computer and Information Science book series (CCIS, volume 329)

Abstract

The emergency evacuation from crowded public transport terminals and critical infrastructures worldwide is a matter of great concern. Numerous research efforts exist, attempting to develop systems to abide by and ensure optimal evacuation guidance from disaster areas. Existing safety systems fail to guide effectively the most vulnerable travelers and to take into consideration the mobility impairment of each individual. In this context, current work elaborates on the issue by proposing a robust evacuation mechanism, incorporated in a Decision Support System. The innovative solution is to provide group-wise optimal routes to the exits, along with personalized routing, taking into account user capabilities and preferences. As a further step, the rescue teams are provided with a plan including prioritized targets with trapped travelers. The paper provides a thorough analysis of existing evacuation models and mechanisms, presents the DSS architecture and embedded algorithms, and discusses its performance.

Keywords

decision support system emergency evacuation capacity constraint route planning rescue team planning 

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References

  1. 1.
    EU funded SAVE ME SST STREP (FP7-234027), http://www.save-me.eu/
  2. 2.
    Braun, A., Musse, S.R., de Oliveira, L.P.L., Bodmann, B.E.J.: Modeling individual behaviors in crowd simulation. In: 16th International Conference on Computer Animation and Social Agents, May 8-9, pp. 143–148 (2003)Google Scholar
  3. 3.
    Yang, L.Z., Zhao, D.L., Li, J., Fang, T.Y.: Simulation of the kin behavior in building occupant evacuation based on Cellular Automaton. Building and Environment 40(3), 411–415 (2005)CrossRefGoogle Scholar
  4. 4.
    Hamacher, H., Tjandra, S.: Mathematical modelling of Evacuation Problems: A State of the Art. Pedestrian and Evacuation Dynamics, pp. 227–266 (2002)Google Scholar
  5. 5.
    Chalmet, L.G., Francis, R.L., Saunders, P.B.: Network Models for Building Evacuation. Management Science 28, 86–105 (1982)CrossRefGoogle Scholar
  6. 6.
    Montes, Christian: Evacuation of Buildings. M.Sc. Thesis, Department of Mathematics, Universitat Kaiserslautern, Kaiserslautern, Germany (1994)Google Scholar
  7. 7.
    Kostreva, M.M., Wiecek, M.M.: Time Dependency in Multiple Objective Dynamic Programming. Journal of Mathematical Analysis and Applications 173(1), 289–307 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Blue, V.J., Adler, J.L.: Using Cellular Automata Microsimulation to Model Pedestrian Movements. In: Proceedings of the 14th International Symposium on Transportation and Traffic Theory, Jerusalem, Israel (1999)Google Scholar
  9. 9.
    Lu, Q., George, B., Shekhar, S.: Capacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 291–307. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Shi, L., Xie, Q., Cheng, X., Chen, L., Zhou, Y., Zhang, R.: Developing a database for emergency evacuation model. Building and Environment 44(8), 1724–1729 (2009)CrossRefGoogle Scholar
  11. 11.
    Wang, F., Yang, M., Yang, R.: Dynamic Fleet Management for Cybercars. In: IEEE Intelligent Transportation Systems Conference, pp. 1246–1250 (2006)Google Scholar
  12. 12.
    Simao, H., Day, J., George, A., Gifford, T., Nienow, J., Powell, W.: An approximate dynamic programming algorithm for large-scale fleet management: A case application. Transport. Science 43(2), 178–197 (2008)CrossRefGoogle Scholar
  13. 13.
    Zografos, K., Androutsopoulos, K.: A heuristic algorithm for solving hazardous materials distribution problems. European Journal of Operational Research, New Technologies in Transportation Systems 152(2), 507–519 (2004)zbMATHGoogle Scholar
  14. 14.
    Bansal, N., Blum, A., Chawla, S., Meyerson, A.: Approximation Algorithms for Deadline-TSP and Vehicle Routing with Time-Windows. In: Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, pp. 166–174 (2004)Google Scholar
  15. 15.
    Kim, S., Shekhar, S., Min, M.: Contraflow Transportation Network Reconfiguration for Evacuation Route Planning. IEEE Transactions on Knowledge and Data Engineering, 1115–1129 (August 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Iraklis Tsekourakis
    • 1
  • Christos Orlis
    • 1
  • Dimosthenis Ioannidis
    • 1
  • Dimitrios Tzovaras
    • 1
  1. 1.Center for Research and Technology HellasInformation Technologies InstituteThessalonikiGreece

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