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A Dartboard Network Cut Based Approach to Evacuation Route Planning: A Summary of Results

  • KwangSoo Yang
  • Venkata M. V. Gunturi
  • Shashi Shekhar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7478)

Abstract

Given a transportation network, a population, and a set of destinations, the goal of evacuation route planning is to produce routes that minimize the evacuation time for the population. Evacuation planning is essential for ensuring public safety in the wake of man-made or natural disasters (e.g., terrorist acts, hurricanes, and nuclear accidents). The problem is challenging because of the large size of network data, the large number of evacuees, and the need to account for capacity constraints in the road network. Promising methods that incorporate capacity constraints into route planning have been developed but new insights are needed to reduce the high computational costs incurred by these methods with large-scale networks. In this paper, we propose a novel scalable approach that explicitly exploits the spatial structure of road networks to minimize the computational time. Our new approach accelerates the routing algorithm by partitioning the network using dartboard network-cuts and groups node-independent shortest routes to reduce the number of search iterations. Experimental results using a Minneapolis, MN road network demonstrate that the proposed approach outperforms prior work for CCRP computation by orders of magnitude.

Keywords

evacuation route planning spatial network dartboard network cut routing and scheduling algorithm 

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References

  1. 1.
    The New York Times, HURRICANE ANDREW: When a Monster Is on the Way, ’It’s Time to Get Out of Town. In: Texas, a Line of Cars 50 Miles Long (August 26, 1992), http://goo.gl/hq0EH (retrieved April 2012)
  2. 2.
    U.S.Census Bureau - TIGER/Lines, http://goo.gl/P6Ye7 (retrieved January 2012)
  3. 3.
    OpenStreetMap, http://goo.gl/Hso0 (retrieved April 2012)
  4. 4.
    Ahuja, R., Magnanti, T., Orlin, J., Weihe, K.: Network flows: theory, algorithms and applications. Prentice Hall (1993)Google Scholar
  5. 5.
    Ben-Akiva, M., et al.: Development of a deployable real-time dynamic traffic assignment system: Dynamit and dynamit-p users guide. Intelligent Transportation Systems Program. Massachusetts Institute of Technology (2002)Google Scholar
  6. 6.
    Bhandari, R.: Survivable Networks: Algorithms for Diverse Routing. Kluwer Academic Publishers, Norwell (1998)Google Scholar
  7. 7.
    Fleischer, L., Skutella, M.: Quickest flows over time. SIAM Journal on Computing 36, 1600–1630 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Ford, D., Fulkerson, D.: Flows in networks. Princeton university press (2010)Google Scholar
  9. 9.
    Gastner, M., Newman, M.: The spatial structure of networks. The European Physical Journal B-Condensed Matter and Complex Systems 49, 247–252 (2006)CrossRefGoogle Scholar
  10. 10.
    Hamacher, H., Tjandra, S.: Mathematical modelling of evacuation problems: State of the art. In: Pedestrian and Evacuation Dynamics, pp. 227–266. Springer (2002)Google Scholar
  11. 11.
    Hillier, F., Lieberman, G., Hillier, M.: Introduction to operations research. McGraw-Hill (1990)Google Scholar
  12. 12.
    Kleinberg, J.M.: Approximation algorithms for disjoint paths problems. Ph.D. Dissertation, Dept. of CS., Massachusetts Institute of Technology (1996)Google Scholar
  13. 13.
    Korf, R., Zhang, W., Thayer, I., Hohwald, H.: Frontier search. Journal of the ACM (JACM) 52, 715–748 (2005)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Levinson, D., Yerra, B.: Self-organization of surface transportation networks. Transportation Science 40, 179–188 (2006)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Mahmassani, H., Sbayti, H., Zhou, X.: Dynasmart-p: Intelligent transportation network planning tool: Version 1.0 users guide. Maryland Transportation Initiative, University of Maryland, College Park, MD (2004)Google Scholar
  17. 17.
    Schrijver, A.: Combinatorial optimization. Springer (2003)Google Scholar
  18. 18.
    Shekhar, S., Chawla, S.: Spatial databases: a tour. Prentice Hall, Upper Saddle River (2003), 7458Google Scholar
  19. 19.
    Sidhu, D., Nair, R., Abdallah, S.: Finding disjoint paths in networks. ACM SIGCOMM Computer Communication Review 21, 43–51 (1991)CrossRefGoogle Scholar
  20. 20.
    Suurballe, J.: Disjoint paths in a network. Networks 4, 125–145 (1974)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Suurballe, J., Tarjan, R.: A quick method for finding shortest pairs of disjoint paths. Networks 14, 325–336 (1984)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Wardrop, J.: Some theoretical aspects of road traffic research. Proceedings of the Institution of Civil Engineers 2(1) (1952)Google Scholar
  23. 23.
    Xie, F., Levinson, D.: Measuring the structure of road networks. Geographical Analysis 39, 336–356 (2007)CrossRefGoogle Scholar
  24. 24.
    Zhou, X., George, B., Kim, S., Wolff, J., Lu, Q., Shekhar, S., Nashua, O., Team, G.: Evacuation planning: A spatial network database approach. Bulletin of the Technical Committee on Data Engineering 33(2), 26 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • KwangSoo Yang
    • 1
  • Venkata M. V. Gunturi
    • 1
  • Shashi Shekhar
    • 1
  1. 1.Department of Computer ScienceUniversity of MinnesotaMinneapolisUSA

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