Analysis of Vulnerability of Road Networks on the Basis of Graph Topology and Related Attribute Information

  • Zhe Zhang
  • Kirsi Virrantaus
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 4)


The safety of people and the security of the vital functions of society are among the core tasks of governments. Various networks, especially transportation networks, are important for human life. Therefore, the government should place greater emphasis on preparedness planning and mitigation actions. Much research has been done to analyse the vulnerability of road networks and most of the methods were based on analysing the topological structure of the network using only topological attributes. This paper introduces a multi-attribute value theory which can combine all the attributes’ values, not only topological but also non-topological, and weight them according to a decision maker’s preference in order to produce an overall value. This overall value is used to compute the vulnerability of a road network. A road has a higher overall value is considered more vulnerable and more effort needs to be put into preparedness in crisis management. In this paper, a decision making software package Web-HIPRE is also introduced and illustrated.


multiple attribute value theory GIS road network vulnerability crisis management 


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  1. 1.
    Batagelj, V., Mrvar, A.: Pajek: Analysis and visualization of large networks. In: Jünger, M., Mutzel, P. (eds.) Graph Drawing software, pp. 77–103. Springer, Berlin (2003)Google Scholar
  2. 2.
    Clemen, R.T.: Making Hard Decisions: An Introduction to Decision Analysis. Duxbury Press, International edition (1995)Google Scholar
  3. 3.
    Demšar, U., Špatenková, O., Virrantaus, K.: Identifying critical location in a spatial network with graph theory. Transactions in GIS 12, 61–68 (2008)CrossRefGoogle Scholar
  4. 4.
    Gabor, T., Griffith, T.K.: The assessment of community vulnerability to acute hazardous materials incidents. Journal of Hazardous Materials 8, 323–333 (1980)CrossRefGoogle Scholar
  5. 5.
    Freeman, L.: Centrality in social network: conceptual clarification. Social Networks 1, 215–239 (1979)CrossRefGoogle Scholar
  6. 6.
    Hwang, C., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1981)zbMATHGoogle Scholar
  7. 7.
    Jia, J.M., Fisher, G.W., Dyer, J.S.: Attribute weighting methods and decision quality in the presence of response error: a simulation study. Journal of Behavioral Decision making 11, 85–105 (1998)CrossRefGoogle Scholar
  8. 8.
    Keeney, R.L.: Value-Focused Thinking: A Path to Creative Decision Making. Harvard University Press, Cambridge (1996)Google Scholar
  9. 9.
    Mustajoki, J., Hämäläinen, R.: Web-HIPRE: Global decision support by value tree and AHP analysis. INFOR Journal 38 (2000)Google Scholar
  10. 10.
    Malczewski, J.: GIS and Multicriteria Decision Analysis. Wiley, New York (1999)Google Scholar
  11. 11.
    Nooy, W., Mrvar, A., Batagelj, V.: Exporatory Social Network Analysis with Pajek. Cambridge University Press, Cambridge (2005)Google Scholar
  12. 12.
    Pöyhönen, M., Vrolijk, H., Hämäläinen, R.: Behavioral and procedural consequences of structural variation in value trees. European Journal of Operational Research 134, 216–227 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Saaty, T.L.: The analytic hierarchy process. McGRaw-Hill, New York (1980)zbMATHGoogle Scholar
  14. 14.
    Strozzi, F., Zaldívar, J.M., Poljansek, K., Bono, F., Gutièrres, E.: From complex networks to time series and viceversa (2009), (Accessed March 5, 2010)
  15. 15.
    Worboys, M., Duckham, M.: GIS: A Computing Perspective. CRC Press, United Kingdom (2004)Google Scholar
  16. 16.
    Winterfeldt, D., Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge (1986)Google Scholar
  17. 17.
    West, D.B.: Introduction to Graph Theory. Prentice-Hall, United States (2001)Google Scholar
  18. 18.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442Google Scholar
  19. 19.
    Zhang, Z., Sunila, R., Virrantaus, K.: A spatio-temporal population model for alarming, situational picture and warning system. In: Joint International conference on theory, data handling and modeling in Geospatial information science (2010)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Zhe Zhang
    • 1
  • Kirsi Virrantaus
    • 1
  1. 1.Department of Surveying, Faculty of Engineering and Architecture, School of Science and TechnologyAalto University 

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