, Volume 45, Issue 4, pp 1029–1050 | Cite as

Accessibility analysis of risk severity

  • Mengying Cui
  • David Levinson


This study measures severity of network disruptions in the Minneapolis–St. Paul region by comparing the cumulative opportunity accessibility before-and-after removing freeway segments. Accessibility to jobs and accessibility to resident workers are measured respectively in the morning and evening peak hours. It is shown that the links with more severe consequences of disruption tend to be near or at freeway interchanges. Betweenness helps explain risk severity.


Accessibility Vulnerability Network structure Betweenness 


  1. American Community Survey: (2010)
  2. Anderson, P., Levinson, D., Parthasarathi, P.: Accessibility futures. Trans. GIS 17(5), 683–705 (2013)Google Scholar
  3. Andres, S., Michael, M.: Urban network analysis—a new toolbox for arcgis, Technical report (2012)Google Scholar
  4. Baradaran, S., Ramjerdi, F.: Performance of accessibility measures in Europe. J. Transp. Stat. 4(2/3), 31–48 (2001)Google Scholar
  5. Ben-Akiva, M.E., Lerman, S.R.: Discrete choice analysis: theory and application to travel demand, vol. 9. MIT press, Cambridge (1985)Google Scholar
  6. Berdica, K.: An introduction to road vulnerability: what has been done, is done and should be done. Transp. Policy 9(2), 117–127 (2002)CrossRefGoogle Scholar
  7. Cervero, R., et al.: Paradigm shift: from automobility to accessibility planning. Urban Futures (Canberra) 22, 9 (1997)Google Scholar
  8. Cheng, J., Bertolini, L.: Measuring urban job accessibility with distance decay, competition and diversity. J. Transp. Geogr. 30, 100–109 (2013)CrossRefGoogle Scholar
  9. Cui, M., Levinson, D.: Accessibility and the ring of unreliability. Transportmetrica A Transp. Sci. 1–18 (2016).
  10. El-Geneidy, A.M., Levinson, D.M.: Access to destinations: development of accessibility measures, Technical report, Minnesota Department of Transportation Research Services Section (2006)Google Scholar
  11. El-Geneidy, A., Levinson, D.: Place rank: a flow-based accessibility measure, Technical report (2009)Google Scholar
  12. El-Rashidy, R.A., Grant-Muller, S.M.: An assessment method for highway network vulnerability. J. Transp. Geogr. 34, 34–43 (2014)CrossRefGoogle Scholar
  13. Ermagun, A., Levinson, D.M., Chatterjee, S.: Using temporal detrending to observe the spatial correlation of traffic. In: Conference: Transportation Research Board (TRB) annual meeting, Washington, DC (United States), Jan 2017 (2017)Google Scholar
  14. Feng, X., Levinson, D.: Evolving transportation networks. Springer, New York (2011)Google Scholar
  15. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)CrossRefGoogle Scholar
  16. Gaillard, J.-C.: Vulnerability, capacity and resilience: perspectives for climate and development policy. J. Int. Dev. 22(2), 218–232 (2010)CrossRefGoogle Scholar
  17. Hansen, W.G.: How accessibility shapes land use. J. Am. Inst. plan. 25(2), 73–76 (1959)CrossRefGoogle Scholar
  18. Iacono, M., Krizek, K., El-Geneidy, A.M.: Access to destinations: how close is close enough? estimating accurate distance decay functions for multiple modes and different purposes, Technical report (2008)Google Scholar
  19. Jenelius, E., Petersen, T., ran Mattsson, L.-G.: Importance and exposure in road network vulnerability analysis. Transp. Res. Part A 40, 537–560 (2006)Google Scholar
  20. Knoop, V., Van Zuylen, H., Hoogendoorn, S.: The influence of spillback modelling when assessing consequences of blockings in a road network. EJTIR 8(4), 287–300 (2008)Google Scholar
  21. Knoop, V.L., Snelder, M., van Zuylen, H.J., Hoogendoorn, S.P.: Link-level vulnerability indicators for real-world networks. Transp. Res. Part A Policy Pract. 46(5), 843–854 (2012)CrossRefGoogle Scholar
  22. LaMondia, J.J., Blackmar, C.E., Bhat, C.R.: Comparing transit accessibility measures: a case study of access to healthcare facilities. In: Proceedings of TRB 2011 Annual Meeting (2011)Google Scholar
  23. Levinson, D.M.: Accessibility and the journey to work. J. Transp. Geogr. 6(1), 11–21 (1998)CrossRefGoogle Scholar
  24. Levinson, D.: Network structure and city size. PLoS One 7(1), e29721 (2012)CrossRefGoogle Scholar
  25. Martellato, D., Nijkamp, P.: The concept of accessibility revisited. In: Reggiani, A. (ed.) Accessibility, Trade and Locational Behaviour. Ashgate, Brookfield (1998)Google Scholar
  26. Murray-Tuite, P., Mahmassani, H.: Methodology for determining vulnerable links in a transportation network. Transp. Res. Rec. J. Transp. Res. Board 1882, 88–96 (2004)CrossRefGoogle Scholar
  27. Owen, A., Levinson, D.: Access to destinations: annual accessibility measure for the twin cities metropolitan region, Technical report, Minnesota Department of Transportation Research Services Section (2012)Google Scholar
  28. Owen, A., Levinson, D.: Access across america: Transit 2014, Technical report, Accessibility Observatory, University of Minnesota (2014)Google Scholar
  29. Owen, A., Levinson, D., Murphy, B.: Access across america: Walking 2014, Technical report, Accessibility Observatory, University of Minnesota (2014)Google Scholar
  30. Owen, A., Murphy, B., Levinson, D.: Access across America: Auto 2015, Technical report, Accessibility Obervatory, University of Minnesota (2015)Google Scholar
  31. Páez, A., Scott, D.M., Morency, C.: Measuring accessibility: positive and normative implementations of various accessibility indicators. J. Transp. Geogr. 25, 141–153 (2012)CrossRefGoogle Scholar
  32. Parthasarathi, P., Levinson, D.M.: Network structure and metropolitan mobility, Available at SSRN 1736324 (2010)Google Scholar
  33. Parthasarathi, P., Hochmair, H., Levinson, D.: Network structure and spatial separation. Environ. Plan. B Plan. Des. 39, 137–154 (2012)CrossRefGoogle Scholar
  34. Sarewitz, D., Pielke, R., Keykhah, M.: Vulnerability and risk: some thoughts from a political and policy perspective. Risk Anal. 23(4), 805–810 (2003)CrossRefGoogle Scholar
  35. Scott, D.M., Novak, D.C., Aultman-Hall, L., Guo, F.: Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J. Transp. Geogr. 14(3), 215–227 (2006)CrossRefGoogle Scholar
  36. Tampère, C., Stada, J., Immers, B., Peetermans, E., Organe, K.: Methodology for identifying vulnerable sections in a national road network. Transp. Res. Rec. J. Transp. Res. Board (2012), 1–10 (2007)Google Scholar
  37. Taylor, M.A., Sekhar, S.V., D’Este, G.M.: Application of accessibility based methods for vulnerability analysis of strategic road networks. Netw. Spat. Econ. 6(3–4), 267–291 (2006)CrossRefGoogle Scholar
  38. TomTom International BV: Speed profiles, Technical report (2013)Google Scholar
  39. Townsend, A.: Re-programming mobility: How the tech industry is driving us towards a crisis in transportation planning, Technical report, New Cities Foundation (2012)Google Scholar
  40. US Census Bureau: LEHD origin-destination employment statistics dataset structure format version 7.0., Technical report. (2013)
  41. Vickerman, R.W.: Accessibility, attraction, and potential: a review of some concepts and their use in determining mobility. Environ. Plan. A 6(6), 675–691 (1974)CrossRefGoogle Scholar
  42. Wachs, M., Kumagai, T.G.: Physical accessibility as a social indicator. Socio-Econ. Plan. Sci. 7(5), 437–456 (1973)CrossRefGoogle Scholar
  43. Xie, F., Levinson, D.: Measuring the structure of road networks. Geogr. Anal. 39(3), 336–356 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Civil, Environmental, and Geo-EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Center for Transportation StudiesUniversity of MinnesotaMinneapolisUSA
  3. 3.School of Civil EngineeringUniversity of SydneySydneyAustralia

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