Natural Hazards

, Volume 86, Issue 1, pp 151–164 | Cite as

Robustness of road systems to extreme flooding: using elements of GIS, travel demand, and network science

Original Paper

Abstract

The main objective of this article is to study the robustness of road networks to extreme flooding events that can negatively affect entire regional systems in a relatively unpredictable way. Here, we adopt a deterministic approach to simulate extreme flooding events in two cities, New York City and Chicago, by removing entire sections of road systems using U.S. FEMA floodplains. We then measure changes in the number of real trips that can be completed (using travel demand data), Geographical Information Systems properties, and network topological indicators. We notably measure and discuss how betweenness centrality is being redistributed after flooding. Broadly, robustness in spatial systems like road networks is dependent on many factors, including system size (number of nodes and links) and topological structure of the network. Expectedly, robustness also depends on geography, and cities that are naturally more at risk will tend to be less robust, and therefore the notion of robustness rapidly becomes sensitive to individual contexts.

Keywords

Robustness Road networks Extreme events GIS analysis Network science 

References

  1. Abowd JM, Haltiwanger J, Lane J (2004) Integrated longitudinal employer-employee data for the United States. Am Econ Rev 94:224–229CrossRefGoogle Scholar
  2. Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406:378–382CrossRefGoogle Scholar
  3. Atun F (2014) Understanding effects of complexity in cities during disasters. In: Understanding complex urban systems: multidisciplinary approaches to modeling. Springer, pp 51–65Google Scholar
  4. Ayyub BM (2014) Systems resilience for multihazard environments: definition, metrics, and valuation for decision making. Risk Anal 34:340–355CrossRefGoogle Scholar
  5. Barabási A-L, Frangos J (2014) Linked: the new science of networks science of networks. Basic Books, New YorkGoogle Scholar
  6. Batty M (2013) The new science of cities. MIT Press, CambridgeGoogle Scholar
  7. Beniston M, Stephenson DB, Christensen OB et al (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Change 81:71–95CrossRefGoogle Scholar
  8. Borgatti SP (1995) Centrality and AIDS. Connections 18:112–114Google Scholar
  9. Cai W, Borlace S, Lengaigne M et al (2014) Increasing frequency of extreme El Niño events due to greenhouse warming. Nat Clim Change 4:111–116CrossRefGoogle Scholar
  10. Callaway DS, Newman ME, Strogatz SH, Watts DJ (2000) Network robustness and fragility: percolation on random graphs. Phys Rev Lett 85:5468CrossRefGoogle Scholar
  11. Cohen R, Erez K, Ben-Avraham D, Havlin S (2001) Breakdown of the Internet under intentional attack. Phys Rev Lett 86:3682CrossRefGoogle Scholar
  12. Cottrill CD, Derrible S (2015) Leveraging big data for the development of transport sustainability indicators. J Urban Technol 22:45–64. doi:10.1080/10630732.2014.942094 CrossRefGoogle Scholar
  13. Creel L (2003) Ripple effects: population and coastal regions. Population Reference Bureau, Washington, DCGoogle Scholar
  14. Crucitti P, Latora V, Marchiori M (2004) Model for cascading failures in complex networks. Phys Rev E 69:045104CrossRefGoogle Scholar
  15. Csardi G, Nepusz T (2006) The igraph software package for complex network research. Inter J Complex Syst 1695:1–9Google Scholar
  16. Derrible S (2012) Network centrality of metro systems. PLoS ONE 7:e40575. doi:10.1371/journal.pone.0040575 CrossRefGoogle Scholar
  17. Derrible S (2016a) Urban infrastructure is not a tree: integrating and decentralizing urban infrastructure systems. Environ Plan B Plan Des. http://epb.sagepub.com/content/early/2016/05/12/0265813516647063.abstract
  18. Derrible S (2016b) Complexity in future cities: the rise of networked infrastructure. Int J Urban Sci. doi:10.1080/12265934.2016.1233075
  19. Derrible S, Ahmad N (2015) Network-based and binless frequency analyses. PLoS ONE 10:e0142108CrossRefGoogle Scholar
  20. Derrible S, Kennedy C (2010) The complexity and robustness of metro networks. Phys Stat Mech Its Appl 389:3678–3691CrossRefGoogle Scholar
  21. Derrible S, Saneinejad S, Sugar L, Kennedy C (2010) Macroscopic model of greenhouse gas emissions for municipalities. Transp Res Rec J Transp Res Board. doi:10.3141/2191-22
  22. Duan Y, Lu F (2013) Structural robustness of city road networks based on community. Comput Environ Urban Syst 41:75–87CrossRefGoogle Scholar
  23. Easterling DR, Meehl GA, Parmesan C et al (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074CrossRefGoogle Scholar
  24. Erath A, Birdsall J, Axhausen KW, Hajdin R (2009) Vulnerability assessment methodology for Swiss road network. Transp Res Rec J Transp Res Board 2137:118–126CrossRefGoogle Scholar
  25. Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1:215–239CrossRefGoogle Scholar
  26. Gottschalk P, McEntarfer E, Moffitt R (2008) Trends in the transitory variance of male earnings in the US, 1991–2003: Preliminary Evidence from LEHD data. Boston College WP, 696Google Scholar
  27. Holling CS (1973) Resilience and stability of ecological systems. Annu Rev Ecol Syst 4:1–23Google Scholar
  28. Holme P, Kim BJ, Yoon CN, Han SK (2002) Attack vulnerability of complex networks. Phys Rev E 65:056109CrossRefGoogle Scholar
  29. Huang X, Gao J, Buldyrev SV et al (2011) Robustness of interdependent networks under targeted attack. Phys Rev E 83:065101CrossRefGoogle Scholar
  30. Jenelius E, Petersen T, Mattsson L-G (2006) Importance and exposure in road network vulnerability analysis. Transp Res Part Policy Pract 40:537–560CrossRefGoogle Scholar
  31. Jiang B (2007) A topological pattern of urban street networks: universality and peculiarity. Phys Stat Mech Its Appl 384:647–655CrossRefGoogle Scholar
  32. Karduni A, Kermanshah A, Derrible S (2016) A protocol to convert spatial polyline data to network formats and applications to world urban road networks. Sci Data. doi:10.1038/sdata.2016.46
  33. Kermanshah A, Derrible S (2016) A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes. Reliab Eng Syst Saf. doi:10.1016/j.ress.2016.04.007 Google Scholar
  34. Kermanshah A, Karduni A, Peiravian F, Derrible S (2014) Impact analysis of extreme events on flows in spatial networks. In: 2014 IEEE international conference on Big Data. IEEE, pp 29–34Google Scholar
  35. Knutson TR, McBride JL, Chan J et al (2010) Tropical cyclones and climate change. Nat Geosci 3:157–163CrossRefGoogle Scholar
  36. Koç Y, Warnier M, Van Mieghem P et al (2014) The impact of the topology on cascading failures in a power grid model. Phys Stat Mech Its Appl 402:169–179CrossRefGoogle Scholar
  37. Martin S, Carr R, Faulon J-L (2006) Random removal of edges from scale free graphs. Phys Stat Mech Its Appl 371:870–876CrossRefGoogle Scholar
  38. Murray AT, Matisziw TC, Grubesic TH (2008) A methodological overview of network vulnerability analysis. Growth Change 39:573–592CrossRefGoogle Scholar
  39. Newman M (2010) Networks: an introduction. Oxford University Press, OxfordCrossRefGoogle Scholar
  40. Peiravian F, Kermanshah A, Derrible S (2014) Spatial data analysis of complex urban systems. In: 2014 IEEE International Conference on Big Data. IEEE, pp 54–59Google Scholar
  41. Perrings C (1998) Resilience in the dynamics of economy-environment systems. Environ Resour Econ 11:503–520CrossRefGoogle Scholar
  42. Pimm SL (1984) The complexity and stability of ecosystems. Nature 307:321–326CrossRefGoogle Scholar
  43. Scott DM, Novak DC, Aultman-Hall L, Guo F (2006) Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J Transp Geogr 14:215–227CrossRefGoogle Scholar
  44. Suarez P, Anderson W, Mahal V, Lakshmanan T (2005) Impacts of flooding and climate change on urban transportation: a systemwide performance assessment of the Boston Metro Area. Transp Res Part Transp Environ 10:231–244CrossRefGoogle Scholar
  45. Taleb NN (2010) The black swan: the impact of the highly improbable fragility. Random House, New YorkGoogle Scholar
  46. Taylor MA (2008) Critical transport infrastructure in Urban Areas: impacts of traffic incidents assessed using accessibility-based network vulnerability analysis. Growth Change 39:593–616CrossRefGoogle Scholar
  47. UNISDR (2012) How to make cities more resilient—a handbook for mayors and local government leaders. United Nations International Strategy for Disaster Reduction, GenevaGoogle Scholar
  48. US Census Bureau (2013) TIGER products. http://www.census.gov/geo/maps-data/data/tiger.html. Accessed 25 May 2015
  49. US Census Bureau Center for Economic Studies (2015) US Census Bureau Center for Economic Studies Publications and Reports Page. http://lehd.ces.census.gov/data/. Accessed 24 Sep 2015
  50. Wang X, Pournaras E, Kooij RE, Van Mieghem P (2014) Improving robustness of complex networks via the effective graph resistance. Eur Phys J B 87:1–12CrossRefGoogle Scholar
  51. Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99:5766–5771CrossRefGoogle Scholar
  52. Woods DD (2015) Four concepts for resilience and the implications for the future of resilience engineeringGoogle Scholar
  53. Zhang G-Q, Wang D, Li G-J (2007) Enhancing the transmission efficiency by edge deletion in scale-free networks. Phys Rev E 76:017101CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Civil and Materials EngineeringUniversity of Illinois at ChicagoChicagoUSA

Personalised recommendations