, Volume 46, Issue 4, pp 1127–1141 | Cite as

The role of travel demand and network centrality on the connectivity and resilience of an urban street system

  • Meisam AkbarzadehEmail author
  • Soroush Memarmontazerin
  • Sybil Derrible
  • Sayed Farzin Salehi Reihani


In the transportation literature, two major and parallel approaches exist to identify the critical elements of a transportation system. On the one hand, conventional transportation engineering emphasizes travel demand, often in terms of traffic volume (i.e., demand side). On the other hand, newer techniques from Network Science emphasize network topology (i.e., supply side). To better understand the relationship between the two approaches, we first investigate whether they correlate by comparing traffic volume and node centrality. Second, we assess the impact of the two approaches on the connectivity and resilience of a transportation network; connectivity is measured by the relative size of the giant component, and resilience is measured by the network’s adaptive capacity (the amount of extra flow it can handle). The urban road system of Isfahan (Iran) is used as a practical case study. Overall, we find that traffic volume indeed correlates with node centrality. In addition, we find that the weighted degree of a node, i.e., the sum of the capacities of its incident links (for small disruptions) and node betweenness (for large disruptions), best captures node criticality. Nodes with high weighted degree and betweenness should therefore be given higher priority to enhance connectivity and resilience in urban street systems. Regarding link criticality, roads with higher capacities showed a more important role as opposed to betweenness, flow, and congestion.


Centrality Resilience Giant Component Adaptive Capacity 


  1. Ahmad, N., Derrible, S., Eason, T., Cabezas, H.: Using Fisher information to track stability in multivariate systems. R. Soc. Open Sci. 3(11), 160582 (2016)CrossRefGoogle Scholar
  2. Albert, R., Jeong, H., Barabási, A.-L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)CrossRefGoogle Scholar
  3. Amini, B., Peiravian, F., Mojarradi, M., Derrible, S.: Comparative analysis of traffic performance of urban transportation systems. Transp. Res. Rec. J. Transp. Res. Board 2594, 159–168 (2016)CrossRefGoogle Scholar
  4. Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23(3), 191–201 (2001)CrossRefGoogle Scholar
  5. Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)CrossRefGoogle Scholar
  6. Callaway, D.S., Newman, M.E., Strogatz, S.H., Watts, D.J.: Network robustness and fragility: percolation on random graphs. Phys. Rev. Lett. 85(25), 5468 (2000)CrossRefGoogle Scholar
  7. Cottrill, C.D., Derrible, S.: Leveraging big data for the development of transport sustainability indicators. J. Urban Technol. 22(1), 45–64 (2015)CrossRefGoogle Scholar
  8. Crane, R.: On form versus function: will the new urbanism reduce traffic, or increase it? J. Plan. Educ. Res. 15(2), 117–126 (1996)CrossRefGoogle Scholar
  9. Crucitti, P., Latora, V., Porta, S.: Centrality in networks of urban streets. Chaos Interdiscip. J. Nonlinear Sci. 16(1), 015113 (2006)CrossRefGoogle Scholar
  10. Derrible, S.: Complexity in future cities: the rise of networked infrastructure. Int. J. Urban Sci. 21, 1–19 (2016a)CrossRefGoogle Scholar
  11. Derrible, S.: Urban infrastructure is not a tree: integrating and decentralizing urban infrastructure systems. Plan. B Plan. Des, Environ (2016b). doi: 10.1177/0265813516647063 Google Scholar
  12. Derrible, S., Ahmad, N.: Network-based and binless frequency analyses. PLoS ONE 10(11), e0142108 (2015)CrossRefGoogle Scholar
  13. Derrible, S., Saneinejad, S., Sugar, L., Kennedy, C.: Macroscopic model of greenhouse gas emissions for municipalities. Transp. Res. Rec. J. Transp. Res. Board 2191, 174–181 (2010)CrossRefGoogle Scholar
  14. Dunn, S., Wilkinson, S.M.: Increasing the resilience of air traffic networks using a network graph theory approach. Transp. Res. Part E Logist. Transp. Rev. 90, 39–50 (2016)CrossRefGoogle Scholar
  15. Ellens, W., Spieksma, F., Van Mieghem, P., Jamakovic, A., Kooij, R.: Effective graph resistance. Linear Algebra Appl. 435(10), 2491–2506 (2011)CrossRefGoogle Scholar
  16. Gallopín, G.C.: Linkages between vulnerability, resilience, and adaptive capacity. Glob. Environ. Change 16(3), 293–303 (2006)CrossRefGoogle Scholar
  17. Gao, C., Wei, D., Hu, Y., Mahadevan, S., Deng, Y.: A modified evidential methodology of identifying influential nodes in weighted networks. Physica A 392(21), 5490–5500 (2013)CrossRefGoogle Scholar
  18. Hu, F., Liu, Y.: Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis. Mod. Phys. Lett. B 29(03), 1450268 (2015)CrossRefGoogle Scholar
  19. Karduni, A., Kermanshah, A., Derrible, S.: A protocol to convert spatial polyline data to network formats and applications to world urban road networks. Sci. Data 3, 160046 (2016)CrossRefGoogle Scholar
  20. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefGoogle Scholar
  21. Kermanshah, A., Derrible, S.: A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes. Reliab. Eng. Syst. Saf. 153, 39–49 (2016a)CrossRefGoogle Scholar
  22. Kermanshah, A., Derrible, S.: Robustness of road systems to extreme flooding: using elements of GIS, travel demand, and network science. Nat. Hazards 1, 1–14 (2016b)Google Scholar
  23. Liu, J., Xiong, Q., Shi, W., Shi, X., Wang, K.: Evaluating the importance of nodes in complex networks. Physica A 452, 209–219 (2016)CrossRefGoogle Scholar
  24. Lü, L., Zhang, Y.-C., Yeung, C.H., Zhou, T.: Leaders in social networks, the delicious case. PLoS ONE 6(6), e21202 (2011)CrossRefGoogle Scholar
  25. Lü, L., Chen, D., Ren, X.-L., Zhang, Q.-M., Zhang, Y.-C., Zhou, T.: Vital nodes identification in complex networks. Phys. Rep. 650, 1–63 (2016)CrossRefGoogle Scholar
  26. Morone, F., Makse, H.A.: Influence maximization in complex networks through optimal percolation. Nature 524, 65–68 (2015)Google Scholar
  27. Newman, M.: Networks: An Introduction. Oxford University Press, New York (2010)CrossRefGoogle Scholar
  28. Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)CrossRefGoogle Scholar
  29. Osei-Asamoah, A., Lownes, N.: Complex network method of evaluating resilience in surface transportation networks. Transp. Res. Rec. J. Transp. Res. Board 2467, 120–128 (2014)CrossRefGoogle Scholar
  30. Porta, S., Crucitti, P., Latora, V.: The network analysis of urban streets: a dual approach. Physica A 369(2), 853–866 (2006a)CrossRefGoogle Scholar
  31. Porta, S., Crucitti, P., Latora, V.: The network analysis of urban streets: a primal approach. Environ. Plan. 33(5), 705–725 (2006b)CrossRefGoogle Scholar
  32. Ren, Z.M., Shao, F., Liu, J.G., Wang, B.-H.: Node importance measurement based on the degree and clustering coefficient information. Acta Phys. Sin. 62(12), 128901 (2013)Google Scholar
  33. 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
  34. Tomović, R.: Sensitivity Analysis of Dynamic Systems. McGraw-Hill, New York (1963)Google Scholar
  35. Tsekeris, T., Geroliminis, N.: City size, network structure and traffic congestion. J. Urban Econ. 76, 1–14 (2013)CrossRefGoogle Scholar
  36. Wang, J., Rong, L., Guo, T.: A new measure method of network node importance based on local characteristics. J. Dalian Univ. Technol. 50(5), 822–826 (2010)Google Scholar
  37. Wang, X., Koç, Y., Derrible, S., Ahmad, S.N., Pino, W.J., Kooij, R.E.: Multi-criteria robustness analysis of metro networks. Physica A 474, 19–31 (2017)CrossRefGoogle Scholar
  38. Wu, T., Chen, L., Zhong, L., Xian, X.: Enhanced collective influence: a paradigm to optimize network disruption. Stat. Mech. Appl., Physica A (2016)Google Scholar
  39. Xinsheng, S., Xiaoxiao, W., ZHANG, L.: Node importance evaluation method for highway network of urban agglomeration. J. Transp. Syst. Eng. Inf. Technol. 11(2), 84–90 (2011)Google Scholar
  40. Yoo, S., Yeo, H.: Evaluation of the resilience of air transportation network with adaptive capacity. Int. J. Urban Sci. 20(sup1), 38–49 (2016)CrossRefGoogle Scholar
  41. Zadeh, A.S.M., Rajabi, M.A.: Analyzing the effect of the street network configuration on the efficiency of an urban transportation system. Cities 31, 285–297 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Meisam Akbarzadeh
    • 1
    Email author
  • Soroush Memarmontazerin
    • 1
  • Sybil Derrible
    • 2
    • 3
  • Sayed Farzin Salehi Reihani
    • 1
  1. 1.Isfahan University of TechnologyIsfahanIran
  2. 2.Complex and Sustainable Urban Networks (CSUN) LaboratoryUniversity of Illinois at ChicagoChicagoUSA
  3. 3.Department of Civil and Materials EngineeringUniversity of Illinois at ChicagoChicagoUSA

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