Advertisement

Journal of Transportation Security

, Volume 2, Issue 1–2, pp 29–46 | Cite as

A methodology to assess the criticality of highway transportation networks

  • Satish V. UkkusuriEmail author
  • Wilfredo F. Yushimito
Article

Abstract

Assessing the importance of transportation facilities is an increasingly growing topic of interest to federal and state transportation agencies. In the wake of recent terrorist attacks and recurring manmade and natural disasters, significant steps are needed to improve security at both state and metropolitan level. This paper proposes a heuristic procedure using concepts of complex networks science to assess the importance of highway transportation networks using travel time as the performance measure to assess criticality. We demonstrate the proposed technique both in a theoretical network (Sioux Falls network) and in a built-up network to assess the criticality of the major infrastructures that are used to access Manhattan in an AM peak hour. The results demonstrate the efficacy of the procedure to determine critical links in a transportation network.

Keywords

Transportation networks Critical links Equilibrium Disruptions 

References

  1. Bar-Noy A, Kuller S, Schieber B (1995) The complexity of finding the most vital arc and nodes. Technical Report CS-TR-3539, University of Maryland, Institute for Advanced Computer Studies, MDGoogle Scholar
  2. Barton A (2005) Addressing the problem of finding a single vital edge in a maximum flow graph. National Research Council Canada. Available online at http://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-48305.pdf Accessed on 1st March 2007
  3. Bell MGH (2000) A game theory approach to measuring the performance reliability of transportation networks. Transp Res Part B Methodological 34:533–545. doi: 10.1016/S0191-2615(99)00042-9 CrossRefGoogle Scholar
  4. Chen A, Kongsomsaksakul S, Zhou Z (2007) Assessing network vulnerability using a combined travel demand model. CD-ROM Transportation Research Board of the National Academies, Washington, D.CGoogle Scholar
  5. Church RL, Scaparra MP, Middleton RS (2004) Identifying critical infrastructure: The median and covering facility interdiction problems. Ann Assoc Am Geogr 94(3):491–502. doi: 10.1111/j.1467-8306.2004.00410.x CrossRefGoogle Scholar
  6. Cormican KJ, Morton DP, Wood RK (1998) Stochastic network interdiction. Oper Res 46(2):184–197. doi: 10.1287/opre.46.2.184 CrossRefGoogle Scholar
  7. Grubesic TH, Murray AT (2006) Vital nodes, interconnected infrastructures, and the geographies of network survivability. Ann Am Geogr 96(1):64–83. doi: 10.1111/j.1467-8306.2006.00499.x CrossRefGoogle Scholar
  8. Ham DB, Lockwood S (2002) National needs assessment for ensuring transportation infrastructure security-contractor’s final Report, Requested by American Association of State Highway and Transportation Officials (AASHTO) Transportation Security Task Force. Available Online at http://freight.transportation.org/doc/NCHRP_B.pdf, Accessed: Mar 01, 2007
  9. Latora V, Marchiori M (2005) Vulnerability and protection of infrastructure networks. Phys Rev E Stat Nonlin Soft Matter Phys 71:015103. R. doi: 10.1103/PhysRevE.71.015103
  10. Liu C, Fan YY (2007) A two-stage stochastic programming model for transportation network retrofit. CD-ROM. Transportation Research Board of the National Academies, Washington, D.CGoogle Scholar
  11. Matisziw TC, Murray AT, Grubesic TH (2007) Evaluating vulnerability and risk in interstate highway operation. Transportation Research Board of the National Academies, Washington, D.CGoogle Scholar
  12. Modarres M, Zarei B (2002) Application of network theory and AHP in urban transportation to minimize earthquake damages. J Oper Res Soc 53:1308–1316. doi: 10.1057/palgrave.jors.2601470 CrossRefGoogle Scholar
  13. Murray-Tuite PM (2003) Identification of vulnerable transportation infrastructure and household decision making under emergency evacuation conditions. Ph.D. dissertation, Univ. of Texas, AustinGoogle Scholar
  14. Nagurney A, Qiang Q (2007) A network efficiency measure for congested networks. Europhysics Letters 79:38005, 1–15Google Scholar
  15. Nagurney A, Qiang Q (2008) A network efficiency measure with application to critical infrastructure networks. J Glob Optim 40:261–275. doi: 10.1007/s10898-007-9198-1 CrossRefGoogle Scholar
  16. NCHRP Project 20-07 (2002) A guide to highway vulnerability assessment for critical asset identification and protection—contractor’s final report, Requested by American Association of State Highway and Transportation Officials (AASHTO) Transportation Office Security Task Force. Available Online at http://www.transportation.org/sites/security/docs/NatlNeedsAssess.pdf, Accessed: Mar 01, 2007
  17. NCHRP REPORT 525 Volume 11 (2006) Disruption Impact Estimating Tool-Transportation (DIETT): a tool for prioritizing high-value transportation choke points. Available Online at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_525v11.pdf, Accessed: Mar 01, 2007
  18. Ortúzar JD, Willumsen LG (2006) Modelling transport. Wiley, NYGoogle Scholar
  19. Qiao J, Jeong D, Lawley M, Richard J-PP, Abraham DM, Yih Y (2007) Allocating security resources to a water supply network. IIE Trans 39:95–109. doi: 10.1080/07408170600865400 CrossRefGoogle Scholar
  20. Ratliff HD, Sicilia GT, Lubore SH (1975) Finding the N most vital links in flow networks. Manage Sci 21(5):531–539. doi: 10.1287/mnsc.21.5.531 CrossRefGoogle Scholar
  21. Scott DM, Novak D, Aultman-Hall L, Guo F (2006) A new approach for identifying critical links in transportation networks. CD-ROM Transportation Research Board of the National Academies, Washington, D.CGoogle Scholar
  22. Sheffi Y (1985) Urban transportation networks: equilibrium analysis with mathematical methods. Prentice HallGoogle Scholar
  23. Shen H (1999) Finding the K most vital edges with respect to the minimum spanning tree. Acta Informatica 36:405–424. doi: 10.1007/s002360050166 CrossRefGoogle Scholar
  24. TCRP REPORT 86/NCHRP REPORT 525 Volume 12 (2006) Making transportation tunnels safe and secure. Available Online at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_525v12.pdf, Accessed: Mar 01, 2007
  25. Wood KR (1993) Deterministic network interdiction. Math Comput Model 17:1–18. doi: 10.1016/0895-7177(93)90236-R CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2009

Authors and Affiliations

  1. 1.Blitman Career Development, 4032 Jonsson Engineering CenterRensselaer Polytechnic InstituteTroyUSA
  2. 2.4002 Jonsson Engineering CenterRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations