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Link-level vulnerability indicators for real-world networks

Author

Listed:
  • Knoop, Victor L.
  • Snelder, Maaike
  • van Zuylen, Henk J.
  • Hoogendoorn, Serge P.

Abstract

It is computationally expensive to find out where vulnerable parts in a network are. In literature a variety of methods were introduced that use simple indicators (measured in real-life or calculated in a traffic simulator) to pre-determine the seriousness of the delays caused by the blocking of that link and thereafter perform a more detailed analysis. This article reviews the indicators proposed in the literature and assesses the quality of these indicators. Furthermore, a multi-linear fit of the indicators is made to find a better, combined, indicator to rank the links according to their vulnerability. The article shows that different indicators assess different links to be vulnerable. Also combined they cannot predict the vulnerability of a link. Therefore, it is concluded that to find vulnerable links, one has to look further than link-based indicators.

Suggested Citation

  • Knoop, Victor L. & Snelder, Maaike & van Zuylen, Henk J. & Hoogendoorn, Serge P., 2012. "Link-level vulnerability indicators for real-world networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 843-854.
  • Handle: RePEc:eee:transa:v:46:y:2012:i:5:p:843-854
    DOI: 10.1016/j.tra.2012.02.004
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    Citations

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    Cited by:

    1. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    2. Oliveira, Eduardo Leal de & Portugal, Licínio da Silva & Porto Junior, Walter, 2016. "Indicators of reliability and vulnerability: Similarities and differences in ranking links of a complex road system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 195-208.
    3. Cats, O. & Yap, M. & van Oort, N., 2016. "Exposing the role of exposure: Public transport network risk analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 1-14.
    4. Demirel, Hande & Kompil, Mert & Nemry, Françoise, 2015. "A framework to analyze the vulnerability of European road networks due to Sea-Level Rise (SLR) and sea storm surges," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 62-76.
    5. Cats, Oded & Jenelius, Erik, 2015. "Planning for the unexpected: The value of reserve capacity for public transport network robustness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 47-61.
    6. repec:eee:reensy:v:152:y:2016:i:c:p:151-165 is not listed on IDEAS
    7. Reggiani, Aura & Nijkamp, Peter & Lanzi, Diego, 2015. "Transport resilience and vulnerability: The role of connectivity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 4-15.
    8. repec:kap:netspa:v:17:y:2017:i:4:d:10.1007_s11067-017-9359-9 is not listed on IDEAS
    9. repec:eee:trapol:v:57:y:2017:i:c:p:20-30 is not listed on IDEAS
    10. repec:eee:transb:v:99:y:2017:i:c:p:251-266 is not listed on IDEAS
    11. Bagloee, Saeed Asadi & Sarvi, Majid & Wolshon, Brian & Dixit, Vinayak, 2017. "Identifying critical disruption scenarios and a global robustness index tailored to real life road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 60-81.

    More about this item

    Keywords

    Vulnerability; Incidents; Delay; Indicators;

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