IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v31y2011i12p1872-1882.html

QRA Model‐Based Risk Impact Analysis of Traffic Flow in Urban Road Tunnels

Author

Listed:
  • Qiang Meng
  • Xiaobo Qu
  • Kum Thong Yong
  • Yoke Heng Wong

Abstract

Road tunnels are vital infrastructures providing underground vehicular passageways for commuters and motorists. Various quantitative risk assessment (QRA) models have recently been developed and employed to evaluate the safety levels of road tunnels in terms of societal risk (as measured by the F/N curve). For a particular road tunnel, traffic volume and proportion of heavy goods vehicles (HGVs) are two adjustable parameters that may significantly affect the societal risk, and are thus very useful in implementing risk reduction solutions. To evaluate the impact the two contributing factors have on the risk, this article first presents an approach that employs a QRA model to generate societal risk for a series of possible combinations of the two factors. Some combinations may result in F/N curves that do not fulfill a predetermined safety target. This article thus proposes an “excess risk index” in order to quantify the road tunnel risk magnitudes that do not pass the safety target. The two‐factor impact analysis can be illustrated by a contour chart based on the excess risk. Finally, the methodology has been applied to Singapore's KPE road tunnel and the results show that in terms of meeting the test safety target for societal risk, the traffic capacity of the tunnel should be no more than 1,200 vehs/h/lane, with a maximum proportion of 18% HGVs.

Suggested Citation

  • Qiang Meng & Xiaobo Qu & Kum Thong Yong & Yoke Heng Wong, 2011. "QRA Model‐Based Risk Impact Analysis of Traffic Flow in Urban Road Tunnels," Risk Analysis, John Wiley & Sons, vol. 31(12), pages 1872-1882, December.
  • Handle: RePEc:wly:riskan:v:31:y:2011:i:12:p:1872-1882
    DOI: 10.1111/j.1539-6924.2011.01624.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.2011.01624.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1539-6924.2011.01624.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mark E. T. Horn & Neale Fulton & Mark Westcott, 2008. "Measures of Societal Risk and Their Potential Use in Civil Aviation," Risk Analysis, John Wiley & Sons, vol. 28(6), pages 1711-1726, December.
    2. Arends, B.J. & Jonkman, S.N. & Vrijling, J.K. & van Gelder, P.H.A.J.M, 2005. "Evaluation of tunnel safety: towards an economic safety optimum," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 217-228.
    3. Qiang Meng & Xiaobo Qu & Xinchang Wang & Vivi Yuanita & Siew Chee Wong, 2011. "Quantitative Risk Assessment Modeling for Nonhomogeneous Urban Road Tunnels," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 382-403, March.
    4. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    5. Jonkman, S.N. & Lentz, A. & Vrijling, J.K., 2010. "A general approach for the estimation of loss of life due to natural and technological disasters," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1123-1133.
    6. Terje Aven, 2007. "On the Ethical Justification for the Use of Risk Acceptance Criteria," Risk Analysis, John Wiley & Sons, vol. 27(2), pages 303-312, April.
    7. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Can Chen & Tienan Li & Jian Sun & Feng Chen, 2016. "Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method," IJERPH, MDPI, vol. 14(1), pages 1-15, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    2. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    3. Rockafellar, R.T. & Royset, J.O., 2010. "On buffered failure probability in design and optimization of structures," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 499-510.
    4. Li, Bo & Hou, Peng-Wen & Chen, Ping & Li, Qing-Hua, 2016. "Pricing strategy and coordination in a dual channel supply chain with a risk-averse retailer," International Journal of Production Economics, Elsevier, vol. 178(C), pages 154-168.
    5. Yao, Yinhong & Chen, Xiuwen & Chen, Zhensong, 2025. "Portfolio tail risk forecasting for international financial assets: A GARCH-MIDAS-R-Vine copula model," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    6. Kull, Andreas, 2009. "Sharing Risk – An Economic Perspective," ASTIN Bulletin, Cambridge University Press, vol. 39(2), pages 591-613, November.
    7. Mínguez, R. & Conejo, A.J. & García-Bertrand, R., 2011. "Reliability and decomposition techniques to solve certain class of stochastic programming problems," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 314-323.
    8. Jia Liu & Cuixia Li, 2023. "Dynamic Game Analysis on Cooperative Advertising Strategy in a Manufacturer-Led Supply Chain with Risk Aversion," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
    9. Jose Blanchet & Henry Lam & Yang Liu & Ruodu Wang, 2025. "Convolution Bounds on Quantile Aggregation," Operations Research, INFORMS, vol. 73(5), pages 2761-2781, September.
    10. Brian Tomlin & Yimin Wang, 2005. "On the Value of Mix Flexibility and Dual Sourcing in Unreliable Newsvendor Networks," Manufacturing & Service Operations Management, INFORMS, vol. 7(1), pages 37-57, June.
    11. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2014. "Bank regulation and international financial stability: A case against the 2006 Basel framework for controlling tail risk in trading books," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 107-130.
    12. Neelke Doorn, 2015. "The Blind Spot in Risk Ethics: Managing Natural Hazards," Risk Analysis, John Wiley & Sons, vol. 35(3), pages 354-360, March.
    13. D. Kuhn, 2009. "Convergent Bounds for Stochastic Programs with Expected Value Constraints," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 597-618, June.
    14. Kasai, Naoya & Matsuhashi, Shigemi & Sekine, Kazuyoshi, 2013. "Accident occurrence model for the risk analysis of industrialfacilities," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 71-74.
    15. Pengyu Wei & Zuo Quan Xu, 2021. "Dynamic growth-optimum portfolio choice under risk control," Papers 2112.14451, arXiv.org.
    16. Kolos Ágoston, 2012. "CVaR minimization by the SRA algorithm," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 623-632, December.
    17. Samir Saissi Hassani & Georges Dionne, 2021. "The New International Regulation of Market Risk: Roles of VaR and CVaR in Model Validation," Working Papers 21-1, HEC Montreal, Canada Research Chair in Risk Management.
    18. Sarah Kaakai & Anis Matoussi & Achraf Tamtalini, 2022. "Multivariate Optimized Certainty Equivalent Risk Measures and their Numerical Computation," Working Papers hal-03817818, HAL.
    19. Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo.
    20. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:31:y:2011:i:12:p:1872-1882. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.