IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v46y2012i5p772-789.html
   My bibliography  Save this article

A method to assess demand growth vulnerability of travel times on road network links

Author

Listed:
  • Watling, David
  • Balijepalli, N.C.

Abstract

Many national governments around the world have turned their recent focus to monitoring the actual reliability of their road networks. In parallel there have been major research efforts aimed at developing modelling approaches for predicting the potential vulnerability of such networks, and in forecasting the future impact of any mitigating actions. In practice—whether monitoring the past or planning for the future—a confounding factor may arise, namely the potential for systematic growth in demand over a period of years. As this growth occurs the networks will operate in a regime closer to capacity, in which they are more sensitive to any variation in flow or capacity. Such growth will be partially an explanation for trends observed in historic data, and it will have an impact in forecasting too, where we can interpret this as implying that the networks are vulnerable to demand growth. This fact is not reflected in current vulnerability methods which focus almost exclusively on vulnerability to loss in capacity. In the paper, a simple, moment-based method is developed to separate out this effect of demand growth on the distribution of travel times on a network link, the aim being to develop a simple, tractable, analytic method for medium-term planning applications. Thus the impact of demand growth on the mean, variance and skewness in travel times may be isolated. For given critical changes in these summary measures, we are thus able to identify what (location-specific) level of demand growth would cause these critical values to be exceeded, and this level is referred to as Demand Growth Reliability Vulnerability (DGRV). Computing the DGRV index for each link of a network also allows the planner to identify the most vulnerable locations, in terms of their ability to accommodate growth in demand. Numerical examples are used to illustrate the principles and computation of the DGRV measure.

Suggested Citation

  • Watling, David & Balijepalli, N.C., 2012. "A method to assess demand growth vulnerability of travel times on road network links," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 772-789.
    • Handle: RePEc:eee:transa:v:46:y:2012:i:5:p:772-789
    DOI: 10.1016/j.tra.2012.02.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856412000274
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2012.02.009?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael Taylor & Somenahalli Sekhar & Glen D'Este, 2006. "Application of Accessibility Based Methods for Vulnerability Analysis of Strategic Road Networks," Networks and Spatial Economics, Springer, vol. 6(3), pages 267-291, September.
    2. Hu Shao & William Lam & Mei Tam, 2006. "A Reliability-Based Stochastic Traffic Assignment Model for Network with Multiple User Classes under Uncertainty in Demand," Networks and Spatial Economics, Springer, vol. 6(3), pages 173-204, September.
    3. Clark, Stephen & Watling, David, 2005. "Modelling network travel time reliability under stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 119-140, February.
    4. Berdica, Katja, 2002. "An introduction to road vulnerability: what has been done, is done and should be done," Transport Policy, Elsevier, vol. 9(2), pages 117-127, April.
    5. W. Szeto & L. O'Brien & M. O'Mahony, 2006. "Risk-Averse Traffic Assignment with Elastic Demands: NCP Formulation and Solution Method for Assessing Performance Reliability," Networks and Spatial Economics, Springer, vol. 6(3), pages 313-332, September.
    6. Chen, Anthony & Yang, Hai & Lo, Hong K. & Tang, Wilson H., 2002. "Capacity reliability of a road network: an assessment methodology and numerical results," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 225-252, March.
    7. Jenelius, Erik & Petersen, Tom & Mattsson, Lars-Göran, 2006. "Importance and exposure in road network vulnerability analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 537-560, August.
    8. Bell, Michael G. H., 2000. "A game theory approach to measuring the performance reliability of transport networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(6), pages 533-545, August.
    9. Agachai Sumalee & Fumitaka Kurauchi, 2006. "Guest Editorial: Reliability and Emergency Issues in Transportation Network Analysis," Networks and Spatial Economics, Springer, vol. 6(3), pages 169-172, September.
    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. 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. Almotahari, Amirmasoud & Yazici, M. Anil, 2019. "A link criticality index embedded in the convex combinations solution of user equilibrium traffic assignment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 67-82.
    3. Muriel-Villegas, Juan E. & Alvarez-Uribe, Karla C. & Patiño-Rodríguez, Carmen E. & Villegas, Juan G., 2016. "Analysis of transportation networks subject to natural hazards – Insights from a Colombian case," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 151-165.
    4. Li, Tao & Rong, Lili & Yan, Kesheng, 2019. "Vulnerability analysis and critical area identification of public transport system: A case of high-speed rail and air transport coupling system in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 55-70.
    5. Aura Reggiani, 2022. "The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 415-437, September.
    6. Nogal, Maria & Morales Nápoles, Oswaldo & O’Connor, Alan, 2019. "Structured expert judgement to understand the intrinsic vulnerability of traffic networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 136-152.
    7. Rodríguez-Núñez, Eduardo & García-Palomares, Juan Carlos, 2014. "Measuring the vulnerability of public transport networks," Journal of Transport Geography, Elsevier, vol. 35(C), pages 50-63.
    8. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    9. 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.
    10. Lu, Qing-Chang & Zhang, Junyi & Peng, Zhong-Ren & Rahman, ABM Sertajur, 2014. "Inter-city travel behaviour adaptation to extreme weather events," Journal of Transport Geography, Elsevier, vol. 41(C), pages 148-153.
    11. John Östh & Aura Reggiani & Peter Nijkamp, 2018. "Resilience and accessibility of Swedish and Dutch municipalities," Transportation, Springer, vol. 45(4), pages 1051-1073, July.
    12. Leng Jun-qiang & Yang Long-hai & Wei-yi Liu & Lin Zhao, 2017. "Measuring Road Network Vulnerability with Sensitivity Analysis," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-11, January.

    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. 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. Chen, Anthony & Zhou, Zhong, 2010. "The [alpha]-reliable mean-excess traffic equilibrium model with stochastic travel times," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 493-513, May.
    3. Zhaoqi Zang & Xiangdong Xu & Kai Qu & Ruiya Chen & Anthony Chen, 2022. "Travel time reliability in transportation networks: A review of methodological developments," Papers 2206.12696, arXiv.org, revised Jul 2022.
    4. 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.
    5. Mengying Cui & David Levinson, 2018. "Accessibility analysis of risk severity," Transportation, Springer, vol. 45(4), pages 1029-1050, July.
    6. Jenelius, Erik & Mattsson, Lars-Göran, 2012. "Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 746-760.
    7. Ng, ManWo & Waller, S. Travis, 2010. "A computationally efficient methodology to characterize travel time reliability using the fast Fourier transform," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1202-1219, December.
    8. Bell, Michael G.H. & Kurauchi, Fumitaka & Perera, Supun & Wong, Walter, 2017. "Investigating transport network vulnerability by capacity weighted spectral analysis," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 251-266.
    9. Rodríguez-Núñez, Eduardo & García-Palomares, Juan Carlos, 2014. "Measuring the vulnerability of public transport networks," Journal of Transport Geography, Elsevier, vol. 35(C), pages 50-63.
    10. Gonçalves, L.A.P.J. & Ribeiro, P.J.G., 2020. "Resilience of urban transportation systems. Concept, characteristics, and methods," Journal of Transport Geography, Elsevier, vol. 85(C).
    11. Nima Haghighi & S. Kiavash Fayyaz & Xiaoyue Cathy Liu & Tony H. Grubesic & Ran Wei, 2018. "A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment," Networks and Spatial Economics, Springer, vol. 18(1), pages 181-203, March.
    12. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.
    13. Prakash, A. Arun & Seshadri, Ravi & Srinivasan, Karthik K., 2018. "A consistent reliability-based user-equilibrium problem with risk-averse users and endogenous travel time correlations: Formulation and solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 171-198.
    14. 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.
    15. Muriel-Villegas, Juan E. & Alvarez-Uribe, Karla C. & Patiño-Rodríguez, Carmen E. & Villegas, Juan G., 2016. "Analysis of transportation networks subject to natural hazards – Insights from a Colombian case," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 151-165.
    16. Lichun Chen & Elise Miller-Hooks, 2012. "Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport," Transportation Science, INFORMS, vol. 46(1), pages 109-123, February.
    17. Gu, Yu & Chen, Anthony & Xu, Xiangdong, 2023. "Measurement and ranking of important link combinations in the analysis of transportation network vulnerability envelope buffers under multiple-link disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 118-144.
    18. Chen, Anthony & Zhou, Zhong & Lam, William H.K., 2011. "Modeling stochastic perception error in the mean-excess traffic equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1619-1640.
    19. ManWo Ng & Hong Lo, 2013. "Regional Air Quality Conformity in Transportation Networks with Stochastic Dependencies: A Theoretical Copula-Based Model," Networks and Spatial Economics, Springer, vol. 13(4), pages 373-397, December.
    20. Ng, ManWo & Szeto, W.Y. & Travis Waller, S., 2011. "Distribution-free travel time reliability assessment with probability inequalities," Transportation Research Part B: Methodological, Elsevier, vol. 45(6), pages 852-866, July.

    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:eee:transa:v:46:y:2012:i:5:p:772-789. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.