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

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  • 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
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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. 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.
    12. John Östh & Aura Reggiani & Peter Nijkamp, 2018. "Resilience and accessibility of Swedish and Dutch municipalities," Transportation, Springer, vol. 45(4), pages 1051-1073, July.

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