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Modeling the Impact of Spatial Correlations of Road Failures on Travel Times during Adverse Weather Conditions

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalTransportation Research Record: Journal of the Transportation Research Board
Early online date7 May 2019
DateAccepted/In press - 14 Feb 2019
DateE-pub ahead of print - 7 May 2019
DatePublished (current) - 1 Jul 2019


Traveling in extreme adverse weather involves a high risk of travel delay and traffic accidents. There is a need to assess the impact of extreme weather on transport infrastructure and to find suitable mitigation strategies to alleviate the associated undesirable outcomes. Previous work in vulnerability studies applied either a constant failure probability or an assumed probabilistic distribution. Such assumptions ignored many factors causing the occurrence of road failure, especially that infrastructure components tend to fail interdependently. Based on empirical data of road failures and rainfall intensity during a typhoon, this study develops a statistical model, incorporating spatial correlations among the segments of road infrastructure, and uses it to evaluate the impact of the typhoon on travel time reliability. Mixed-effects logistic regression as well as rare-events logistic regression are applied to understand the factors involved in road failures and the spatial correlations of the failed segments. The analysis suggested that, in addition to the rainfall intensity, the road geometry, including elevation, land slope, and distance from the nearest river, were important factors in the failure. In addition, there is a significant correlation of failures within watersheds. This model gives an insight into the characteristics of road failures and their associated travel risks, which is useful for authorities to find proper mitigations to reduce the adverse effects in future disasters.

    Research areas

  • Transport Resilience, Typhoon, Travel Time Reliability, Spatial Correlations

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    Accepted author manuscript, 1.25 MB, PDF document

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