4.1 Article

Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding

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METHODSX
卷 8, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.mex.2021.101483

关键词

Coastal flooding; Transportation; Resilience; Sea level rise

资金

  1. UPS Endowment Fund
  2. Stanford's Bill Lane Center for the American West
  3. NSF through the Office of Polar Programs [PLR-1744758, PLR-1739027]
  4. Stanford Graduate Fellowship
  5. the Haas Center for Public Service
  6. School of Earth, Energy and Environmental Sciences
  7. Department of Civil and Environmental Engineering
  8. Institute for Computational and Mathematical Engineering
  9. Center for Sustainable Development and Global Competitiveness
  10. Dawe family
  11. Woods Institute for the Environment
  12. Stanford Professionals in Real Estate

向作者/读者索取更多资源

Sea level rise and coastal floods are disrupting coastal communities worldwide, impacting critical urban systems like transportation. The closure of low-lying coastal roads and highways due to floods can lead to travel delays and increased accident risks. Quantifying the disruption of the urban traffic system by floods presents challenges, but implementing three corrections can improve the accuracy of identifying flooded roads and assessing flood impacts on urban traffic systems.
Sea level rise and coastal floods are disrupting coastal communities across the world. The impacts of coastal floods are magnified by the disruption of critical urban systems such as transportation. The flood-related closure of low-lying coastal roads and highways can increase travel time delays and accident risk. However, quantifying the flood-related disruption of the urban traffic system presents challenges. Traffic systems are complex and highly dynamic, where congestion resulting from road closures may propagate rapidly from one area to another. Prior studies identify flood-related road closures by spatially overlaying coastal flood maps onto road network models, but simplifications within the representation of the road network with respect to the coastline or creeks may lead to an incorrect identification of flooded roads. We identify three corrections to reduce potential biases in the identification of flooded roads: 1. We correct for the geometry of highways; 2. We correct for the elevation of bridges and highway overpasses; and 3. We identify and account for road-creek crossings. Accounting for these three corrections, we develop a methodology for accurately identifying flooded roads, improving our ability to quantify flood impacts on urban traffic systems and accident rates. (C) 2021 The Authors. Published by Elsevier B.V.

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