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

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...

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Autores principales: Kasmalkar, Indraneel G., Serafin, Katherine A., Suckale, Jenny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374724/
https://www.ncbi.nlm.nih.gov/pubmed/34434881
http://dx.doi.org/10.1016/j.mex.2021.101483
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author Kasmalkar, Indraneel G.
Serafin, Katherine A.
Suckale, Jenny
author_facet Kasmalkar, Indraneel G.
Serafin, Katherine A.
Suckale, Jenny
author_sort Kasmalkar, Indraneel G.
collection PubMed
description 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.
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spelling pubmed-83747242021-08-24 Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding Kasmalkar, Indraneel G. Serafin, Katherine A. Suckale, Jenny MethodsX Method Article 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. Elsevier 2021-08-09 /pmc/articles/PMC8374724/ /pubmed/34434881 http://dx.doi.org/10.1016/j.mex.2021.101483 Text en © 2021 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Method Article
Kasmalkar, Indraneel G.
Serafin, Katherine A.
Suckale, Jenny
Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
title Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
title_full Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
title_fullStr Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
title_full_unstemmed Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
title_short Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
title_sort integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374724/
https://www.ncbi.nlm.nih.gov/pubmed/34434881
http://dx.doi.org/10.1016/j.mex.2021.101483
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