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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Coupled Situational Awareness System to Improve Transportation Infrastructure Performance during Extreme Events

Authors: Hatim Sharif; Samer Dessouky; Md Jobair Bin Alam; Raghava R. Kommalapati; Hongbo Du;

Coupled Situational Awareness System to Improve Transportation Infrastructure Performance during Extreme Events

Abstract

The dense road networks and numerous low water crossings throughout Texas may be contributing to the higher recurrence rates of floods that pose a danger to vehicles. A timely issue that should be addressed by researchers is the compounding of disaster. Flooding can be combined with other life-threatening occurrences such as power loss and interruptions of health and emergency services. During these events, rescue requests from the stranded communities overwhelm the emergency response facilities; impassable roadways and the paucity of reliable information on the affected areas and their accessibility hamper emergency response operations, causing several detours and delays that put both the responders and evacuees at risk. This research presents a framework for improved situational awareness during extreme flooding events by combing a flood inundation model with transportation infrastructure performance assessment. The flood inundation model can be driven by real-time radar rainfall data in an efficient manner. The road network work model can use land use, census data, and locations of critical facilities in combination with spatial analysis. The proposed framework is demonstrated on a small catchment in San Antonio, Texas. The study includes the following tasks: literature review, vehicle-related flood fatality analysis, review of flood warning systems in Texas, and the proposed framework of a road flooding forecasting system that can predict land surface flooding in detail and the impacts on the road network and provide information on the spatial and temporal evolution of road network access during flooding events. It is recommended that the framework be used for identifying the transportation network-wide impacts of flood ‘hot-spots’ and assessment of transportation-related flood mitigation alternatives. The framework can also support disaster planning and emergency preparedness measures in preparation for major events. Examples may include contingency planning for deployment of barricades, mitigation of critical facilities, and large-scale evacuation planning. The methodology can provide useful outputs on system-wide costs of flooded roads that can be used to inform regional mitigation efforts.

Tran-SET Project: 21SATUTSA02

Related Organizations
Keywords

Hazardous Weather, Floods, Traffic Safety

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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