Deep Learning Models and Tools for Disaster Evacuation and Routing

Corns, Steven M; Long, Suzanna K; Edara, Praveen; Hale, Jacob; Chang, Daeyeol; Bennett, Rick; Kutheis, Nick; Missouri University of Science and Technology. Dept. of Engineering Management and Systems Engineering; University of Idaho; University of Missouri--Columbia. Department of Civil and Environmental Engineering; WSP USA Inc.

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Deep Learning Models and Tools for Disaster Evacuation and Routing

2022-12-01
By Corns, Steven M ; Long, Suzanna K ; Edara, Praveen ; ...

[PDF-4.20 MB]

English

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Details:

Creators:

Corns, Steven M ; Long, Suzanna K ; Edara, Praveen ; Hale, Jacob ; Chang, Daeyeol ; ... More +

Corns, Steven M ; Long, Suzanna K ; Edara, Praveen ; Hale, Jacob ; Chang, Daeyeol ; Bennett, Rick ; Kutheis, Nick Less -
Corporate Creators:

Missouri University of Science and Technology. Dept. of Engineering Management and Systems Engineering ; University of Idaho ; University of Missouri--Columbia. Department of Civil and Environmental Engineering ; WSP USA Inc.

Missouri University of Science and Technology. Dept. of Engineering Management and Systems Engineering ; University of Idaho ; University of Missouri--Columbia. Department of Civil and Environmental Engineering ; WSP USA Inc. Less -
Corporate Contributors:

Missouri. Dept. of Transportation. Division of Construction and Materials ; United States. Department of Transportation. Federal Highway Administration ; Missouri Center for Transportation Innovation

Missouri. Dept. of Transportation. Division of Construction and Materials ; United States. Department of Transportation. Federal Highway Administration ; Missouri Center for Transportation Innovation Less -
Subject/TRT Terms:

[+]

Disasters Disasters And Emergency Operations Earthquakes Emergencies Evacuation Machine Learning Mercalli Intensity Scale Routes And Routing Scale Models Seismic Prospecting
Publication/ Report Number:

cmr 22-014
Resource Type:

Tech Report
Geographical Coverage:

Missouri ; United States

Missouri ; United States Less -
Edition:

Final Report (January 2022-October 2022)
Corporate Publisher:

Missouri. Department of Transportation. Construction and Materials Division
Abstract:

Engineering managers and transportations planners need robust tools to communicate evacuation routing plans following disruptions from earthquake events. The project will use the New Madrid Seismic Zone in South-East Missouri as a testbed for modeling the response to an earthquake and aftershocks at Magnitude 8+. This area was chosen as it allows solutions to specific regions with inadequate road networks, limited communications protocols, and high likelihood of structural damage for the proposed scenario. Research tasks include identifying road structure damage based on the Mercalli Intensity Scale, running traffic simulations for post-earthquake evacuation to determine the desired routes out of the area. This research will then be able to display the warning of the earthquake event along with the desired route for the end user. Effectively providing the safest navigation routes are a vital part of these planning efforts.
Format:

PDF
Funding:

MoDOT project # TR202202
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:ccb9f3019186f8797b738fd8076a5c16aba4d07597ca94fc1127ab0fa1032021
Download URL:

https://rosap.ntl.bts.gov/view/dot/65825/dot_65825_DS1.pdf
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No Additional Files

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Deep Learning Models and Tools for Disaster Evacuation and Routing

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