<div class="csl-bib-body">
<div class="csl-entry">Wang, C., Antos, S. E., Gosling-Goldsmith, J. G., Triveno, L. M., & Zhao, B. (2023, January). <i>A Vision-based Two-phase Framework for Urban Road Safety Evaluation: Data and Algorithms</i> [Poster Presentation]. Transportation Research Board (TRB) 102nd Annual Meeting, Washington, DC, United States of America (the). http://hdl.handle.net/20.500.12708/152696</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/152696
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dc.description.abstract
Urban transportation infrastructure is a vital component to the societal sustainability and economic growth. Its functionality and safety are key factors for quantifying its performance. It is reported that the cost of road fatalities and injuries are disproportionately high in low- and middle-income countries. Therefore, it is important for these countries to systematically assess road safety and screen road risks to mitigate potential losses and to support sustainable development of the society. However, traditionally methods for such large-scale assessment or screening require a tremendous amount of road and traffic data. To this end, we conducted a pilot project to develop a framework that only takes street view image
as the input and can predict the risk level of a road segment. The availability of street view images makes it possible to screen road risks at large-scale. The framework consists of a module to extract road information from images and a module to predict the risk level using the road information. Both modules are machine learning based. The training of the machine learning models is based on multiple sources of data, which is made available through the Development Data Partnership program. We tested the framework in two cities. The accuracy of the final prediction is 72.5%, which shows promising potential.
en
dc.language.iso
en
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dc.subject
road safety
en
dc.subject
road risk screening
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dc.subject
big data
en
dc.subject
deep learning
en
dc.title
A Vision-based Two-phase Framework for Urban Road Safety Evaluation: Data and Algorithms
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
University of Florida, United States of America (the)
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dc.contributor.affiliation
World Bank, United States of America (the)
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dc.contributor.affiliation
World Bank, United States of America (the)
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dc.contributor.affiliation
World Bank, United States of America (the)
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dc.type.category
Poster Presentation
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tuw.researchTopic.id
E1
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tuw.researchTopic.id
E2
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tuw.researchTopic.id
C6
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tuw.researchTopic.name
Energy Active Buildings, Settlements and Spatial Infrastructures
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tuw.researchTopic.name
Sustainable and Low Emission Mobility
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.value
10
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tuw.researchTopic.value
40
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tuw.researchTopic.value
50
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tuw.publication.orgunit
E230-01 - Forschungsbereich Verkehrsplanung und Verkehrstechnik
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tuw.author.orcid
0000-0002-4351-3610
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tuw.author.orcid
0000-0002-2369-7731
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tuw.event.name
Transportation Research Board (TRB) 102nd Annual Meeting
en
tuw.event.startdate
08-01-2023
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tuw.event.enddate
12-01-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Washington, DC
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tuw.event.country
US
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tuw.event.presenter
Zhao, Bingyu
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wb.sciencebranch
Wirtschaftswissenschaften
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wb.sciencebranch
Verkehrswesen
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wb.sciencebranch.oefos
5020
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wb.sciencebranch.oefos
2013
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wb.sciencebranch.value
20
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wb.sciencebranch.value
80
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item.languageiso639-1
en
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item.openairetype
conference poster not in proceedings
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item.grantfulltext
none
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_18co
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crisitem.author.dept
University of Florida
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crisitem.author.dept
World Bank
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crisitem.author.dept
World Bank
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crisitem.author.dept
World Bank
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crisitem.author.dept
E230-01 - Forschungsbereich Verkehrsplanung und Verkehrstechnik
