Probabilistic model data of spatial-dependent crashes for ranking risk of road segments

This article presents the databases analyzed and used to evaluate the risk of segment-based roads resulting from traffic crashes for three main motorways in UK from 2010 to 2014. The raw database is collection to many partial data for variables related to compute the crashes rates for each segment....

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Detalles Bibliográficos
Autores principales: Kadhem, Safaa K., Hewson, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Mathematics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6926288/
https://www.ncbi.nlm.nih.gov/pubmed/31890806
http://dx.doi.org/10.1016/j.dib.2019.104966
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author Kadhem, Safaa K.
Hewson, Paul
author_facet Kadhem, Safaa K.
Hewson, Paul
author_sort Kadhem, Safaa K.
collection PubMed
description This article presents the databases analyzed and used to evaluate the risk of segment-based roads resulting from traffic crashes for three main motorways in UK from 2010 to 2014. The raw database is collection to many partial data for variables related to compute the crashes rates for each segment. These data were used to develop and select the best Bayesian probabilistic model presented in our research article (Kadhem et al., 2018) [1]. The data provided in this article would be an important source for studies that require evaluating statistical models and also to improve and develop the plans of traffic safety.
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spelling pubmed-69262882019-12-30 Probabilistic model data of spatial-dependent crashes for ranking risk of road segments Kadhem, Safaa K. Hewson, Paul Data Brief Mathematics This article presents the databases analyzed and used to evaluate the risk of segment-based roads resulting from traffic crashes for three main motorways in UK from 2010 to 2014. The raw database is collection to many partial data for variables related to compute the crashes rates for each segment. These data were used to develop and select the best Bayesian probabilistic model presented in our research article (Kadhem et al., 2018) [1]. The data provided in this article would be an important source for studies that require evaluating statistical models and also to improve and develop the plans of traffic safety. Elsevier 2019-12-09 /pmc/articles/PMC6926288/ /pubmed/31890806 http://dx.doi.org/10.1016/j.dib.2019.104966 Text en © 2019 The Authors http://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 Mathematics
Kadhem, Safaa K.
Hewson, Paul
Probabilistic model data of spatial-dependent crashes for ranking risk of road segments
title Probabilistic model data of spatial-dependent crashes for ranking risk of road segments
title_full Probabilistic model data of spatial-dependent crashes for ranking risk of road segments
title_fullStr Probabilistic model data of spatial-dependent crashes for ranking risk of road segments
title_full_unstemmed Probabilistic model data of spatial-dependent crashes for ranking risk of road segments
title_short Probabilistic model data of spatial-dependent crashes for ranking risk of road segments
title_sort probabilistic model data of spatial-dependent crashes for ranking risk of road segments
topic Mathematics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6926288/
https://www.ncbi.nlm.nih.gov/pubmed/31890806
http://dx.doi.org/10.1016/j.dib.2019.104966
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AT hewsonpaul probabilisticmodeldataofspatialdependentcrashesforrankingriskofroadsegments

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