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Modeling road accident fatalities with underdispersion and zero-inflated counts

In 2013, Thailand was ranked second in the world in road accident fatalities (RAFs), with 36.2 per 100,000 people. During the Songkran festival, which takes place during the traditional Thai New Year in April, the number of road traffic accidents (RTAs) and RAFs are markedly higher than on regular d...

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Autores principales: Simmachan, Teerawat, Wongsai, Noppachai, Wongsai, Sangdao, Lerdsuwansri, Rattana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671366/
https://www.ncbi.nlm.nih.gov/pubmed/36395111
http://dx.doi.org/10.1371/journal.pone.0269022
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author Simmachan, Teerawat
Wongsai, Noppachai
Wongsai, Sangdao
Lerdsuwansri, Rattana
author_facet Simmachan, Teerawat
Wongsai, Noppachai
Wongsai, Sangdao
Lerdsuwansri, Rattana
author_sort Simmachan, Teerawat
collection PubMed
description In 2013, Thailand was ranked second in the world in road accident fatalities (RAFs), with 36.2 per 100,000 people. During the Songkran festival, which takes place during the traditional Thai New Year in April, the number of road traffic accidents (RTAs) and RAFs are markedly higher than on regular days, but few studies have investigated this issue as an effect of festivity. This study investigated the factors that contribute to RAFs using various count regression models. Data on 20,229 accidents in 2015 were collected from the Department of Disaster Prevention and Mitigation in Thailand. The Poisson and Conway–Maxwell–Poisson (CMP) distributions, and their zero-Inflated (ZI) versions were applied to fit the data. The results showed that RAFs in Thailand follow a count distribution with underdispersion and excessive zeros, which is rare. The ZICMP model marginally outperformed the CMP model, suggesting that having many zeros does not necessarily mean that the ZI model is required. The model choice depends on the question of interest, and a separate set of predictors highlights the distinct aspects of the data. Using ZICMP, road, weather, and environmental factors affected the differences in RAFs among all accidents, whereas month distinguished actual non-fatal accidents and crashes with or without deaths. As expected, actual non-fatal accidents were 2.37 times higher in April than in January. Using CMP, these variables were significant predictors of zeros and frequent deaths in each accident. The RAF average was surprisingly higher in other months than in January, except for April, which was unexpectedly lower. Thai authorities have invested considerable effort and resources to improve road safety during festival weeks to no avail. However, our study results indicate that people’s risk perceptions and public awareness of RAFs are misleading. Therefore, nationwide road safety should instead be advocated by the authorities to raise society’s awareness of everyday personal safety and the safety of others.
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spelling pubmed-96713662022-11-18 Modeling road accident fatalities with underdispersion and zero-inflated counts Simmachan, Teerawat Wongsai, Noppachai Wongsai, Sangdao Lerdsuwansri, Rattana PLoS One Research Article In 2013, Thailand was ranked second in the world in road accident fatalities (RAFs), with 36.2 per 100,000 people. During the Songkran festival, which takes place during the traditional Thai New Year in April, the number of road traffic accidents (RTAs) and RAFs are markedly higher than on regular days, but few studies have investigated this issue as an effect of festivity. This study investigated the factors that contribute to RAFs using various count regression models. Data on 20,229 accidents in 2015 were collected from the Department of Disaster Prevention and Mitigation in Thailand. The Poisson and Conway–Maxwell–Poisson (CMP) distributions, and their zero-Inflated (ZI) versions were applied to fit the data. The results showed that RAFs in Thailand follow a count distribution with underdispersion and excessive zeros, which is rare. The ZICMP model marginally outperformed the CMP model, suggesting that having many zeros does not necessarily mean that the ZI model is required. The model choice depends on the question of interest, and a separate set of predictors highlights the distinct aspects of the data. Using ZICMP, road, weather, and environmental factors affected the differences in RAFs among all accidents, whereas month distinguished actual non-fatal accidents and crashes with or without deaths. As expected, actual non-fatal accidents were 2.37 times higher in April than in January. Using CMP, these variables were significant predictors of zeros and frequent deaths in each accident. The RAF average was surprisingly higher in other months than in January, except for April, which was unexpectedly lower. Thai authorities have invested considerable effort and resources to improve road safety during festival weeks to no avail. However, our study results indicate that people’s risk perceptions and public awareness of RAFs are misleading. Therefore, nationwide road safety should instead be advocated by the authorities to raise society’s awareness of everyday personal safety and the safety of others. Public Library of Science 2022-11-17 /pmc/articles/PMC9671366/ /pubmed/36395111 http://dx.doi.org/10.1371/journal.pone.0269022 Text en © 2022 Simmachan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Simmachan, Teerawat
Wongsai, Noppachai
Wongsai, Sangdao
Lerdsuwansri, Rattana
Modeling road accident fatalities with underdispersion and zero-inflated counts
title Modeling road accident fatalities with underdispersion and zero-inflated counts
title_full Modeling road accident fatalities with underdispersion and zero-inflated counts
title_fullStr Modeling road accident fatalities with underdispersion and zero-inflated counts
title_full_unstemmed Modeling road accident fatalities with underdispersion and zero-inflated counts
title_short Modeling road accident fatalities with underdispersion and zero-inflated counts
title_sort modeling road accident fatalities with underdispersion and zero-inflated counts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671366/
https://www.ncbi.nlm.nih.gov/pubmed/36395111
http://dx.doi.org/10.1371/journal.pone.0269022
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