Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model

Metadel Azeze; Awoke Seyoum; Endalew Tesfa; Legesse Kassa Debusho

doi:doi:10.11648/j.ijtam.20200606.12

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Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model

Metadel Azeze, Awoke Seyoum, Endalew Tesfa, Legesse Kassa Debusho

Published in International Journal of Theoretical and Applied Mathematics (Volume 6, Issue 6)

Received: 22 October 2020 Accepted: 10 November 2020 Published: 16 December 2020

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Abstract

Road traffic crashes are a major socio-economic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.

Published in	International Journal of Theoretical and Applied Mathematics (Volume 6, Issue 6)
DOI	10.11648/j.ijtam.20200606.12
Page(s)	95-104
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Road Traffic Crash, GLM, Over Dispersion, AIC, BIC, Count Data, Ethiopia

References

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APA Style

Metadel Azeze, Awoke Seyoum, Endalew Tesfa, Legesse Kassa Debusho. (2020). Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model. International Journal of Theoretical and Applied Mathematics, 6(6), 95-104. https://doi.org/10.11648/j.ijtam.20200606.12

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ACS Style

Metadel Azeze; Awoke Seyoum; Endalew Tesfa; Legesse Kassa Debusho. Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model. Int. J. Theor. Appl. Math. 2020, 6(6), 95-104. doi: 10.11648/j.ijtam.20200606.12

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AMA Style

Metadel Azeze, Awoke Seyoum, Endalew Tesfa, Legesse Kassa Debusho. Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model. Int J Theor Appl Math. 2020;6(6):95-104. doi: 10.11648/j.ijtam.20200606.12

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@article{10.11648/j.ijtam.20200606.12,
  author = {Metadel Azeze and Awoke Seyoum and Endalew Tesfa and Legesse Kassa Debusho},
  title = {Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model},
  journal = {International Journal of Theoretical and Applied Mathematics},
  volume = {6},
  number = {6},
  pages = {95-104},
  doi = {10.11648/j.ijtam.20200606.12},
  url = {https://doi.org/10.11648/j.ijtam.20200606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20200606.12},
  abstract = {Road traffic crashes are a major socio-economic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.},
 year = {2020}
}

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TY  - JOUR
T1  - Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model
AU  - Metadel Azeze
AU  - Awoke Seyoum
AU  - Endalew Tesfa
AU  - Legesse Kassa Debusho
Y1  - 2020/12/16
PY  - 2020
N1  - https://doi.org/10.11648/j.ijtam.20200606.12
DO  - 10.11648/j.ijtam.20200606.12
T2  - International Journal of Theoretical and Applied Mathematics
JF  - International Journal of Theoretical and Applied Mathematics
JO  - International Journal of Theoretical and Applied Mathematics
SP  - 95
EP  - 104
PB  - Science Publishing Group
SN  - 2575-5080
UR  - https://doi.org/10.11648/j.ijtam.20200606.12
AB  - Road traffic crashes are a major socio-economic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.
VL  - 6
IS  - 6
ER  -

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Author Information

Metadel Azeze

Department of Statistics, College of Science, Debre Markos University, Debre Markos, Ethiopia
Awoke Seyoum

Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia
Endalew Tesfa

Department of Statistics, College of Science, Debre Markos University, Debre Markos, Ethiopia
Legesse Kassa Debusho

Department of Statistics, College of Science, University of South Africa, Pretoria, South Africa

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APA Style

Metadel Azeze, Awoke Seyoum, Endalew Tesfa, Legesse Kassa Debusho. (2020). Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model. International Journal of Theoretical and Applied Mathematics, 6(6), 95-104. https://doi.org/10.11648/j.ijtam.20200606.12

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ACS Style

Metadel Azeze; Awoke Seyoum; Endalew Tesfa; Legesse Kassa Debusho. Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model. Int. J. Theor. Appl. Math. 2020, 6(6), 95-104. doi: 10.11648/j.ijtam.20200606.12

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AMA Style

Metadel Azeze, Awoke Seyoum, Endalew Tesfa, Legesse Kassa Debusho. Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model. Int J Theor Appl Math. 2020;6(6):95-104. doi: 10.11648/j.ijtam.20200606.12

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@article{10.11648/j.ijtam.20200606.12,
  author = {Metadel Azeze and Awoke Seyoum and Endalew Tesfa and Legesse Kassa Debusho},
  title = {Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model},
  journal = {International Journal of Theoretical and Applied Mathematics},
  volume = {6},
  number = {6},
  pages = {95-104},
  doi = {10.11648/j.ijtam.20200606.12},
  url = {https://doi.org/10.11648/j.ijtam.20200606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20200606.12},
  abstract = {Road traffic crashes are a major socio-economic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.},
 year = {2020}
}

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TY  - JOUR
T1  - Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model
AU  - Metadel Azeze
AU  - Awoke Seyoum
AU  - Endalew Tesfa
AU  - Legesse Kassa Debusho
Y1  - 2020/12/16
PY  - 2020
N1  - https://doi.org/10.11648/j.ijtam.20200606.12
DO  - 10.11648/j.ijtam.20200606.12
T2  - International Journal of Theoretical and Applied Mathematics
JF  - International Journal of Theoretical and Applied Mathematics
JO  - International Journal of Theoretical and Applied Mathematics
SP  - 95
EP  - 104
PB  - Science Publishing Group
SN  - 2575-5080
UR  - https://doi.org/10.11648/j.ijtam.20200606.12
AB  - Road traffic crashes are a major socio-economic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.
VL  - 6
IS  - 6
ER  -

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