Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach

Julius Nyerere Odhiambo; Anthony Kibira Wanjoya; Anthony Gichuhi Waititu

doi:doi:10.11648/j.ajtas.20150403.24

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Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach

Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 3)

Received: 5 May 2015 Accepted: 13 May 2015 Published: 27 May 2015

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Abstract

Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 3)
DOI	10.11648/j.ajtas.20150403.24
Page(s)	178-184
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), 2024. Published by Science Publishing Group

Keywords

Road Traffic Accidents, Injuries, Artificial Neural Networks, Negative Binomial, Nairobi

References

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APA Style

Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. (2015). Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach. American Journal of Theoretical and Applied Statistics, 4(3), 178-184. https://doi.org/10.11648/j.ajtas.20150403.24

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ACS Style

Julius Nyerere Odhiambo; Anthony Kibira Wanjoya; Anthony Gichuhi Waititu. Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach. Am. J. Theor. Appl. Stat. 2015, 4(3), 178-184. doi: 10.11648/j.ajtas.20150403.24

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AMA Style

Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach. Am J Theor Appl Stat. 2015;4(3):178-184. doi: 10.11648/j.ajtas.20150403.24

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@article{10.11648/j.ajtas.20150403.24,
  author = {Julius Nyerere Odhiambo and Anthony Kibira Wanjoya and Anthony Gichuhi Waititu},
  title = {Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {3},
  pages = {178-184},
  doi = {10.11648/j.ajtas.20150403.24},
  url = {https://doi.org/10.11648/j.ajtas.20150403.24},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150403.24},
  abstract = {Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.},
 year = {2015}
}

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TY  - JOUR
T1  - Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach
AU  - Julius Nyerere Odhiambo
AU  - Anthony Kibira Wanjoya
AU  - Anthony Gichuhi Waititu
Y1  - 2015/05/27
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150403.24
DO  - 10.11648/j.ajtas.20150403.24
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 178
EP  - 184
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150403.24
AB  - Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.
VL  - 4
IS  - 3
ER  -

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Author Information

Julius Nyerere Odhiambo

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Anthony Kibira Wanjoya

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Anthony Gichuhi Waititu

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya

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APA Style

Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. (2015). Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach. American Journal of Theoretical and Applied Statistics, 4(3), 178-184. https://doi.org/10.11648/j.ajtas.20150403.24

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ACS Style

Julius Nyerere Odhiambo; Anthony Kibira Wanjoya; Anthony Gichuhi Waititu. Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach. Am. J. Theor. Appl. Stat. 2015, 4(3), 178-184. doi: 10.11648/j.ajtas.20150403.24

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AMA Style

Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach. Am J Theor Appl Stat. 2015;4(3):178-184. doi: 10.11648/j.ajtas.20150403.24

Copy | Download

@article{10.11648/j.ajtas.20150403.24,
  author = {Julius Nyerere Odhiambo and Anthony Kibira Wanjoya and Anthony Gichuhi Waititu},
  title = {Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {3},
  pages = {178-184},
  doi = {10.11648/j.ajtas.20150403.24},
  url = {https://doi.org/10.11648/j.ajtas.20150403.24},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150403.24},
  abstract = {Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach
AU  - Julius Nyerere Odhiambo
AU  - Anthony Kibira Wanjoya
AU  - Anthony Gichuhi Waititu
Y1  - 2015/05/27
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150403.24
DO  - 10.11648/j.ajtas.20150403.24
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 178
EP  - 184
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150403.24
AB  - Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.
VL  - 4
IS  - 3
ER  -

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