Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 3, May 2015, Pages: 178-184
Received: May 5, 2015; Accepted: May 13, 2015; Published: May 27, 2015
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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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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.
Road Traffic Accidents, Injuries, Artificial Neural Networks, Negative Binomial, Nairobi
To cite this article
Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu, Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 3, 2015, pp. 178-184. doi: 10.11648/j.ajtas.20150403.24
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