Affiliations: Department of Statistics, University of Ghana, Legon, Ghana
Correspondence:
[*]
Corresponding author: Samuel Iddi, Department of Statistics, University of Ghana, P.O. Box LG115, Legon, Ghana. E-mail: siddi@ug.edu.gh.
Abstract: Deaths due to road accidents are a major concern to many stakeholders in Ghana especially because road accidents only come second behind malaria for cause of deaths. Statistical models can be helpful in evaluating the effect of factors responsible for mortality and morbidity during vehicular accidents. There is often a spoilt for choice on the type of models that may be used to explain a particular phenomenon. Picking a model can be based on the researcher’s knowledge or experience and the simplicity of the model. However, in common applications, the models applied are often not adequate to accurately and efficiently explain the underlying phenomenon particularly when it fails to address certain characteristics of the data. In this paper, an appropriate statistical model on the number of vehicular deaths in Ghana is fitted. The Poisson, Negative Binomial (NB), Zero-Inflation Poisson (ZIP) and Zero-Inflation Negative Binomial (ZINB) models, estimated by the method of maximum likelihood, are compared to determine the most appropriate model for the data at hand. In addition, due to the large number of explanatory variables, the backward model selection procedure was adopted to select the most significant factors associated with crash fatalities. After a careful model building process, the ZINB model was identified as the most appropriate for modelling road crash mortality. The model also identified factors such as shoulder type, time of crash, driver’s sex, road environment landmarks, among others as having significant effect on the fatalities during vehicular accidents in Ghana. It is recommended that authorities focus on installing reflective markings on the shoulders of roads and increase education of drivers in adhering to road regulations while also paying keen attention to road environmental landmarks.
