Bayesian and Multilevel Approaches to Modelling Road Traffic Fatalities in Ghana

Abstract

The knowledge of accident rates in a country provides a useful tool for a comprehensive analysis of their causes as well as for their prevention. Prof. R. J. Smeed, in 1949, gave a regression model for estimating road traffic fatalities. The study gives a modified form of Smeed’s regression formula for estimating road traffic fatalities in Ghana. The modified regression model was found to be relatively more accurate form for estimating road traffic fatalities in Ghana, than Smeed’s formula. Bayesian model for predicting the annual regional distribution of the number of road traffic fatalities in Ghana is derived, using road traffic accident statistics data from the National Road Safety Commission, Ghana Statistical Service and Driver and Vehicle Licensing Authority. The data span 1991 to 2009. Since the parameters are assumed to vary across the various regions, they are considered to be random variables with probability distributions. The Markov chain Mont Carlo (MCMC) sampling techniques were used to draw samples from each of the posterior distribution, thereby determining the values of the unknown parameters for each region based on a given data. The study has shown that population and number of registered vehicles are predominant factors affecting road traffic fatalities in Ghana. The effect of other additional factors on road traffic fatality such as human (the driver, passenger and pedestrian), vehicle (its condition and maintenance), environmental/weather and nature of the road cannot be ruled out. Similar to the Bayesian model, where the regression parameters are considered as random variables, a Multilevel Random Coefficient (MRC) model for predicting road traffic fatalities in Ghana was also developed. In this model, the number of road traffic fatalities and the regional groups are conceptualized as a hierarchical system of road traffic fatalities and geographical regions of Ghana, with fatalities and regions defined at separate levels of this hierarchical system. Instead of estimating a separate regression equation for each of the 10 regions in Ghana, a multilevel regression analysis was applied to estimate the values of the regression coefficients for each region based on data given.