A Bayesian Model for Predicting Road Traffic Fatalities in Ghana

Abstract

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 Monte 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 error (due to the driver, passenger and/or pedestrian), vehicle (its condition and maintenance), environmental/weather and nature of the road cannot be ruled out.