Paper presented at the 30th Annual Southern African Transport Conference 11-14 July 2011 "Africa on the Move", CSIR International Convention Centre, Pretoria, South Africa.
Road traffic accidents (RTAs) are one of the major causes of death in Sudan, notably in
the age group of 20 to 40 that constitutes 44% of the population. Fatality rate per 10,000
vehicles is one of the highest in the world, in spite of Sudan's low vehicle-per-capita ratio
of 125 persons per car (average value over the last 20 years) Thus, it signifies the
importance of properly analyzing traffic accident data and predicting casualties. Such
studies will explore the underlying causes of RTAs and thereby develop appropriate safety measures to reduce RTA casualties. In this paper, analysis and prediction of RTAs in Sudan were undertaken using Artificial Neural Networks (ANNs). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However, the use of ANNs in the area of traffic engineering and accidents analysis is relatively new and rare. Input variables to ANN model were carefully selected through examining the strength of the correlation between the annual number of accidents and related variables such as annual population growth, gross domestic product, number of driving licenses issued annually, etc. For further validation of the model, principle component regression (PCR) technique was used to fit the same data. Both approaches attempted to model accidents using historical data on related factors, such as population, number of cars on the road and so on, covering the period from 1991 to 2009.
Forecasts for the years 2005 to 2012 were made using ANNs and principle component
regression method. Analysis using ANNs resulted in the best fit for the data with high R'.
However, both methods provided forecasts that were very similar in values. The study
showed that ANNs are more suitable for interpolation than extrapolation. Nevertheless, it
demonstrates that ANNs provide a potentially powerful tool in analyzing and forecasting
traffic accidents and casualties.