Bayesian hierarchical statistics for traffic safety modelling and forecasting

Abstract

Road traffic accidents (RTAs) represent a serious problem globally causing losses in many ways. Gulf Cooperation Council (GCC) countries have a high rate of RTAs compared to other high-income countries. In this study, a Bayesian hierarchical model was utilized for accident counts forecasting in Abu Dhabi, United Arab Emirates. This work will help traffic planners and decision makers to enhance road safety levels and decrease accident fatality rate. Accidents data along 5 years from 2008 to 2012 at 143 road sites in Abu Dhabi with 5,511 accidents were used. The proposed model considered a number of covariates; speed limit, type of road, number of lanes, type of area, weather, time, surface condition and seat belt usage. Five sites with the highest numbers of accidents were studied. Year 2012 was used to validate predictions. The model prediction accuracy was 72%.