Influence of Environmental Factors on Injury Severity Using Ordered Logit Regression Model in Limpopo Province, South Africa

Abstract

Globally, road traffic accidents are a major cause of death and severe injuries. It is estimated that the number of deaths on the world’s roads at 1.5 million per annum puts road traffic injuries as the eighth leading cause of death globally. Understanding the influence of environmental factors on deaths and severe injuries will help in policy-making and the development of strategies in Limpopo Province. We, therefore, aim to study environmental factors that influence road deaths and severe injuries and to identify whether their impact on injury severity levels varies. The study was based on secondary data on road traffic accidents obtained from the Department of Roads and Transport in Limpopo Province. The data comprised 18 029 road traffic accidents for the period January 2009–December 2015. The study found that weekends (Saturdays and Sundays) had the highest number of accidents when compared to weekdays. The proportion of observations in each severity level was not constant across explanatory variables. The generalized ordered logit regression (GOLR) models seemed to be an effective predicting model that can be adapted to determine the influence of environmental factors on injury severity compared to the ordered logit regression (OLR) model. The results of the GOLR model suggest that environmental factors such as slippery road conditions, rainy weather, and spring season lower the likelihood of severe crash occurrence. On the other hand, poor or defective road surface, time interval (6 a.m. to 11 p.m.), and provincial roads have a higher likelihood of severe crash occurrence. To decrease the severity of injuries in the province, provincial roadways must be maintained regularly.