Abstract:
Several studies have examined a wide range of accident risk factors affecting road safety. The purpose of this study is to examine the main traffic accident factors that affect the severity of road segments. The practical objective of the article is to assist specialists in identifying risk patterns both from a spatial and casualty point of view. To achieve the desired goals, accidents of a road network have been analyzed through three major steps; segmentation, black spot identification, and decision analysis. A new spatial clustering methodology has been used to divide accidents into smaller groups (or clusters) based on their spatial aggregations. The spatial characteristics are argued to be an important factor, in revealing the heterogeneity between accident data. Then, the empirical Bayesian has been applied to rank the resulted segments by severity level. During this step, the technique of decision rules has been applied to identify the main contributors to accidents in certain segments. The result shows that there is a significant relationship between the accident severity level and the traffic and geometrical characteristics (i.e. speed limits, average daily traffic, path shape) of road segments. The results also revealed that the closer the road to secure and non-hazardous road environmental conditions, the lower the risk level of the road segment.
