University of British Columbia
Vancouver, British Columbia
In traditional Black Spot programs aimed at improving highway safety, locations are identified as accident prone based on the total number of accidents. This criteria, provides no consideration of whether the accidents were caused or could be corrected by road improvements. Combining accidents that are correctable and non-correctable by road improvements can be misleading and may consequently lead to misallocation of funds by road authorities. This paper presents a method to identify accident correctability using a fuzzy classification algorithm (fuzzy k-nearest neighbors). The method utilizes safety experts' knowledge to classify accidents according to their contributing factors and causes into the three road system components (the vehicle, the driver and the road environment). The membership of each accident in the road environment group is used to represent accident correctability by road improvements. The advantage of this method is its ability to identify accident prone locations that are most promising to be treated by road improvements, thus increasing the potential effectiveness of safety improvement projects. The method is tested using data from the accident database of the British Columbia Ministry of Transportation and Highways. The method and the results are described.