Fuzzy Logic–Based Mapping Algorithm for Improving Animal-Vehicle Collision Data

Abstract

Animal-vehicle collisions (AVCs) cause hundreds of human and wildlife animal fatalities and tens of thousands of human and wildlife animal injuries in North America. It is estimated that AVCs cause more than $1 billion in property damage each year in the United States. Further research efforts are needed to identify effective countermeasures against AVCs. Two types of data have been widely used in AVC-related research: collision reported (CRpt) data and carcass removal (CR) data. However, previous studies showed that these two data set are significantly different, implying the incompleteness in either set of the data. Hence, this study aims at developing an algorithm to combine these two types of data to improve the completeness of data for AVC studies. A fuzzy logic–based data mapping algorithm is proposed to identify matching data from the two data sets so that data are not overcounted when combining the two data sets. The membership functions of the fuzzy logic algorithm are determined by a survey of the Washington State Department of Transportation CR staff. As verified by expert judgment collected through another survey, the accuracy of this algorithm was approximately 90%. Applying this algorithm to the WSDOT data sets identified that approximately 25∼35% of the CRpt data records have matching pairs in the CR data. Compared with the original CR data set, the combined data set has 15∼22% more records. The proposed algorithm provides an effective means for merging the CRpt data and the CR data. Such a combined data set is more complete for wildlife safety studies and may provide additional insights into understanding the issue of AVCs.