Explore the vast range of titles available.

Effective measures reducing risk of animal-vehicle collisions (AVC) require defining high-risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individual AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non-random (clustered) occurrence of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE+ methods) identified factors causing local clustering of AVCs. Distance from forest (<350 m) or linear vegetation were important factors for estimating presence of clusters of AVCs; in open areas, AVC clusters were absent. Further research on effectiveness of measures reducing risk of AVC should focus on clusters of AVCs, not on the individual AVC. We recommend that state transportation agencies focus mitigation actions in forested areas.