For road mitigation measures to prevent roadkill and conserve landscape connectivity to be effective, the measures must be located where animals are most likely to encounter roads. However, accurate identification of road encounter hotspots is difficult when presence records are sparse and collected haphazardly, often the case with small, uncommon species. Blanding’s turtle Emydoidea blandingii (BT) is a threatened species for which road-mortality contributes to population declines. Using fortuitous detections of BT along roads, we investigated whether it is possible to predict road encounter hotspots throughout an extensive road network with such data. We applied three approaches: (1) general linear modeling (GLM) to infer landscape features associated with BT road encounter records; (2) after locating spatial clusters of encounters, GLM was used to identify landscape features associated with these hotspots; and (3) BT least cost movement paths were delineated within the landscape and sites where paths crossed roads were located. Predicted hotspots based on the modeled movement trajectories were then compared with BT road encounter hotspots. BT locations were positively associated with presence of wetlands and negatively associated with grasslands and developed land use. Hotspots were located along predicted BT least cost movement paths, indicating that behavioral movement models are useful for predicting encounter locations. Each of the three modeling approaches identified valid landscape indicators of BT road encounter hotspots, and a significant fraction of road encounter records came from a small number of hotspot sites, located along the predicted movement paths. Overall, we conclude that it is possible to generate predictive models of road encounter hotspots even when data are sparse, collected unsystematically, and subject to spatial biases in reporting across a road network, and these models can be applied throughout a road network to identify road segments that are good candidates for effective road mitigation.