Animal-vehicle collisions (AVCs) involving ungulate species pose a serious problem in many countries, and the prediction of accidents and costs on a regional and national spatial scale is important for efficient accident mitigation. Based on the assumption that AVCs are determined by traffic volume and ungulate population dynamics, this study developed a relatively simple method for calculating and predicting the costs of current and future traffic accidents involving moose, roe deer and wild boar in Sweden. A logistic population model was assumed for all three ungulate species and econometric methods were applied to obtain population growth models based on panel data on traffic accidents, traffic load, hunting bags, hunting licenses and landscape characteristics for each Swedish county and year from 2003 to 2015. The population growth models were used to predict vehicle accidents and costs. The predicted annual discounted costs of AVCs over a 15-year period based on projected ungulate populations and traffic volume fell by 25% from 406 million USD in 2015, however the allocation of costs between ungulates differed. AVCs involving roe deer accounted for the largest share of the costs (54%), but collisions involving wild boar showed the most rapid increase over the study period because of a relatively high estimated growth rate and recent expansion of wild boar populations to several new counties. However, the predicted costs were sensitive to assumptions regarding population dynamics as well as assumptions about future hunting pressure and traffic volume.