Modern road safety management programs are required to meet a number of objectives within the overall goal of reducing accidents on the road network. One of these objectives is the identification of hazardous locations through network screening. Often screening is focused on specific accident types to be mitigated by specific programs, for example, red light cameras for reducing right-angle accidents at signalized intersections. Recent methods developed for network screening utilize safety performance functions and the empirical Bayes methodology to overcome limitations in existing methods such as ignoring regression to the mean in using accident counts or rates as a ranking measure. These newer methods often are referred to as screening for the potential for safety improvement (PSI). In one version of the PSI method, sites are ranked according to their expected accident frequency; in another, sites are ranked by the expected excess frequency measured as the difference between the expected accident frequency and that expected at similar sites. This study focuses on a relatively untested method that screens for high proportions of specific accident types by using estimates of the probability that a site's observed proportion of an accident type is truly above a given critical proportion. The impacts on the method of selection for the critical proportion are examined and the efficiency of this method is evaluated by comparing its application with that of the two PSI methods for a data set of stop-controlled intersections. Results indicate that screening for high proportions may be a reasonable alternative where PSI-type approaches are not possible.