When effective programs to improve roadway safety are being developed, one of the primary tasks is to select sites for data collection. Selecting sites by ranking them simply by crash counts or crash rates is a common practice of transportation agencies because only crash data are required. Although the empirical Bayes (EB) method is a better option for site selection than this simple ranking method, the EB method requires additional data that might not be readily available or up to date, such as on annual average daily traffic and roadway characteristics, and this requirement could subsequently hinder the implementation of any EB method. This research, sponsored by the Georgia Department of Transportation, is motivated by the need to develop amore effective site selection method. The contributions of this paper include (a) proposing a Poisson distribution-based wavelet shrinkage site selection (WASSS) method that can incorporate various wavelet shrinkage methods; (b) obtaining a superior wavelet shrinkage method, the Bayesian Multiscale method (BMSM), for WASSS by evaluating various wavelet shrinkage methods; and (c) comparing the EB method and this proposed WASSS method. It is found that the proposed BMSM-based WASSS method, as compared with the EB method, produces slightly better (or at least the same) level of performance (i.e., in terms of rates of false negatives and false positives); in addition this proposed method does not require additional data (as does the EB method). This study demonstrates that the proposed WASSS method is a promising site selection alternative that requires only crash data and that performs acceptably.