Abstract
This study examines the feasibility of Bayesian and data envelopment techniques for robust identification of safety priorities in the Washington State DOT improvement program. Findings indicate that the methods employed in this study have potential for reliable predictions and identification of safety needs. A case study dataset of high accident locations was used to make this assessment. (Author/publisher)