The idea being tested is identifying roadway hotspots by using a fixed crash rate threshold to flag segments for further review. In this approach, each road segment’s crash rate—accounting for how much traffic flows there (exposure)—is compared to a predefined critical rate. If the observed rate exceeds that threshold, the segment is considered a hotspot and worth closer examination. This thresholding helps distinguish unusual clustering of crashes from normal random variation due to traffic levels. This differs from regression analysis, which builds a statistical model to explain how crashes relate to factors like traffic volume, road geometry, and environment rather than using a fixed threshold to flag hotspots. It also differs from benefit-cost analysis, which weighs the costs and benefits of safety investments, and from time-series forecasting, which predict future crashes based on past trends rather than screening current locations for excess crashes.