This study investigates the relationship between crash frequencies, roadway design and use
features by utilizing the benefits of clustered panel data. Homogeneous high-speed roadway
segments across the State of Washington were grouped using TwoStep Cluster Analysis
technique, resulting in grouped observations with reasonably continuous crash count values.
This permitted application of both fixed- and random-effects linear regression models for the
total number of crashes per million vehicle miles traveled (VMT). A crash severity model also
was estimated, using an ordered logistic regression, allowing transformation of total crash counts
into counts by severity. Speed limit information is found to be very valuable in predicting crash
rates, and the models are seemingly able to predict “optimal” speed limits in order to minimize
crash rates and crash costs. However, speed limits may have biased coefficients, most likely
attributable to unobserved safety-related effects. For the “average” high-speed segment in the
data set, a minimum expected crash cost is achieved at a speed limit of 70 mi/h, while the
maximum crash rate is predicted to occur at a speed limit of 43.5 mi/h. While these calculations
may not be realistic, the models appear to accurately predict crash rates (R2 of 0.90 for total crash
count) and the results provide useful information for a variety of design and use effects. For
example, crashes are more frequent on shorter horizontal curves, while uphill segments with
wider medians are found to experience less severe crashes.