This paper describes the estimation of Poisson regression models for predicting both single and multi-vehicle highway crash rates as a function of traffic density and land use, as well as ambient light conditions and time of day. The study focuses on seventeen rural, two-lane highway segments, each one-half mile in length with varying land use patterns and where actual hourly exposure values are available in the form of observed traffic counts. Land-use effects are represented by the number of driveways of various types on each segment. Hourly exposure is represented for single-vehicle crashes as the total vehicle miles traveled and volume/capacity ratio; for multi-vehicle crashes it is the product of the hourly volumes on the main highway and the roads intersecting it along the study segment. For single-vehicle crashes, the following variables were found to be significant, with a positive or negative effect as noted: daytime (06:00-19:00 h, negative effect), the natural log of the segment volume/capacity ratio (negative), percent of the segment with no passing zones (positive), shoulder width (positive), number of intersections (negative), and driveways (mixed effects by type). Good multi-vehicle crash prediction models had quite different variables: daylight conditions from 10:00-15:00 and 15:00-19:00 h (positive), number of intersections (negative), and driveways (positive for all types). The results show that traffic intensity explains differences in crash rates even when controlling for time of day and light conditions, and that these effects are quite different for single and multi-vehicle crashes. Suggestions for future research are also given.