A multivariate model that incorporates the effects of design, traffic, weather, and related interactions with design variables on reported roadside crashes is presented. By providing for a framework that accounts for all measurable effects, the model minimizes the impact of omitted variable effects. Furthermore, the presented framework accounts for partial observability effects that stem from fluctuations in environmental conditions as well as unobserved effects that contribute to heterogeneity in the traffic safety network. A sample of 318 sections 1 mi long was used for the study. These sections represent the state highway network in the state of Washington on the basis of environmental and road classification factors and therefore were used for the collection of detailed precipitation, snowfall, and temperature data in addition to roadway and roadside design and traffic parameters. The resulting model suggests that the marginal impact of weather is both in main effects and interactive form, and that even after controlling for unobserved heterogeneity and partial observability, weather effects play a statistically significant role in roadside crash occurrence. In particular, it was found that in addition to precipitation, average monthly snowfall exceeding 4 in. and interactions between snow depths and horizontal curves were found to have a statistically significant effect on roadside crash frequency probabilities. The marginal effects of these variables were also statistically significant; furthermore, the contribution of weather and related interactions to the likelihood of roadside crash frequencies was approximately 19%, design main effects contributed to 33%, and traffic and design interactions contributed to 6%. Weather interactions with design contributed to approximately 6% of the overall likelihood. Traffic as a main effect contributed 36% to the overall roadside crash likelihood.