This study evaluates the influence of roadway, weather and accidents conditions, and type of traffic control on accident severity (number of person killed) using Negative Binomial and Poisson regression models. Information on accident severity and roadway and weather conditions was obtained from the Michigan Department of Transportation Accident Database. Negative Binomial (NB) and Poisson regression models were deployed to measure the association between accident severity and roadway, weather and accidents conditions. NB regression model results presented that monthly, daily, hourly and weekday variations are not statistically significant on accident severity (number of persons killed). However, Poisson regression results were the reverse with respect to these variables. Type of traffic control was also found to be not statistically significant. Number of vehicles involved, crash type (overturn, rear-end, side-swipe, head-on, hit object, and so on), injury types (A, B, C), number of uninjured, number of occupants and weather conditions are statistically significant at 0.05 level. Light and surface conditions were also statistically significant at 0.10 level. The findings of the Poisson regression are very similar to NB regression but the parameter estimations are little bit different from those determined by NB regression. The results are in agreement with professional judgments with respect to the factors affecting the accident severity on highway crashes.