The frequency of accidents, as well as statistical models of accident frequency, are often used as a foundation for prioritizing improvements to roadway safety by several transportation organizations. However, the use of accident severities in safety programming has frequently been restricted to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (slight damage, serious damage) which is required in order to properly evaluate the advantages of several competing efforts aimed at improving safety. Within the scope of this research, we provide a sufficient modeling technique that may be used to get a better understanding of the accident severity level that occur on highway segments, as well as the influence of traffic characteristics such as annual daily flow, percentage of heavy vehicle and free flow speed. The modeling approach that used in this research (random parameters model) provides the possibility that the estimated values of the model parameters might differ from one road segment to another to account the heterogeneity of the independent variables. The estimated random parameters models are developed using accident severity data and traffic characteristics data that obtained from Fallujha – Al-Qaeam rural multilane road in Al-Anbar province, Iraq. The results of the estimated results suggest annual daily flow, percentage of heavy vehicle and free flow speed all have significant effect on the accident severity level. For the purpose of prioritizing highway safety improvements, a number of government transportation authority’s base their decisions on accident rates and statistical models of accident rates. The random parameters models have been shown to have significant potential for use as a sufficient method in the programming of highway safety.