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A number of models have been used for estimating frequency of accidents. Weighted and simple linear regressions are common and in the recent years artificial neural network models have also been used as prediction models of accidents. Researchers need to select and use some models with the best performance particularly with the minimum of mean square errors. In this paper, traffic volume, surface condition, heavy traffic, and monthly accident data have been analysed in two Iranian major freeways named Tehran-Qom and Karaj-Qazvin-zanjan and three different kinds of models including simple and weighted linear regression and artificial neural network have been developed for estimating the number of monthly accident based on the above input variables. The well-known software of MATLAB has been used for analytical process and principle component analysis technique has been used to ensure that input variables don't have inter-relations. Principle components and loading have been calculated and results of PCA show that all input variables should be considered in modeling. The effectiveness of input variables based on T-test has been analyzed and the results show that traffic volume and surface condition have more effect in rural accidents. For models' performance comparison, the mean square errors have been considered. It can be concluded, from the results, that artificial neural network has the best performance with minimum mean square errors.