Road freight transportation between provinces of a country has an important effect on the traffic flow of intercity transportation networks. Therefore, an accurate estimation of the road freight transportation for provinces of a country is so crucial to improve the rural traffic operation in a large scale management. Accordingly, the focused case study database in this research is the information related to Iran’s provinces in the year 2008. Correlation between road freight transportation with variables such as transport cost and distance, population, average household income and Gross Domestic Product (GDP) of each province is calculated. Results clarify that the population is the most effective factor in the prediction of provinces’ transported freight. Linear Regression Model (LRM) is calibrated based on the population variable, and afterwards Fuzzy Regression Algorithm (FRA) is generated on the basis of the LRM. The proposed FRA is an intelligent modified algorithm with an accurate prediction and fitting ability. This methodology can be significantly useful in macro-level planning problems where decreasing prediction error values is one of the most important concerns for decision makers. In addition, Back-Propagation Neural Network (BPNN) is developed to evaluate the prediction capability of the models and to be compared with FRA. According to the final results, the modified FRA estimates road freight transportation values more accurately than the BPNN and LRM. Finally, in order to predict the road freight transportation values, the reliability of the calibrated models is analyzed using the information of the year 2009. Results show higher reliability for the proposed modified FRA.
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