Freight forecasting models are data intensive and require many explanatory variables to be accurate. One problem, particularly in the United States, is that public data sources are mostly at highly aggregate geographic levels but models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input–output models that lack explanatory power. This study addressed these challenges with a structural equation modeling approach that was not confined to a specific spatial structure, as spatial regression models would have been, and allowed correlations between commodities. A model for structural commodity generation that was based on freight analysis framework was specified, estimated, and shown to provide a better fit to the data than did independent regression models for each commodity. Three features of the model are discussed: indirect effects, supply chain elasticity, and intrazonal supply–demand interactions. A goal programming method was used with imputed data to validate the geographic scalability of the model.

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Several methods have been proposed to disaggregate Freight Analysis Framework (FAF) commodity flows to zonal structures of greater geographical detail. This disaggregation is usually performed on the basis of explanatory variables related to the supply and demand of goods. This paper studied a complementary procedure to determine the mode splits of disaggregated FAF flows. A goal programming approach was proposed to allocate FAF mode flow data on the basis of mode-related variables. The formulated goal programming problem minimized the deviation between the mode flow decision variables and target mode flow values, subject to given FAF mode flow information. The use of mode split models was proposed to define the problem's target values. In a sample application of the procedure, a method to estimate aggregate mode split models with FAF data was discussed. Mode split models could be used by transportation organizations that did not have access to freight mode choice models to define the goal programming problem's target mode flow values. In addition, an optimization problem was formulated to account for FAF mode flow data in the disaggregation of total commodity flows. Last, validation procedures for FAF disaggregation and mode allocation results were discussed, and an example of a validation approach was presented.