INTERREGIONAL COMMODITY FLOW MODEL USING STRUCTURAL EQUATION MODELING: APPLICATION TO THE CALIFORNIA STATEWIDE FREIGHT FORECASTING MODEL

Freight forecasting models are data intensive and may require many explanatory variables to achieve prediction accuracy. One problem, particularly in the United States, is that public data sources are usually available only at highly aggregate geographic levels, while 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 which lack explanatory power. This study addresses these challenges by considering a Structural Equation Modeling approach, that is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between industries. The goal of the proposed methodology is to design a reliable and policy sensitive modeling framework for long term commodity flow forecasting that makes the best use of public available data sources. Practicality and improvement over previously available freight generation and distribution models are the highlights of this approach.

There are two primary developed in this study. The first one is a structural commodity generation model. The second model is the Structural Equations for Multi-Commodity OD Distribution (SEMCOD) model. The models are specified and estimated based on FAF3 data. It is shown that the proposed modeling framework provides a better fit to the data than independent regression models for each commodity. The three components of the models are: direct and indirect effects, supply chain elasticities at zone level and at origin-destination level, and intra-zonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using a zoning system consisting of 97 county or sub-county zones in California