Analysis of Interregional Commodity Flows

Abstract

Problem statement: Commodity Flow Survey (CFS) was launched to collect comprehensive freight flow data throughout the kingdom of Thailand. The survey’s database is the most complete collection of commodity flow data in Thailand. The need to reveal interregional freight characteristics using available data from the CFS led to the objectives of this research. Approach: An origin destination matrix based on province was calibrated using a flexible Box-Cox function form. It used maximum likelihood and the backward method for calibration and Root Mean Square Error (RMSE) and Mean Relative Error (MRE) to verify the model’s performance. Independent variables were classified into three groups: origin variable, destination variable and geographic variable. The origin variable represented the behavior of the trip as generated at the place of origin. Some consumption occurred at the origin. The employment and the average plant size variables were selected for potential productivity while personal income per capita and total populations were included to explain consumption behavior at the origin. Personal income per capita and total populations were selected for destination variables which act as proxy for final demand at the destination. The third category, distance, was the most conventional friction variable for geographical variables. Results: The calibrated model revealed that origin income, origin average plant size and origin population performed poorly. Therefore these variables were eliminated. The best developed model included four strongly significant variables at a 5% level: origin employment, destination population, destination income per capita and distance. Conclusion: The results showed that the selected variables and the Box-Cox functional form were successful in explaining behavior of interregional freight transportation in Thailand. The developed model was the first interregional freight transportation model to be calibrated against Thailand commodity flow survey data.