Evaluation of Interregional Freight Generation Modelling Methods by using Nationwide Commodity Flow Survey Data

A trip generation model is one of the four parts of the classical transport planning model, which explores the volume of trip or freight at the originating and destination points of a traffic analysis zone. The process of calibrating a trip generation model needs appropriate data. Freight transport data are always robust and a powerful calibration technique is required to handle the robustness of such data. The objective of this research is to evaluate the performance of the freight generation model, calibrated by the Artificial Neural Network (ANN), against the conventional linear regression model. The 2012 Thailand commodity flow survey data from National Statistics Organization of Thailand were used for calibration. Interprovincial freight shipment data, across the kingdom of Thailand (77 origins and 77 destinations), were divided into four categories-agricultural products, industrial products, consumer products and construction material. The results indicated that the regression based model failed to accord with the regression assumption, while ANN can also provide the same performance in explaining the relationship between dependent and independent variables. ANN is considered to be a better calibration technique as the concerned data do not accord with regression assumption.