Abstract
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.
Highlights
Freight flow plays an important role in transportation planning as well as passenger transportation and is primarily concerned with the economic activities of trip origin and destination
The objective of this research is to evaluate the performance of the freight generation model calibrated by the Artificial Neural Network (ANN) against conventional linear regression model
The t statistics values of the developed equations suggest that the coefficient is useful in estimating the freight generation
Summary
Freight flow plays an important role in transportation planning as well as passenger transportation and is primarily concerned with the economic activities of trip origin and destination. The two freight data sources, which are commonly used to calibrate the transportation planning model include road side survey (Hirun and Sirisoponsilp, 2010; Kulpa, 2014) and Commodity Flow Survey (Celik, 2004; Park et al, 2012; Park and Hahn, 2015). The Commodity Flow Survey (CFS) collects shipment data from sampled shippers and the shipment data include information on Origin-Destination (O-D) of shipment, weight of shipment, value of shipment, etc. The roadside survey collects shipment data by interviewing drivers along the transportation link. CFS surveys are costlier than roadside surveys, especially the surveys conducted on a national level
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