Modeling freight distribution using artificial neural networks

Abstract

Studies about freight distribution modeling are limited due to the limitations in data availability. Existing studies in this subject, generally either use the conventional gravity models or the regression based models as modeling techniques. The present study, using the 1993 US Commodity Flow Survey Data, models inter-regional commodity flows for 48 continental states of the US with three different artificial neural networks (ANN). The results are compared with those of Celik and Guldmann's (2002) Box–Cox Regression Model. The ANN using conventional gravity model variables provides a slight improvement with respect to this Box–Cox model. However, the ANNs using theoretically relevant variables provide surprising improvements in comparison to the Box–Cox model. It is concluded that ANN architecture is a very promising technique for predicting short-term inter-regional commodity flows.