Please use this identifier to cite or link to this item:
Title: Modeling freight distribution using artificial neural networks
Authors: Çelik, Hüseyin Murat
Keywords: Artificial neural networks
Commodity flows
Freight transportation
Spatial interaction models
Issue Date: Jun-2004
Publisher: Elsevier Ltd.
Source: Çelik, H. M. (2004). Modeling freight distribution using artificial neural networks. Journal of Transport Geography, 12(2), 141-148. doi:10.1016/j.jtrangeo.2003.12.003
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.
ISSN: 0966-6923
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
4750.pdfMakale271.69 kBAdobe PDFThumbnail
Show full item record

CORE Recommender


checked on Jul 8, 2023

Page view(s)

checked on Jun 19, 2023


checked on Jun 19, 2023

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.