American Society of Civil Engineers


Forecasting Freight Volume Based on an Entropy Combination Method


by Xiao-feng Liu, (School of Transportation Engineering, Tongji University, P.O. Box: 201804, Shanghai, China. E-mail: microbreeze@126.com), Daniel (Jian) Sun, (School of Transportation Engineering, Tongji University, P.O. Box: 201804, Shanghai, China. E-mail: microbreeze@126.com), Rong-yi Du, (School of Transportation Engineering, Tongji University, P.O. Box: 201804, Shanghai, China. E-mail: microbreeze@126.com), and Zhong-ren Peng, (School of Transportation Engineering, Tongji University, P.O. Box: 201804, Shanghai, China; and Department of Urban and Regional Planning, University of Florida, Gainesville, P.O. Box: 115701, Florida, USA. Email: microbreeze@126.com. E-mail: zpeng@dcp.ufl.edu)
Section: Transportation Management, Operation Technology and Systems, pp. 1143-1148, (doi:  http://dx.doi.org/10.1061/41127(382)122)

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Document type: Conference Proceeding Paper
Part of: ICCTP 2010: Integrated Transportation Systems: Green, Intelligent, Reliable
Abstract: Freight volume forecasting is essential for transportation network planning, construction and management. However, it is generally difficult to forecast the freight volume accurately by using any single method. Combination method can effectively improve forecasting accuracy, and consequently is widely utilized. Based on the entropy theory, a parallel grey neural network combination model is constructed to forecast freight volume. The effectiveness and feasibility of the entropy combination method is demonstrated by a numerical example provided.


ASCE Subject Headings:
Forecasting
Freight transportation
Entropy methods