Volume 6 Number 9 (Sep. 2011)
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JSW 2011 Vol.6(9): 1680-1687 ISSN: 1796-217X
doi: 10.4304/jsw.6.9.1680-1687

Road Traffic Freight Volume Forecast Using Support Vector Machine Combining Forecasting

Shang Gao1, Zaiyue Zhang2, Cungen Cao2
1School of Computer Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212003, China
2Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China


Abstract—The grey system forecasting model, neural network forecasting model and support vector machine forecasting model are proposed in this paper. Taking the road goods traffic volume from year of 1996 to 2003 in the whole country as a study case, the forecasting results are got by three methods. From the forecasting results, we can conclude that the accuracy of the support vector machine forecasting method is higher. Analyzing the characteristic of combining forecasting method, based on grey system forecasting model, neural network forecasting model and support vector machine forecasting model, the linear combining forecasting model, neural network combining forecasting model and support vector machine combining forecasting model are set up. Compared with single prediction methods, linear combining forecasting method and neural network combining forecasting model, the accuracy of the support vector machine combining forecasting method is higher.

Index Terms—grey system, neural network, support vector machine, combining forecasting, traffic volume

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Cite: Shang Gao, Zaiyue Zhang, Cungen Cao, "Road Traffic Freight Volume Forecast Using Support Vector Machine Combining Forecasting," Journal of Software vol. 6, no. 9, pp. 1680-1687, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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