Jilin Traffic Volume Impact Analysis on Economic Development

Yuanyuan Huang; Zhe Yin; Jinghu Shen

doi:10.4236/ojbm.2015.31012

Home > Journals > Business & Economics > OJBM

OJBM> Vol.3 No.1, January 2015

Share This Article:

Open Access

Jilin Traffic Volume Impact Analysis on Economic Development

Abstract Full-Text HTML XML

Download as PDF (Size:2564KB) PP. 119-124

DOI: 10.4236/ojbm.2015.31012 2,241 Downloads 2,613 Views

Author(s) Leave a comment

Yuanyuan Huang, Zhe Yin^*, Jinghu Shen

Affiliation(s)

Mathematics Department, Yanbian University, Yanji, China.

ABSTRACT

Since the reform and opening up, China’s transport development has made brilliant achievements and has a strong support for the rapid development of economy and society. In this paper, we collate, screen and analyze a total of 32-year data from 1980 to 2011 on traffic volume in Jilin Province, then we build a partial least squares regression model to the quantitatively predict and analyze the relations of transportation construction and economic development.

KEYWORDS

Factors, Gray Correlation, Partial Least Squares Regression, Forecast

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Huang, Y. , Yin, Z. and Shen, J. (2015) Jilin Traffic Volume Impact Analysis on Economic Development. Open Journal of Business and Management, 3, 119-124. doi: 10.4236/ojbm.2015.31012.

References

[1]	Yang, Z.X. (2009) Transport Volume Forecast Based on GRNN. Technology Economy in Area Communications, 4, 20-21.

[2]	Gao, W. (2009) Research on the Gray-Markov Forecast Model for Traffic Volume of Highway Transport. China Market, 15, 95-98.

[3]	Li, Y.M. and Liang, Q.O. (2010) The Prediction of Traffic Transportation Volume of Zhejiang Province with BP Neural Network. Journal of Lishui University, 32, 21-25.

[4]	Cao, X.P., He, Y.H. and Chen, G.P. (2010) Analysis of Grey Relatedness between Transportation and Regional Economy in Jiangsu from 1999 to 2008. Journal of Nantong Vocational & Technical Shipping College, 9, 1-4.

[5]	Wang, H.W. (1999) Partial Least Squares Regression Method and Its Application. National Defense Industry Press, Beijing, 201-206.

[6]	(2013) Jilin Bureau of Statistics Yearbook 2013. Statistics Press, Beijing.

[7]	Yi, D.S. (1992) Grey Theory and Methods: Summary Program Application Problem Solution. Petroleum Industry Press, Beijing.

[8]	Li, Y.K. and Yao, C.Z. (2009) Analyzing the Impact of China’s Economic Growth Factors. Statistics and Decision, 19, 104-106.

[9]	Wold, H. (1975) Soft Modelling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS) Approach. Perspectives in Probability and Statistics: Papers in Honour of M. S. Bartlett. Academic Press, London, 117-142.

[10]	Ma, M.D., Ma, X.J., Xie, Y.Z. and Ma, T. (2014) Analysis the Relationship between Ecological Footprint of Ningxia and Influencing Factors: Partial Least-Squares Regression. Acta Eclogica Sinica, 34, 682-689.