The Mining and Analysis on Traffic Condition of Business Circle Based on C5.0

Article Preview

Abstract:

Traffic congestion has increasingly become a problem that couldnt be ignored, and how to control traffic scientifically and effectively has become more and more important. The article uses the decision tree algorithm C5.0 to build the model, through to the mining and analysis on the traffic data of business circles, analyzing the traffic condition of business circles according to the characteristics of them. It is more targeted to solve their traffic problems, and can be helpful for people to go out.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

4468-4471

Citation:

Online since:

March 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Jianhu Zhen, Xiaohui Lin, Lixi Zhen. Predicting traffic congestion state Based on the markov chain model, Traffic Engineering, no. 12(2012), pp.76-79.

Google Scholar

[2] Yang Fan, Zhongzhen Yan. Hybrid neural network mining model in the research of traffic flow prediction. Small microcomputer system, no. 9(2012), p.1978-(1981).

Google Scholar

[3] Tingyang Li, Luan Xin, Zhenghong Peng. Application of traffic mode choice model based on decision tree algorithm. Journal of wuhan university (Engineering Science), vol. 46, no. 3(2013), pp.354-358.

Google Scholar

[4] Fan Ming, Xiaofeng Meng. The concept of data mining and technology, Mechanical industry press (2012), Beijing.

Google Scholar

[5] Guiyan Jiang. Road traffic state discrimination technology and application. People's traffic press (2004), Beijing.

Google Scholar

[6] Guo Wei, Danya Yao, Fu Yi. The feature extraction of regional traffic flow and the assessment method of the traffic condition, Highway traffic science and technology, vol. 22, no. 7(2005), pp.101-114.

Google Scholar

[7] The ministry of public security traffic management bureau. The evaluation index system of urban road traffic management (2002).

Google Scholar