Traffic status evaluation based on fuzzy clustering and rbf neural network

Author

Xiaofeng Liu ; Sun, D. ; Yuntao Chang ; Zhongren Peng

Author_Institution

Sch. of Transp. Eng., Tongji Univ., Shanghai, China

Volume

3

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1405

Lastpage

1408

Abstract

This paper introduces the C-means fuzzy clustering method to evaluate the road traffic status. During the analysis, road traffic status was categorized into four types by using ISODATA algorithm based on expert knowledge. Meanwhile, RBF neural network classification model was established to evaluate the road traffic status. The implementation results showed that the proposed method was capable of evaluating road traffic status, and reflecting the related quantitative fluctuations.

Keywords

fuzzy set theory; pattern clustering; radial basis function networks; road traffic; traffic engineering computing; C-means fuzzy clustering method; ISODATA algorithm; RBF neural network classification model; expert knowledge; road traffic status evaluation; Algorithm design and analysis; Artificial neural networks; Classification algorithms; Clustering algorithms; Data models; Probes; Roads; ISODATA algorithm; RBF neural network; Road traffic status evaluation; fuzzy C-means clustering; traffic congestion;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on

Conference_Location

Yantai

Print_ISBN

978-1-4244-5931-5

Type

conf

DOI

10.1109/FSKD.2010.5569188

Filename

5569188