A Hybrid Efficient Short-term Traffic Flow Forecast Technology

Author

Lin, Xin ; Wang, Xiaoye ; Xiao, Yingyuan ; Zhang, Degan

Author_Institution

Tianjin Key Lab. of Intell. Comput. & Novel Software Technol., Tianjin Univ. of Technol., Tianjin, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

371

Lastpage

374

Abstract

This paper presents a hybrid short-term traffic flow forecast technology. For the uncertainty, the short-term traffic flow forecast is complicated, and the accuracy is not high. This strategy combines the RBF neural network and ant colony clustering algorithm to forecast the traffic flow. It used ant colony clustering algorithm to get the centers of hidden layer neurons. To find the best clustering result, local search is used in ant colony algorithm. The model has strong local generalization abilities and high accuracy. The simulation experiment results illuminate that the application is fairly effective.

Keywords

pattern clustering; radial basis function networks; traffic engineering computing; ant colony clustering algorithm; hybrid efficient short-term traffic flow forecast technology; intelligent transportation systems; radial basis function neural network; Clustering algorithms; Communication system traffic control; Computer vision; Educational technology; Feedforward neural networks; Intelligent transportation systems; Neural networks; Technology forecasting; Telecommunication traffic; Traffic control; RBF neural network; ant colony clustering; traffic flow forecast;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.628

Filename

5358567