A hybrid efficient short-term traffic flow forecast technology

Authors:
Xin Lin;Xiaoye Wang;Yingyuan Xiao;Degan Zhang
Affiliations:
Tianjin Key Lab of Intelligent Computing & Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...;Tianjin Key Lab of Intelligent Computing & Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...;Tianjin Key Lab of Intelligent Computing & Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...;Tianjin Key Lab of Intelligent Computing & Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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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.