Research Article | OPEN ACCESS
A New Method for Traffic Forecasting Based on the Data Mining Technology with Artificial Intelligent Algorithms
1Wei He, 2Tao Lu and 3Enjun Wang
1Transportation Engineering Institute of Minjiang University, Fujian, 350108, China
2Hubei Province Key Laboratory of Intelligent Robot, College of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, 430070, China
3Transportation Research Center, Wuhan Institute of Technology, 430073, Wuhan, China
Research Journal of Applied Sciences, Engineering and Technology 2013 12:3417-3422
Received: October 17, 2012 | Accepted: November 23, 2012 | Published: April 10, 2013
Abstract
This study aims to investigate the traffic information forecasting based on the data mining technology. As well known, useful knowledge in traffic management system often hides in a large amount of traffic data. Generally, prior data pattern labels have been used to train the Artificial Neural Network (ANN) to identify the traffic conditions in the traffic information forecasting. The performance of the ANN models suffers from the prior information of the experts. To relieve this impact in the traffic information forecasting, a new ANN model is proposed based on the data mining technology in this study. The Self-Organized Feature Map (SOFM) is firstly employed to cluster the traffic data through an unsupervised learning and provide the labels for these data. Then the labeled data were used to train the GA-Chaos optimized RBF neural network. Herein, the GA-Chaos algorithm is used to train the RBF parameters. Experimental tests use practical data sets from the Intelligent Transportation Systems (ITS) were implemented to validate the performance of the proposed ANN model. The analyses results demonstrate that the proposed method can extract the potential patterns hidden in the traffic data and can accurately predict the future traffic state. The prediction accuracy is beyond 95%. Hence, the new data mining model can provide practical application for traffic information forecasting in the ITS system.
Keywords:
Artificial neural network, data mining, optimization, traffic forecasting,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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