A Combination Predicted Model of Short Term Traffic Flow
This paper proposes a combination forecasting model for short-term traffic flow based on a wavelet neural network. The model has three stages: the relevant forecasting variable to the traffic flow is selected by use data mining technology (such as the genetic algorithm); a training pattern of wavelet neural network that is similar to the forecast term is carried out by using data mining technology; and the wavelet neural network is then used to forecast the traffic flow. The authors used the traffic flow at Xinhua Street in Huhehot (China) to demonstrate that this model has a higher precision and reliability than the grey model and the BP artificial neural network model. The authors conclude that this wavelet neural network-based model provides a new, reliable, and effective strategy to forecast short-term traffic flow of nodes in urban road networks.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/7560323553
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Corporate Authors:
Institute of Electrical and Electronics Engineers (IEEE)
Operations Center, 445 Hoes Lane, P.O. Box 1331
Piscataway, NJ United States 08855-1331 -
Authors:
- Bin-sheng, Liu
- Zhan-wen, Xing
- Hai-tao, Yang
- Yu-peng, Hou
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Conference:
- 2006 International Conference on Management Science and Engineering
- Location: Lille , France
- Date: 2006-10-5 to 2006-10-7
- Publication Date: 2007
Language
- English
Media Info
- Media Type: Print
- Features: References;
- Pagination: pp 2075-2080
Subject/Index Terms
- TRT Terms: Data mining; Forecasting; Genetic algorithms; Mathematical models; Neural networks; Optimization; Simulation; Traffic; Traffic engineering; Wavelets
- Uncontrolled Terms: Grey relational analysis
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01054245
- Record Type: Publication
- ISBN: 7560323553
- Files: TRIS
- Created Date: Jul 24 2007 1:37PM