Chapter
Apr 26, 2012
Traffic Flow Prediction Based on Wavelet Analysis and Artificial Neural Network
Authors: Hongbo Li [email protected] and Yong PengAuthor Affiliations
Publication: ICLEM 2010: Logistics For Sustained Economic Development: Infrastructure, Information, Integration
Abstract
The time series of traffic flow can be deconstructed into several stationary detailed time series. A tendency time series according to the algorithm of this multi-scale is presented in this paper. Decomposed time series are forecasted with BP neural network to obtain the prediction series. Then the forecasting results are reconstructed by wavelet theory. The real detected traffic data are used to testify the precision of the model; the results show that the method of coupling multi- scale decomposition and BP neural network has advantages over the traditional BP neural network in predicted qualification- rate.
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© 2010 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Infrastructure
- Mathematical functions
- Mathematics
- Models (by type)
- Network analysis
- Neural networks
- Statistics
- Time series analysis
- Traffic analysis
- Traffic engineering
- Traffic flow
- Traffic management
- Traffic models
- Transportation engineering
- Transportation management
- Transportation networks
- Wavelets
Authors
Affiliations
Transport School, Chongqing Jiao tong University, No. 66, Xuefu Road, Chongqing 400074, China.E-mail: [email protected]
Yong Peng
Transport School, Chongqing Jiao tong University, No. 66, Xuefu Road, Chongqing 400074, China
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