scholarly journals Prediction for Short-Term Traffic Flow Based on Optimized Wavelet Neural Network Model

2015 ◽  
Vol 7 (2) ◽  
pp. 183-192 ◽  
Author(s):  
Tao Li ◽  
Liu Sheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Wavelet Neural Network ◽  
Short Term

scholarly journals Short-term forecasting of available parking space using wavelet neural network model

2015 ◽  
Vol 9 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Yanjie Ji ◽  
Phil Blythe ◽  
Weihong Guo ◽  
Wei Wang ◽  
Dounan Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Wavelet Neural Network ◽  
Parking Space ◽  
Short Term ◽  
Short Term Forecasting

scholarly journals Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jinxing Shen ◽  
Wenquan Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Traffic Volume ◽  
Wavelet Neural Network ◽  
Time Interval ◽  
Short Term ◽  
Comparison Results ◽  
Hidden Neurons

In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM) is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN). The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing
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