Error Correction and Wavelet Neural Network Based Short-term Traffic Flow Prediction
- https://doi.org/10.2991/csic-15.2015.19How to use a DOI?
- Traffic flow prediction, Wavelet neural network, ARIMA, Error correction
Real-time and accurate short-term traffic flow prediction is an important part of intelligent transportation system research. Wavelet neural network is a preferable method for predicting traffic flow. However, its performance is not satisfactory since it’s easy to fall into local optimum. This paper proposed an Error Correction Wavelet Neural Network prediction method (EC-WNN) to predict short-term traffic flow. First, we use Wavelet Neural Network to predict the traffic flow, and build the error prediction model of Auto-Regressive Integrated Moving Average (ARIMA) based on the error series. Then we use the prediction errors to update the prediction results. Finally, the real detected traffic data are used to evaluate the precision of the model, the results show that EC-WNN is superior to traditional WNN in accuracy of prediction.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yulin Pan AU - Dong Wang AU - Xiaohong Li AU - Zhu Xiao PY - 2015/07 DA - 2015/07 TI - Error Correction and Wavelet Neural Network Based Short-term Traffic Flow Prediction BT - Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication PB - Atlantis Press SP - 83 EP - 86 SN - 2352-538X UR - https://doi.org/10.2991/csic-15.2015.19 DO - https://doi.org/10.2991/csic-15.2015.19 ID - Pan2015/07 ER -