Hybrid Methods for Short-Term Traffic Flow Prediction Based on ARIMA-GARCH Model and Wavelet Neural Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
Accurate short-term traffic flow prediction is essential for real-time traffic control. A linear hybrid method and a nonlinear hybrid method for short-term traffic flow prediction are proposed with vehicle type as one concern. Traffic flow data are divided into the similar, volatile, and irregular parts. The selected methods are the autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model, the Markov model with state membership degree, and the wavelet neural network. The ARIMA-GARCH model is used to predict the similar and volatile parts, and the other methods are adopted to predict the irregular part. This paper aims at providing better prediction methods for short-term traffic flow, and comparing the advantages and disadvantages of the linear and nonlinear hybrid methods. Additionally, the impacts of vehicle type on the predicted values are analyzed. The proposed methods are tested using field data from Dalian, China, and Hefei, China. The results suggest that the developed nonlinear hybrid method should be used with vehicle type and sampling interval as concerns.
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Data Availability Statement
Some data used to support the findings of this study are available from http://www.openits.cn/ (accessed March 1, 2019). The rest of the data and the models or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research reported in this paper has been funded by the National Natural Science Foundation of China (Grant No. 51578111). The authors thank Meng Long, Lan Yang and Xiaojing Du for their assistance in English writing and proofreading. The authors acknowledge the anonymous reviewers for valuable comments.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 5, 2019
Accepted: Feb 19, 2020
Published online: Jun 12, 2020
Published in print: Aug 1, 2020
Discussion open until: Nov 12, 2020
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