Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks

被引:7
|
作者
Hu, Guojing [1 ,2 ]
Whalin, Robert W. [1 ]
Kwembe, Tor A. [3 ]
Lu, Weike [4 ]
机构
[1] Jackson State Univ, Dept Civil & Environm Engn, Jackson, MS 39217 USA
[2] Suzhou Univ Sci & Technol, Dept Civil Engn, Suzhou 215009, Jiangsu, Peoples R China
[3] Jackson State Univ, Dept Math & Stat Sci, Jackson, MS 39217 USA
[4] Soochow Univ, Sch Rail Transportat, Suzhou 215131, Jiangsu, Peoples R China
关键词
Traffic flow prediction; Secondary decomposition; DeepESN; EMPIRICAL MODE DECOMPOSITION; PASSENGER FLOW; MACHINE;
D O I
10.1016/j.physa.2023.129313
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Short-term traffic flow prediction is a significant and challenging research topic as it is closely related to the application of intelligent transportation systems. Due to the variable and random characteristics of the transportation system, raw traffic flow data often contain noise, and pre-dicting the raw data directly may reduce the accuracy and effectiveness of the prediction models. Therefore, a hybrid method is established in this research which combines denoising schemes and deep learning models to improve the prediction accuracy. The time series denoising schemes include two parts: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and wavelet packet decomposition (WPD). Firstly, the raw traffic flow data are decomposed by CEEMDAN to obtain intrinsic mode functions (IMFs) and a residual. Then the IMFs are divided into anti-persistent and persistent components through the Hurst Exponent index. The anti-persistent components are re-decomposed by the WPD algorithm, and persistent components are aggregated into one component. Finally, these components and residual are forecasted by the deep echo state network (DeepESN) model. In the experiment, to investigate the prediction performance of the proposed CEEMDAN-WPD123456-7a11-DeepESN model, the LSTM, CEEMDAN-LSTM, CEEMDAN-WPD-LSTM, DeepESN, CEEMDAN-DeepESN, CEEMDAN-WPD1-DeepESN, CEEMDAN-WPD123456-DeepESN and CEEMDAN-WPD1a6-7a11-DeepESN models are considered to be comparison models. The experimental results demonstrate that the proposed model has superior performance on both efficiency and accuracy.
引用
收藏
页数:18
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