Article: Forecasting of short-term traffic flow using LSTM ensembled with variational mode decomposition and wavelet denoising Journal: International Journal of Intelligent Systems Technologies and Applications (IJISTA) 2026 Vol.24 No.1 pp.1 - 27 Abstract: Accurate prediction of traffic flow is essential for effective traffic management. This paper proposes a long-short-term memory network ensembled with variational mode decomposition and wavelet denoising (VMD-WD-LSTM). First, the LSTM model is employed as a basic prediction model. Subsequently, variational mode decomposition (VMD) is applied to decompose the time series, followed by a stationarity test on the resulting components. The intrinsic mode functions (IMFs) with non-stationary characteristics are further denoised using wavelet denoising. Finally, the processed time series are input into the input layer of the LSTM model, respectively. The final short-term traffic flow prediction is obtained by reconstructing each time series prediction result. Six sets of traffic flow data are selected to test the model. The results show that the proposed model is superior to other four methods. Inderscience Publishers - linking academia, business and industry through research

Title: Forecasting of short-term traffic flow using LSTM ensembled with variational mode decomposition and wavelet denoising

Authors: Lulu Wang; Meiqing An; Wenlong Zhu; Wanli Xiang; Ruichun He

Addresses: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China ' School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China ' School of Management, Anhui University, Hefei, 230601, China ' School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China; Institute of Modern Logistics, Lanzhou Jiaotong University, Lanzhou, 730070, China ' School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China

Abstract: Accurate prediction of traffic flow is essential for effective traffic management. This paper proposes a long-short-term memory network ensembled with variational mode decomposition and wavelet denoising (VMD-WD-LSTM). First, the LSTM model is employed as a basic prediction model. Subsequently, variational mode decomposition (VMD) is applied to decompose the time series, followed by a stationarity test on the resulting components. The intrinsic mode functions (IMFs) with non-stationary characteristics are further denoised using wavelet denoising. Finally, the processed time series are input into the input layer of the LSTM model, respectively. The final short-term traffic flow prediction is obtained by reconstructing each time series prediction result. Six sets of traffic flow data are selected to test the model. The results show that the proposed model is superior to other four methods.

Keywords: short-term traffic flow; variational mode decomposition; VMD; wavelet denoising; LSTM network; forecasting.

DOI: 10.1504/IJISTA.2026.152033

International Journal of Intelligent Systems Technologies and Applications, 2026 Vol.24 No.1, pp.1 - 27

Received: 05 Oct 2024
Accepted: 13 Mar 2025
Published online: 04 Mar 2026 *

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Title: Forecasting of short-term traffic flow using LSTM ensembled with variational mode decomposition and wavelet denoising

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