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

Hu, Guojing; Whalin, Robert W.; Kwembe, Tor A.; Lu, Weike

doi:10.1016/j.physa.2023.129313

Cited by 6 publications

(1 citation statement)

References 54 publications

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“…Zhao et al [24] applied a secondary decomposition technique and an ELM model for short-term traffic flow prediction. Hu [25] combined denoising schemes and an echo state network for short-term traffic flow forecasting. Li et al [26] decomposed the carbon price time series data using CEEMD and VMD, and BPNN was used to build forecasting models.…”

Section: Data Decompositionmentioning

confidence: 99%

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

Bing,

Zhao,

Ren

et al. 2024

Sustainability

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Because of the random volatility of traffic data, short-term traffic flow forecasting has always been a problem that needs to be further researched. We developed a short-term traffic flow forecasting approach by applying a secondary decomposition strategy and CNN–Transformer model. Firstly, traffic flow data are decomposed by using a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Secondly, the IMF1 obtained from the CEEMDAN is further decomposed into some sub-series by using Variational Mode Decomposition (VMD) algorithm. Thirdly, the CNN–Transformer model is established for each IMF separately. The CNN model is employed to extract local spatial features, and then the Transformer model utilizes these features for global modeling and long-term relationship modeling. Finally, we obtain the final results by superimposing the forecasting results of each IMF component. The measured traffic flow dataset of urban expressways was used for experimental verification. The experimental results reveal the following: (1) The forecasting performance achieves remarkable improvement when considering secondary decomposition. Compared with the VMD-CNN–Transformer, the CEEMDAN-VMD-CNN–Transformer method declined by 25.84%, 23.15% and 22.38% in three-step-ahead forecasting in terms of MAPE. (2) It has been proven that our proposed CNN–Transformer model could achieve more outstanding forecasting performance. Compared with the CEEMDAN-VMD-CNN, the CEEMDAN-VMD-CNN–Transformer method declined by 13.58%, 11.88% and 11.10% in three-step-ahead forecasting in terms of MAPE.

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Section: Data Decompositionmentioning

confidence: 99%