A new traffic flow prediction model based on cosine similarity variational mode decomposition, extreme learning machine and iterative error compensation strategy

Yang, Huirong; Cheng, Yuanxun; Li, Guohui

doi:10.1016/j.engappai.2022.105234

Cited by 26 publications

(4 citation statements)

References 41 publications

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“…Wu [17] utilized both the CEEMDAN algorithm and different machine learning models to forecast shortterm traffic data. Yang [18] utilized improved VMD and an Extreme Learning Machine (ELM) model for traffic flow prediction. However, there are still many shortcomings in these methods.…”

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%

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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“…This also proves the superiority of SSA in improving VMD. In order to test the significant difference between SSA-VMD and the two comparison models statistically, this paper uses Diebol-Mariano (DM) statistics [ 60 ] to test the comparison model and SSA-VMD. The results of DM test is shown in Table 7 .…”

Section: Simulation Experimentsmentioning

confidence: 99%

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

Yang

Liu

2023

Computers in Biology and Medicine

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“…The results demonstrated that this model is versatile and well-adapted for forecasting on various road segments. Yang et al [24] proposed a model that integrates Cosine Similarity Variational Mode Decomposition (CSVMD), Extreme Learning Machine (ELM), and an Iterative Error Compensation Strategy to enhance Traffic Flow Density (TFD) prediction. Results from testing indicates that this model outperforms others in terms of prediction accuracy and proves to be highly effective in TFD forecasting applications.…”

Section: Introductionmentioning

confidence: 99%

Short-term traffic flow prediction based on combination of SSA and LSTM

Song,

Chang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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A new traffic flow prediction model based on cosine similarity variational mode decomposition, extreme learning machine and iterative error compensation strategy

Cited by 26 publications

References 41 publications

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

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

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

Short-term traffic flow prediction based on combination of SSA and LSTM

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