A new traffic flow prediction model based on cosine similarity variational mode decomposition, extreme learning machine and iterative error compensation strategy. (October 2022) - Yang, Hong; Cheng, Yuanxun; Li, Guohui
Abstract: Traffic flow data (TFD) prediction is a hot research area in intelligent transportation system. TFD is non-stationary and nonlinear, so it has become a challenge to predict it accurately. In order to improve TFD prediction accuracy, a new TFD prediction model based on cosine similarity variational mode decomposition (CSVMD), extreme learning machine (ELM), and iterative error compensation strategy, named CSVMD-ELM-error, is proposed. To solve mode number K value selection of variational mode decomposition, CSVMD is proposed, which realizes the self-adaptive determination of K value. The idea of CSVMD-ELM-error is roughly as follows. Firstly, CSVMD decomposes TFD into a series of intrinsic mode functions (IMFs), and ELM is established for each IMF component. Then, in order to further improve the prediction accuracy, ELM is used to correct the prediction error of each IMF. Finally, the revised IMF error results and the IMF prediction results are reconstructed to complete the prediction. Four TFDs and nine comparison models are used for simulation experiment, the experimental result shows that CSVMD-ELM-error has the best prediction accuracy and has an effective application in TFD prediction. Graphical abstract: Highlights: To our knowledge, this is the first work that uses VMD for traffic flow data (TFD) prediction. Variational mode decomposition based on cosine similarity (CSVMD) is proposed. This paper innovatively applies the error correction strategy to predict TFD. Prediction model based on CSVMD-ELM-error is proposed for TFD.