@article{Zheng2025, 
author = {Yifei Zheng and Xiang Wang and Xintong Liu and Shaoweihua Liu and Zhiyuan Liu and Kai Huang},
title = {A variational mode decomposition assisted temporal-frequency pure convolutional neural network for highway traffic flow forecasting},
year = {2025},
journal = {Electronic Research Archive},
volume = {33},
number = {11},
pages = {7247-7276},
keywords = {multivariate time series forecasting, deep learning, signal decomposition, frequency learning, convolutional neural network},
url = {https://www.sciopen.com/article/10.3934/era.2025320},
doi = {10.3934/era.2025320},
abstract = {This paper addressed the problem of highway traffic flow multivariate time series forecasting with the challenges of variate heterogeneity. To improve forecast performance without a heavy computational burden in large-scale networks, we innovatively introduced variational mode decomposition and established a decomposition-assisted multi-tasking deep learning forecasting architecture. To improve variate-specific pattern learning and mitigate pattern mixing, we proposed a novel temporal-frequency pure convolutional neural network incorporating discrete Fourier transform, deepwise convolution, and batchwise feedforward neural network. To verify the proposed model, we conducted a case study on a regional network located in Jiangsu, China. Results demonstrate strong forecast performance and efficient computation. The proposed model offers suitability toward highway operators for large-scale engineering deployment and better facilitates their managerial actions.}
}