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TC-Linear: lightweight traffic speed forecasting via trend-seasonal decomposition and temporal convolution

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This model reduces MSE by 2.7–41.3% in traffic forecasting, suggesting strong robustness and efficiency in Intelligent Transportation Systems.

The TC-Linear model achieves significant accuracy improvements in traffic speed forecasting.
By integrating local convolution with global modeling, it greatly enhances computational efficiency.
Experimental results indicate a reduction in mean squared error (MSE) by up to 41.3% and mean absolute error (MAE) up to 12.4%.
The model's robustness to noise supports reliable smart city decision-making systems.

Qin et al. (2025) studied this question.

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This model reduces MSE by 2.7–41.3% in traffic forecasting, suggesting strong robustness and efficiency in Intelligent Transportation Systems.