## 1 Introduction
This influence stems from the models' learning and inferential capabilities relying on the statistical regularities within the input data. In making forecasts, researchers often overlook the inherent patterns of changes in traffic flow data that demonstrate the characteristics of a time-series distribution; however, these laws significantly impact the accuracy of traffic flow predictions. Consequently, numerous academics have integrated various data decomposition methods into traffic flow prediction to address this issue. The data decomposition method, a signal processing technique, decomposes complex signals into simpler ones, thereby facilitating the analysis and comprehension of the signal's characteristics and structure. Data decomposition methods have extensive applications in time-series forecasting. They enable forecasting models to delve deeply into the intrinsic patterns governing traffic flow data changes, thereby augmenting their capability to extract data characteristics. Researchers have employed data decomposition methods to preprocess traffic flow data, inputting the decomposed signals into various prediction models and synthesizing each model's outputs. This process significantly enhances the models' capability to extract data features, improving the prediction accuracy and robustness. Commonly employed data decomposition methods, including the Fourier Transform (FT), Wavelet Transform (WT) [11], and Empirical Mode Decomposition (EMD) [12], decompose signals within the frequency, time-frequency, and time domains, respectively, unveiling the signal's structure and characteristics from diverse perspectives. Applying these decomposition methods to traffic flow prediction yielded superior outcomes compared with a singular predictive model. However, the Fourier Transform struggles with non-stationary data and fails to analyze signals' local characteristics adequately. At the same time, the Wavelet Transform is constrained by [PERSON]'s uncertainty principle, leading to pseudo-spectra and spectral aliasing in signals with mutations. Empirical Mode Decomposition, conversely, is subject to mode aliasing and endpoint effects. Section 2 delves into a comprehensive review of this paper's research findings on traffic flow prediction. Section 3 outlines the fusion model's overall architecture, including Variational Mode Decomposition [13], CSPNet-Attention [14], the Two-stage Transformer, and the Temporal Convolutional Neural Network-Long Short-Term Memory Network(TCN-LSTM) [15]. Section 4 is experimental data processing and experimental validation. Section 5 summarizes the research conducted in this paper and offers insights into the future development of traffic flow prediction. This study employs Variational Mode Decomposition (VMD) as an adaptive and fully non-recursive method for modal change and signal processing to mitigate the complexity and nonlinearity of time series. Secondly, this study adopts a new attention mechanism, CSPNet-Attention, to replace the traditional attention mechanism, which reduces the computational complexity and memory consumption by sparsifying the attention matrix and achieves similar or even more than the prediction accuracy. Then, we take into full consideration the seasonal characteristics of the experimental data and adopt a model that integrates the Two-stage Transformer and TCN-LSTM model to conduct the prediction experiment. The overall fusion model is named CT+. The experimental results are reconstructed together using VMD to get the final experimental results.