## 5 Conclusion and Future Directions  
This study introduces a model architecture integrating Variational Mode Decomposition, the CT-Transformer, and TCN-LSTM for analyzing non-stationary traffic flow data with time-series characteristics. Variational Mode Decomposition is initially applied to segment the data into multiple subsequences. Subsequently, each subsequence's autocorrelation and partial autocorrelation coefficients are analyzed to assess their seasonal characteristics. We propose replacing the attention mechanism in the Encoder module of the two-stage Transformer's first stage with CPSNet-Attention to reduce computational costs and memory usage, thereby enhancing model prediction accuracy. We designate the refined model as the CT-Transformer. Furthermore, given the TCN-LSTM model's robust long-term dependency modeling capabilities, subsequences lacking seasonal characteristics after modal decomposition are inputted into it. In contrast, those with seasonal characteristics are directed to the CT-Transformer. Ultimately, the output predictions from both models undergo variational mode reconstruction, culminating in the final predictive outcomes. Subsequent validations of the dataset demonstrate the fusion model's substantial predictive capabilities and robustness. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Metrics**} & \multicolumn{5}{c}{**PeMS Traffic Dataset**} \\ \cline{3-6}  & & **96** & **192** & **336** & **720** \\ \hline \multirow{3}{*}{Ours} & MAE & 0.231 & 0.236 & 0.234 & 0.258 \\ \cline{2-6}  & MSE & 0.137 & 0.142 & 0.138 & 0.156 \\ \hline \multirow{3}{*}{no-V} & MAE & 0.251 & 0.257 & 0.259 & 0.298 \\ \cline{2-6}  & MSE & 0.161 & 0.160 & 0.162 & 0.202 \\ \hline \multirow{3}{*}{no-C} & MAE & 0.265 & 0.271 & 0.324 & 0.317 \\ \cline{2-6}  & MSE & 0.163 & 0.178 & 0.221 & 0.238 \\ \hline \multirow{3}{*}{no-T} & MAE & 0.276 & 0.280 & 0.325 & 0.400 \\ \cline{2-6}  & MSE & 0.179 & 0.191 & 0.220 & 0.319 \\ \hline \multirow{3}{*}{No-VT} & MAE & 0.270 & 0.271 & 0.348 & 0.449 \\ \cline{2-6}  & MSE & 0.171 & 0.182 & 0.249 & 0.399 \\ \hline \multirow{3}{*}{no-VCT} & MAE & 0.310 & 0.315 & 0.426 & 0.509 \\ \cline{2-6}  & MSE & 0.321 & 0.229 & 0.365 & 0.505 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparative table of ablation study results. Additionally, we recognize the importance of explainable AI (XAI) in achieving the interpretability of the proposed technique. Following recent trends in other vertical domains, incorporating XAI can provide transparent and interpretable insights into the model's decision-making process. This approach not only enhances the trustworthiness of the model but also facilitates its adoption in practical applications. Future work will explore the integration of XAI methods to further improve the interpretability and explainability of our traffic flow prediction model. Future traffic flow prediction endeavors will increasingly incorporate a broader array of external factors, extending beyond weather and temperature to include holidays, the surge in new energy vehicles, and the implementation of new road traffic regulations. Recent studies reveal that conditions at the current location dictate alterations in traffic flow, and flows at adjacent locations also significantly influence these changes. This interplay among adjacent locations suggests a spatial correlation or dependence within traffic flows. Consequently, future research in traffic flow prediction may evolve by examining inter-location traffic flows within a defined radius. Researchers anticipate that this avenue of research will enhance the accuracy and promptness of traffic flow predictions, thereby providing more dependable support for transportation planning and management decisions. A comprehensive analysis of spatial correlation's impact on traffic flow will elucidate the intricacies of urban transportation systems and furnish effective strategies for alleviating traffic congestion and optimizing transportation networks.