## 4 Experiments and Results
### Predicted Results  
To verify the advantages of the BI-GAT-TF model proposed in this paper, a comparative experiment was conducted with the CNN-LSTM-TF prediction model. Additionally, toassess the effectiveness of the proposed frequency-domain spatial feature extraction in improving the model's prediction accuracy, an ablation study was performed by removing the frequency-domain feature extraction module from the BI-GAT-TF model and observing the results. For a more intuitive understanding of the impact of frequency-domain feature extraction on the experimental results, a comparison was also made between the traditional CNN-LSTM model and the CNN-LSTM-TF model.  
The experimental results comparing the proposed prediction model with the baseline models are shown in Figures 5 and 6. From the results, the proposed model achieves the lowest MAE, MAPE (%), and RMSE on both the PEMS04 and PEMS08 datasets. Specifically, in the PEMS04 dataset, the values are 26.05, 12.36, and 37.08, while in the PEMS08 dataset, the values are 19.12, 7.25, and 19.12, respectively. The NSE is also the highest in both datasets, with values of 0.93 and 0.97, respectively. These results indicate that the proposed model achieves optimal performance in terms of prediction error and accuracy. Notably, the experimental results on the PEMS08 dataset outperform those on the PEMS04 dataset. This can be attributed to the fact that the PEMS08 dataset contains a larger number of traffic flow sequences, which provides a longer total sequence length. This increase in sequence length offers a clear advantage in terms of model training and feature extraction. In conclusion, the proposed prediction model demonstrates excellent detection performance on real-world test datasets, achieving more accurate predictions.  
Figures 7 and 8 show the experimental results for node 10 in the PEMS04 and PEMS08 datasets, respectively. (a) shows the 14-day traffic flow prediction results for each model; (b) shows the traffic flow prediction results for each model on the first day; (c) shows the traffic flow prediction results for CNN-LSTM and CNN-LSTM-TF on the first day; (d) shows the traffic flow prediction results for BI-GAT and BI-GAT-TF on the first day; (e) shows the traffic flow prediction results for CNN-LSTM-TF and BI-GAT-TF on the first day.  
Figure 5: Performance metrics of each model in the PEMS04 dataset.  
Figure 6: Performance metrics of each model in the PEMS08 dataset.  
As shown in Figures 6(c),d and 6(c),d, the results indicate that the prediction models with the frequency domain feature extraction module are closer to the true data. The results in Figures 6(e) and 6(e) more clearly demonstrate that the BI-GAT-TF model performs better in terms of fitting compared to CNN-LSTM-TF. This suggests that the proposed model, both in terms of the selected time domain feature extraction model and the idea of frequency domain feature extraction, significantly improves the prediction accuracy of the model.  
It is worth noting that, both in the PEMS04 and PEMS08 datasets, there are data points with large fluctuations, such as around noon in PEMS04 (around 12:00) and around 1 AM in PEMS08. At these times, the fitting performance of CNN-LSTM-TF is better than the other three models. However, while analyzing these points, it is important to consider not only the model's fitting at these points but also whether the data collected at these time points may be anomalous.  
Figure 7: Experimental results in the PEMS04 dataset.