## 5 Performance Analysis
### Comparative Analysis of Results  
Table 2 displays the comparative model and ADDGCN prediction results for two real traffic flow test datasets [16] for the next hour, consisting of 12 consecutive time steps. With the exception of the MAPE measure in PEMS04, which performs just slightly less well than SCINet, the proposed ADDGCN outperforms all baseline methods on both datasets. Specifically, on the PEMS04 dataset, ADDGCN demonstrates performance improvements of 2.46% and 2.90% in MAE and RMSE, in comparison to the best baseline model, accordingly. Similarly, for the PEMS08 dataset, ADDGCN achieves performance gains of 3.46%, 1.50%, and 0.21% in MAE, RMSE, and another metric, in comparison to the best baseline model, accordingly. The results presented in Table 2 highlight the challenges faced by statistically based methods (HA, VAR) in handling non-linear and non-stationary time sequence data, leading to higher prediction errors. While deep learning methods, like TCN and LSTM, are superior to statistical models, they primarily concentrate on temporal correlations, ignoring the complex spatial relationships seen in traffic data. As a result, their modeling capabilities for spatio-temporal data are limited. On the other hand, models like STGCN and DCRNN incorporate GCN to extract spatial correlations, leading to better performance compared to time-based methods. By building adaptive or dynamic graph structures based on geographical correlations in historical data, AGCRN overcomes the drawbacks of fixed graph structures and achieves even higher prediction performance than earlier approaches. Graph WaveNet, which combines diffusion GCN with TCN, demonstrates exceptional ability in capturing temporal correlations, surpassing the performance of recently proposed models (SCINet, STG-NCDE). Notably, SCINet performs well even when spatial correlations are ignored, demonstrating the value of interactive learning. While STG-NCDE, a novel deep learning model, achieves promising results, it falls short compared to our proposed ADDGCN due to its sequential capture of spatio-temporal features. In contrast, our method employs a down-sampling interactive learning strategy to simultaneously capture spatio-temporal relationships. To imitate the dynamic links between nodes over time and study latent spatial relationships, the DGCN module looks into hidden node interactions in the road system. Moreover, our approach incorporates a fea \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Metrics** & \multicolumn{10}{c}{**Model**} \\ \hline  & & **HA** & **VAR** & **LSTM** & **TCN** & **DCRNN** & **STGCN** & **GWN** & **AGCRN** & **SCINet** & **STG-** & **APD** & **ADD** \\  & MAE & 38.03 & 24.54 & 26.77 & 23.22 & 21.22 & 21.16 & 19.36 & 19.83 & 19.30 & 19.21 & 19.09 & 18.62 \\ PEMS04 & RMSE & 59.24 & 38.61 & 40.68 & 37.26 & 33.44 & 34.89 & 31.72 & 32.26 & 31.28 & 31.09 & 31.01 & 30.11 \\  & MAPE & 27.88\% & 17.24\% & 18.23\% & 15.99\% & 14.17\% & 13.83\% & 13.31\% & 12.97\% & 12.05\% & 12.76\% & 12.62\% & 12.43\% \\ \hline  & MAE & 34.86 & 19.19 & 23.09 & 22.72 & 16.82 & 17.50 & 15.07 & 15.95 & 15.76 & 15.54 & 15.02 & 14.50 \\ PEMS08 & RMSE & 59.24 & 29.81 & 35.17 & 35.59 & 26.36 & 27.09 & 23.85 & 25.22 & 24.65 & 24.81 & 24.37 & 23.49 \\  & MAPE & 27.91\% & 13.10\% & 14.99\% & 14.03\% & 10.92\% & 11.29\% & 9.51\% & 10.09\% & 10.01\% & 9.92\% & 9.68\% & 9.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing the performance of several models. and attention mechanism to unveil multi-scale features and capture long-distance traffic fluctuation correlations. To provide a clear visualization of the performance variances of the approach we propose and the baseline approaches, we depict the variations in MAPE, RMSE, and MAE for several methods on the two traffic datasets in Figure 6. Because MAPE, RMSE, and MAE are all indicators used to measure the size of prediction errors, the better our model performs in terms of predictions, the lower the values of these three indicators [49].