## 4 Experiments Datasets
### Experiment Result Analysis
; and approximately 3.07% in MAE, 2.81% in RMSE, and 5.2% in MAPE in the 60 min time series. (5) Our model outperforms other GCN models, specifically T-GCN, STGCN, and AGCRN, with the advantages being more pronounced in short-term horizon predictions. In the 15 min time series, our model demonstrates reductions of approximately 15.33% in MAE, 2.55% in RMSE, and 20.87% in MAPE compared to the T-GCN model; approximately 2.93% in MAE, 1.97% in RMSE, and 4.45% in MAPE compared to the STGCN model; and approximately 3.63% in MAE, 4.06% in RMSE, and 6.02% in MAPE compared to the AGCRN model using the Los-loop dataset. In the 60 min time series, our model exhibits reductions of approximately 17.49% in MAE, 3.66% in RMSE, and 17.85% in MAPE compared to the T-GCN model. Additionally, our model performs at a similar level to the STGCN and AGCRN models in terms of MAE, RMSE, and MAPE metrics. The relatively high performance in short-term prediction can possibly be attributed to the increased importance of local spatial attention due to the relatively small changes in the temporal dimension. To examine the influence of each component in our model, we proceed to assess the performance of the model variants by eliminating the temporal attention layer (HSTAN w/o TA) and the spatial attention layer (HSTAN w/o SA). Table 2 clearly demonstrates that HSTAN consistently outperforms its variants, underscoring the significance of the spatial attention layer and the temporal attention layer in capturing intricate spatial-temporal dependencies. Hence, the prediction error of HSTAN w/o SA is lower than that of HSTAN w/o TA, thereby establishing the primacy of the temporal attention layer over the spatial attention layer. Figure 4 clearly demonstrates that HSTAN consistently outperforms its variants. The temporal attention layer is crucial for both datasets, providing a 43% and 33.1% reduction in MAE and RMSE for the SZ-taxi data, and a 57.2% and 44.6% reduction in MAE and RMSE for the Los-loop data. This indicates that temporal correlations play an important role in the traffic data imputation problem for urban road networks. With the temporal \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**MAE**} & \multicolumn{5}{c}{**RMSE**} \\ \cline{3-11}  & & **15 min** & **30 min** & **45 min** & **60 min** & **Avg.** & **15 min** & **30 min** & **45 min** & **60 min** & **Avg.** \\ \hline \multirow{4}{*}{**GCN-based**} & HSTAN w/o TA & 4.80 & 4.82 & 4.84 & 4.88 & 4.83 & 6.24 & 6.29 & 6.60 & 6.36 & 6.37 \\  & HSTAN w/o SA & 3.57 & 3.65 & 3.83 & 3.95 & 3.75 & 4.77 & 4.89 & 5.17 & 5.60 & 5.10 \\  & HSTAN & 2.68 & 2.71 & 2.79 & 2.84 & 2.75 & 4.17 & 4.21 & 4.29 & 4.38 & 4.26 \\ \hline \multirow{4}{*}{**GCN-based**} & HSTAN w/o TA & 7.64 & 7.72 & 7.78 & 7.93 & 7.76 & 10.71 & 10.86 & 10.88 & 11.01 & 10.86 \\  & HSTAN w/o SA & 3.03 & 3.87 & 5.36 & 5.12 & 4.34 & 5.47 & 6.93 & 8.67 & 8.81 & 7.47 \\ \cline{1-1}  & HSTAN & 2.65 & 3.03 & 3.28 & 3.49 & 3.11 & 4.96 & 5.86 & 6.40 & 6.83 & 6.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of model variants using the SZ-taxi and Los-loop datasets. attention layer ablated, the MAE and RMSE values of the SZ-taxi data increase to 26.6% and 16.5%, whereas the MAE and RMSE values of the Los-loop data increase to 28.3% and 19.5%. We can find that temporal dependencies contribute more to the traffic data imputation problem than spatial correlations.