## 4 Experiments Datasets
### Experiment Result Analysis  
We compare the performance of the HSTAN model with benchmark models listed in Table 1 and Figure 3 for time intervals of 15 min (3 steps), 30 min (6 steps), 45 min (9 steps), and 60 min (12 steps) using the SZ-Taxi and Los-loop datasets. Our findings indicate that (1) our model consistently achieves the best or second-best prediction performance compared to the benchmarks on both datasets from 3 steps to 12 steps, with the advantages being more pronounced in short-term horizon predictions. (2) Deep learning models that incorporate spatial dependency as graph structures consis \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**15 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**45 min**} & \multicolumn{3}{c}{**60 min**} \\ \cline{3-14}  & & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{5}{*}{**G-Taxi**} & ARIMA & 4.98 & 7.24 & -- & 4.67 & 6.79 & -- & 4.67 & 6.78 & -- & 4.75 & 6.77 & -- \\  & TGCN & 3.57 & 4.86 & 34.65 & 3.62 & 4.92 & 35.16 & 3.65 & 4.96 & 35.46 & 3.69 & 5.01 & 35.90 \\  & STGCN & 3.16 & 4.46 & 28.70 & 3.21 & 4.53 & 28.99 & 3.24 & 4.55 & 29.31 & 3.27 & 4.60 & 29.35 \\  & AGCRN & 3.15 & 4.44 & 28.74 & 3.20 & 4.51 & 29.09 & 3.24 & 4.55 & 29.31 & 3.27 & 4.59 & 29.17 \\  & ASTGCN & 2.83 & 4.24 & 26.82 & 2.89 & 4.27 & 26.98 & 2.88 & **4.28** & 27.14 & 2.93 & **4.26** & 27.31 \\  & HSTAN (Ours) & **2.68** & **4.17** & **25.37** & **2.71** & **4.21** & **25.45** & **2.79** & 4.29 & **24.38** & **2.84** & 4.38 & **25.89** \\ \hline \multirow{5}{*}{**G-Taxi**} & ARIMA & 7.68 & 10.04 & -- & 7.69 & 9.34 & -- & 7.69 & 10.05 & -- & 7.70 & 9.87 & -- \\  & TGCN & 3.13 & 5.09 & 8.67 & 3.66 & 5.99 & 10.21 & 4.17 & 6.68 & 11.39 & 4.23 & 7.09 & 12.10 \\  & STGCN & 2.73 & 5.06 & 7.18 & 3.13 & 5.96 & 8.85 & 3.32 & 6.46 & 9.38 & **3.49** & 6.87 & **9.94** \\  & AGCRN & 2.75 & 5.17 & 7.30 & 3.12 & 6.01 & 8.66 & 3.33 & 6.49 & 9.37 & **3.49** & **6.82** & 9.95 \\  & A3 TGCN & 3.12 & 5.55 & 8.55 & 3.65 & 6.57 & 10.46 & 4.06 & 7.30 & 12.13 & 4.46 & 7.95 & 13.78 \\  & HSTAN (Ours) & **2.65** & **4.96** & **6.86** & **3.03** & **5.86** & **8.25** & **3.28** & **6.40** & **9.18** & **3.49** & 6.83 & **9.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Traffic prediction on the SZ-taxi and Los-loop datasets. Figure 3: Comparison of our model and baselines using the SZ-taxi and Los-loop datasets, which are listed as (**a**) MAE, RMSE, and MAPE for the SZ-taxi set; (**b**) MAE, RMSE, and MAPE for the Los-loop dataset. tently outperform conventional statistical method ARIMA. This demonstrates the power of deep learning models and highlights the importance of incorporating graph structures into temporal prediction. (3) Both A3 TGCN and our model outperform conventional graph deep learning models, namely T-GCN, STGCN, and AGCRN, using the SZ-Taxi dataset. This demonstrates the significance of the global feature-capturing capability offered by the self-attention mechanism. The relatively low prediction error can possibly be attributed to the superior global spatial-temporal feature-capturing capability of the self-attention mechanism in traffic prediction compared to the local convolutional operation of the GCN-based methods. This is true regardless of the predefined graph structure or the use of adaptive graph structure by the GCN-based methods. (4) The superiority of our model over the A3 TGCN model indicates the necessity of fusing local and global spatial-temporal correlations via the hierarchical structure to enhance model performance. On the SZ-taxi dataset, our model exhibits reductions of approximately 5.3% in MAE, 1.65% in RMSE, and 5.4% in MAPE compared to the A3 TGCN model in the 15 min time series; approximately 6.22% in MAE, 1.4% in RMSE, and 5.67% in MAPE in the 30 min time series; approximately 3.12% in MAE, 0.2% in RMSE, and 10.16% in MAPE in the 45 min time series.