## 4 Experiments and Results
### Results and Discussions
#### 4.2.1 Comparison of Prediction Performance  
In order to show the excellent short-term OD prediction performance of the proposed PIANet, comprehensive comparisons among the proposed PIANet and the three comparison methods were conducted. Table 3 shows the comparisons of RMSE, MAE, and SMAPE among the proposed PIANet and the three comparison methods (i.e., ConvLSTM, STResNet, and CASCNN) in BJSubway2021 and BJSubway2022. Note that all values of RMSE, MAE, and SMAPE are displayed in the form of mean \(\pm\) std (standard deviation) in Tables 3 and 4. As shown in Table 3, the proposed PIANet can obtain lower RMSE, lower MAE, and lower SMAPE than the comparison methods in BJSubway2021 and BJSubway2022, which indicates that the proposed PIANet performs better than the comparison methods in short-term OD prediction performance. Specifically, compared with the suboptimal results, the RMSE, MAE, and SMAPE obtained by the proposed PIANet are, respectively, reduced by 19.1%, 9.9%, and 4.3% in BJSubway2021, and the RMSE, MAE, and SMAPE obtained by the proposed PIANet are, respectively, reduced by 20.3%, 14.7%, and 6.6% in BJSubway2022. In order to present more detailed comparison results, the comparisons of RMSE, MAE, and SMAPE at each time interval during the morning peak hours (i.e., 7:30-9:10) among the proposed PIANet and the comparison methods (i.e., ConvLSTM, STResNet, and CASCNN) in BJSubway2021 and BJSubway2022 are shown in Figure 5 and Figure 6, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**BJSubway2021**} & \multicolumn{3}{c}{**BJSubway2022**} \\ \cline{2-7}  & **RMSE** & **MAE** & **SMAPE** & **RMSE** & **MAE** & **SMAPE** \\ \hline ConvLSTM & 0.648 \(\pm\) 0.343 & 0.161 \(\pm\) 0.100 & 0.185 \(\pm\) 0.078 & 0.632 \(\pm\) 0.353 & 0.143 \(\pm\) 0.089 & 0.166 \(\pm\) 0.070 \\ STResNet & 0.722 \(\pm\) 0.476 & 0.172 \(\pm\) 0.119 & 0.189 \(\pm\) 0.078 & 0.633 \(\pm\) 0.348 & 0.149 \(\pm\) 0.100 & 0.176 \(\pm\) 0.078 \\ CASCNN & 0.740 \(\pm\) 0.473 & 0.178 \(\pm\) 0.116 & 0.195 \(\pm\) 0.077 & 0.617 \(\pm\) 0.341 & 0.146 \(\pm\) 0.090 & 0.175 \(\pm\) 0.071 \\ PIANet & **0.524 \(\pm\) 0.191\({}^{*}\)** & **0.145 \(\pm\) 0.084** & **0.177 \(\pm\) 0.074** & **0.022 \(\pm\) 0.155** & **0.122 \(\pm\) 0.067** & **0.155 \(\pm\) 0.062** \\ \hline \hline \end{tabular}
* Bold for the evaluation metric represents the best result.  
\end{table} 
Table 3: Comparisons of RMSE, MAE, and SMAPE among the proposed PIANet and the three comparison methods in BJSubway2021 and BJSubway2022. As shown in Figure 5a, the RMSE obtained by ConvLSTM is lower than those obtained by STResNet and CASCNN at each time interval in BJSubway2021. According to Figure 6b,c, the MAE and SMAPE obtained by ConvLSTM are, respectively, lower than those obtained by STResNet and CASCNN at each time interval in BJSubway2022. However, according to Figure 5b,c, the MAE and SMAPE obtained by ConvLSTM do not maintain the performance advantage compared with STResNet and CASCNN at each time interval in BJSubway2021. Specifically, in Figure 5b, the MAE obtained by ConvLSTM is lower than those obtained by STResNet and CASCNN before 8:30, and then higher than those obtained by STResNet and CASCNN after 8:30. In Figure 5c, the SMAPE obtained by ConvLSTM is lower than those obtained by STResNet and CASCNN before 8:15, and then higher than those obtained by STResNet and CASCNN after 8:15. Similarly, as shown in Figure 6a, the RMSE obtained by ConvLSTM does not maintain the performance advantage compared with STResNet and CASCNN at each time interval in BJSubway2022. The above results show that none among ConvLSTM, STResNet, and CASCNN can always maintain the advantages in short-term OD prediction performance under the three evaluation metrics (i.e., RMSE, MAE, and SMAPE).