## 4 Experiments and Results
### Results and Discussions
#### 4.2.2 Ablation Studies  
The OAMs and feature interaction are proposed to further improve the short-term OD prediction performance. To verify the effectiveness of the proposed OAMs and feature interaction, four extra experiments were conducted. In order to verify the effectiveness of the proposed OAMs, three experiments were conducted to form three variants of the proposed PIANet: (1) PIANet-A--all DCABs in Figure 4 are changed to DCBs; (2) PIANet-B--only DCABs in stage 2 of Figure 4 are changed to DCBs; (3) PIANet-C--only DCABs in stage 1 of Figure 4 are changed to DCBs. Moreover, in order to verify the effectiveness of feature interaction, the feature interaction in stage 2 of Figure 4 is removed to form a variant of the proposed PIANet, named PIANet-D. The comparisons of RMSE, MAE, and SMAPE among the proposed PIANet and its variants (i.e., PIANet-A, PIANet-B, PIANet-C, and PIANet-D) in BJSubway2021 and BJSubway2022 are presented in Table 4. Furthermore, the comparisons of convergence curves of best validation RMSE among the proposed PIANet and its variants in BJSubway2021 and BJSubway2022 are shown in Figure 8.  
Figure 7: Comparisons of convergence curves of best validation RMSE among the proposed PIANet and the comparison methods in BJSubway2021 and BJSubway2022. According to Table 4, the proposed PIANet shows better short-term OD prediction performance with lower RMSE, lower MAE, and lower SMAPE than PIANet-B and PIANet-C, and then PIANet-B and PIANet-C perform better than PIANet-A in general, except that the SMAPE of PIANet-C is higher than that of PIANet-A in BJSubway2021. The above comparison results show that both the OAMs in stage 1 and the OAMs in stage 2 play an important role in improving the short-term OD prediction performance. And the more OAMs, the better the short-term OD prediction performance. Furthermore, the proposed PIANet outperforms PIANet-D, which indicates that feature interaction plays an important role in improving the short-term OD prediction performance. According to Figure 8, the proposed PIANet can converge to a lower best validation RMSE than PIANet-B and PIANet-C in BJSubway2021 and BJSubway2022, and PIANet-B and PIANet-C can converge to a lower best validation RMSE than PIANet-A in BJSubway2021 and BJSubway2022. Moreover, the proposed PIANet can converge to a lower best validation RMSE than PIANet-D in BJSubway2021 and BJSubway2022. The comparison results of convergence curves in Figure 8 further verify the analytical results of Table 4.  
In a word, it can be seen from Table 4 and Figure 8 that the proposed OAMs and feature interaction are effective in improving the short-term OD prediction performance. studies, the proposed OAMs and feature interaction play an important role in improving the short-term OD prediction performance. The embedding of the proposed OAMs and the feature interaction makes the proposed PIANet have excellent feature extraction ability. Therefore, the proposed PIANet is not only suitable for the URT system of Beijing, but also can be extended to the URT system in other cities. Specifically, when the PIANet is extended to another URT system with different characteristics and scale, only the sizes of input and output need to be modified according to the scale of the new URT system, and then the corresponding input data and output data can be used for training. Due to its excellent feature extraction ability, the PIANet can capture the new passenger flow distribution during training. Therefore, after training, the PIANet can adapt to the new URT system and achieve accurate short-term OD prediction. Conceptualization, W.Z., C.G. and T.T. ; methodology, W.Z. ; software, W.Z. ; validation, W.Z. ; formal analysis, W.Z. ; investigation, W.Z. ; resources, W.Z. and C.G. ; data curation, C.G. ; writing--original draft preparation, W.Z. ; writing--review and editing, W.Z. ; visualization, W.Z. ; supervision, C.G. and T.T. ; project administration, C.G. and T.T. All authors have read and agreed to the published version of the manuscript.