## 4 Passenger Flow Prediction Results and Discussion
### Prediction Results and Analysis of OD Passenger Flow  
We conducted experiments using the OD passenger flow dataset of Chongqing Rail Transit. Firstly, the attention mechanism adaptively assigns different weights to different parts of the input data sequence. Then, the TCN-Attention-LSTM model is used to train and predict passenger flow data and generate prediction results. The goodness of fit and residual analysis of the model were evaluated through simulation experiments on three typical passenger flow intervals. Figure 11 shows the simulated loss value curve; the horizontal axis represents the number of training iterations, and the vertical axis is the training loss value. It can be seen that the two curves gradually become stable with the training of the model, which indicates that the training network runs normally.  
Figure 12 shows the predicted results of three typical passenger flow intervals. The horizontal coordinate is the time series index, indicating the sample number of OD passenger flow in a single day, and the ordinate is the OD passenger flow. It can be seen that the test values fit well with the true values, indicating that the deep learning model designed in this study can to some extent capture the changing characteristics of OD passenger flow and reflect the changing patterns of OD passenger flow.  
Figure 10: Structure diagram of TCN-LSTM model.  
Figure 11: Schematic diagram of combined model loss values.  
Figure 12: Short-term OD passenger flow prediction results.  
Further analysis was conducted on the prediction accuracy of the TCN-Attention-LSTM model under different levels of OD passenger flow, and the TCN-LSTM baseline model was also used to predict OD passenger flow. The introduction of the TCN-LSTM combination model can be found in our previous paper, which has been successfully applied to station passenger flow prediction [29]. Afterwards, the predicted value of the TCN-LSTM model based on the passenger flow output of the interval and the output predicted value of the TCN-Attention-LSTM model are compared with the corresponding real value in the three typical sections selected. The results are shown in Figure 13. From the prediction results in the figure, it can be seen that the data curve of the TCN-Attention-LSTM model for passenger flow prediction can fit the data curve of real passenger flow well and better predict the peak OD passenger flow in different periods. However, the TCN-LSTM model lacks an attention mechanism to extract key information, resulting in a large prediction error for the peak OD passenger flow.  
Figure 13: Comparison results of OD passenger flow prediction.  
The comparison of the prediction results of the two models shows that the TCN-Attention-LSTM model has excellent performance in predicting short-term OD passenger flow and captures the corresponding temporal and spatial characteristics. By introducing an attention mechanism, the LSTM model can adaptively adjust the attention to different time steps in the input sequence, better capture important information in the sequence, and reduce attention to irrelevant information. This can enhance the modeling ability of the model for time series and improve predictive performance.