## 4 Passenger Flow Prediction Results and Discussion
### Model Comparison and Evaluation  
In order to better compare the predictive performance of the TCN-Attention-LSTM model constructed in this study, according to different intervals, the models are quantified by two common evaluation indicators, root mean square error (RMSE) and mean absolute error (MAE). The calculation formula is as follows:  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{(x_{i}-p_{i})^{2}}}} \tag{15}\]  
\[MAE=\frac{1}{N}{\sum\limits_{i=1}^{N}{|x_{i}-p_{i}|}} \tag{16}\]  
where \(N\) is the total sample of the OD passenger flow prediction, \(x_{i}\) is the actual OD passenger flow, and \(p_{i}\) is the predicted OD passenger flow. The mean square error is used to explain the statistical dispersion of the prediction results of passenger flow. The mean absolute error can better reflect the actual situation of the error of the prediction results of interval passenger flow. For a comparison of the accuracy of the TCN-Attention-LSTM model with the TCN-LSTM model and a comparison of the RMSE of the prediction results of interval passenger flow of the TCN-Attention-LSTM model with the MSE and MAE of the OD passenger flow prediction results of the TCN-LSTM model, the results are shown in Table 7.  
From the prediction results of the above example, compared with the TCN-LSTM model, the prediction accuracy of the TCN-Attention-LSTM model is greatly improved. From Table 6, it can be seen that compared with the TCN-LSTM model, the RMSE index of the prediction results of TCN-Attention-LSTM model decreases by 45.0%, 29.1%, and 8.7% in the high (from Jiazhoulu to Guanyinqiao), middle (Guanyinqiao to Huaxinije), and low (Sigongli to Nanjing) OD passenger flows, respectively. The MAE index decreases by 55.2%, 31.4%, and 7.0%, respectively, in the high (from Jiazhoulu to Guanyinqiao), middle (Guanyinqiao to Huaxinijie), and low (Sigongli to Nanjing) OD passenger flows. The prediction results indicate that the TCN-Attention-LSTM deep learning model proposed in this study has high prediction accuracy. Even in the case of low OD passenger flow, the results are still at a high level and better than the baseline model, confirming that the OD \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Passenger Flow Intervals** & **Model** & **RMSE** & **MAE** \\ \hline From Jiazhoulu to Guanyinqiao & TCN–Attention–LSTM & 3.249 & 3.106 \\  & TCN–LSTM & 5.908 & 6.934 \\ \hline From Guanyinqiao to Huaxinijie & TCN–Attention–LSTM & 0.348 & 0.203 \\  & TCN–LSTM & 0.491 & 0.296 \\ \hline From Sigongli to Nanjing & TCN–Attention–LSTM & 0.348 & 0.212 \\  & TCN–LSTM & 0.381 & 0.228 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RMSE and MAE of TCN–Attention–LSTM model and TCN-LSTM model. passenger flow prediction method considering the external environmental impact of urban rail transit in this paper has high adaptability. To further validate the efficacy of our model, we conducted a comparative analysis against the integrated models presented in references [30, 31, 32], which served as our baseline. While these methodologies demonstrate commendable performance in forecasting passenger flow at individual stops using singular models, combined models, and attention mechanisms, their effectiveness diminishes when applied to the prediction of short-term regional origin-destination (OD) passenger flows. Specifically, their predictive accuracy falls short of expectations, with notably higher errors compared to our proposed model. This discrepancy underscores the necessity for a more nuanced approach to OD flow prediction, as highlighted by our analysis and comparison with existing works. By combining different OD passenger flow prediction curves, it can be found that the TCN-Attention-LSTM model has high coupling in predicting the overall trend of OD passenger flow. At the same time, the overall prediction accuracy is higher in the medium-to-high OD passenger flow range, while the prediction trend fluctuates greatly in the low-passenger-flow range, and the accuracy is unstable.