## 5 Conclusions  
In order to achieve an accurate prediction of OD passenger flow in urban rail transit and improve the operational efficiency and service quality of urban rail transit, this study constructed a TCN-Attention-LSTM OD passenger flow prediction model. Firstly, the characteristics of OD passenger flow in urban rail transit were analyzed. Then, a deep learning model was used for passenger flow prediction, and external weather and date factors were coupled for prediction analysis. The results indicate the following:  
1. A spatiotemporal distribution analysis was conducted on the OD passenger flow of Chongqing Rail Transit Line 3, and it was found that there was a bimodal pattern of morning and evening OD flow, with the passenger flow on weekdays being greater than that on rest days. The analysis of factors affecting OD passenger flow shows that date attributes, time attributes, and air quality have a significant impact on OD passenger flow data. This can provide a good dataset for accurately predicting OD passenger flow in urban rail transit in the future.
2. The TCN-LSTM model, which combines the attention mechanism, has better prediction accuracy than the original TCN-LSTM model, and the prediction results are more in line with the actual changes in OD passenger flow.
3. The attention mechanism can compensate for the shortcomings of the TCN-LSTM model in actual OD passenger flow prediction. The RMSE and MAE values of its prediction results are significantly reduced. The TCN-LSTM model mixed with the attention mechanism performs better than the TCN-LSTM model in predicting OD passenger flow and can more accurately predict OD passenger flow in rail transit.  
* The OD passenger flow prediction work of urban rail transit carried out in this study can provide some technical support for rail transit line planning and vehicle scheduling in large cities. At the same time, the proposed prediction method is not only applicable to new line expansion scenarios based on existing infrastructure, but also applicable to the future development of intelligent rail transit systems, such as urban rail transit systems based on virtual coupling technology.  
Conceptualization, Z.H.; data curation, J.H.; methodology, Z.H.; software, Z.H.; validation, G.Y.; visualization, J.H.; writing--original draft, Z.H.; writing--review and editing, G.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 52072054); the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202100729); and the Natural Science Foundation of Chongqing, China, (Grant No. cstc2021)cyjmssmX0534). The authors are deeply grateful for this support.  
Not applicable.  
The datasets utilized and analyzed in the present study can be obtained from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical considerations, as they contain sensitive information that could compromise the confidentiality of the participants. Access to the data will be granted in compliance with institutional and legal requirements to ensure the protection of participant identities.  
The comments of the anonymous reviewers have improved the quality of this paper and are also gratefully acknowledged.  
The authors declare no conflicts of interest.