# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 7 Conclusions  
Aiming at the problem of short-term passenger flow prediction in URT, this paper proposes a TCN-LSTM prediction model considering multiple factors influencing passenger flow. Based on the passenger flow data of some stations of Chongqing Rail Transit Line 3, external factors such as date attributes, weather conditions, and air quality are coupled into the prediction model. The short-term passenger flow predictions of the combined model and the single model are carried out respectively. The results show that:  
(1) After analyzing the temporal and spatial distribution characteristics of passenger flow, the correlation analysis of passenger flow influencing factors can provide a good data set for the follow-up short-term passenger flow prediction model of urban rail transit to accurately predict the short-term passenger flow.  
(2) The Long Short-Term Memory (LSTM) network model can fully mine and effectively use the relationship between historical passenger flow data and can better predict the passenger flow trend of different passenger flow types of stations.  
(3) The TCN-LSTM combined model can make up for the deficiency of the LSTM single model in the actual passenger flow forecast, and its RMSE and MAPE values are significantly reduced. Through the comparison of the two forecast results, it was found that the TCN-LSTM combined model performs better than the LSTM single model in the peak, flat peak, and all-day stages of passenger flow forecast, and can more accurately predict changes in short-term passenger flow.  
(4) The short-term passenger flow forecast of urban rail transit carried out in this paper can provide some technical support for rail transit line planning and vehicle operation scheduling.  
This combined neural network model based on deep learning still has room for optimization in parameter setting and model training in the short-term passenger flow forecast of urban rail transit. In the future, the model can be optimized to achieve a better forecasting effect and can be extended to the short-term passenger flow forecast of different cities and scenarios.  
Conceptualization, Z.Y.; Formal analysis, G.Y.; Supervision, Z.D.; Writing--original draft, Z.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** 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); the Natural Science Foundation of Chongqing, China, (Grant No. cstc2021 jcyimsxmX0534); Chongqing Smart City and Sustainable Development Academy Application Form for Tech-Inno Fund Project (Grant No. 20210601); and the Opening Fund of Urban Rail Transit Vehicle System Integration and Control Laboratory in Chongqing (Grant No. CKLURTSIC-KFKT-201810). The authors are deeply grateful for this support. The comments of the anonymous reviewers have improved the quality of this paper and are also gratefully acknowledged.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the present article.  
**Conflicts of Interest:** The authors declare no conflict of interest.