# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 6 Discussion  
From the above example prediction results, the prediction accuracy of TCN-LSTM combined deep learning model constructed in this paper is much higher than that of LSTM single model. As can be seen from Table 4, the RMSE values of the prediction results of the TCN-LSTM combined deep learning model constructed in this paper has decreased by 66%, 20%, and 17% respectively in high (Guanyinqiao), medium (Jiazhoulu), and low (Longtousi) passenger flow stations compared with the LSTM single model. MAPE values decreased by 69%, 26%, and 18% respectively in high (Guanyinqiao), medium (Jiazhoulu), and low (Longtousi) passenger flow stations. On the one hand, it shows that the prediction model proposed in this paper has good simulation accuracy. On the other hand, it was found that the decrease of the predicted indicator values will increase with the increase of passenger flow.  
When combined with the passenger flow prediction curves between different stations, it was found that in medium and high passenger flow stations, the TCN-LSTM model has higher coupling and lower oscillation in the prediction of the overall trend, and the overall prediction accuracy is more accurate. In low passenger flow stations, the prediction trend of TCN-LSTM model fluctuates greatly, and the variation range of accuracy shows an unstable trendy, which is consistent with the results fed back by RMSE values and MAPE values. This may be related to the sample point data of high passenger flow stations. The sample data base of high passenger flow stations is large, which is also greatly affected by external factors, and the passenger flow presents a double peak distribution. The TCN-LSTM combined forecasting model proposed in this paper not only couples multi-source external factors, but also has a better ability to capture the peak passenger flow than a single LSTM model, which leads to a large difference in the forecasting performance in high passenger  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Stations** & **Prediction Model** & **RMSE** & **MAPE/\%** \\ \hline \multirow{2}{*}{Guanyinqiao} & TCN-LSTM model & 24.70 & 8.56 \\  & Single LSTM model & 72.58 & 27.50 \\  & TCN-LSTM model & 36.37 & 11.34 \\  & Single LSTM model & 45.64 & 15.32 \\  & TCN-LSTM model & 48.45 & 16.58 \\  & Single LSTM model & 58.24 & 20.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison evaluation results of model prediction.  
flow stations. However, the sample data base of low passenger flow stations is small, which is less affected by external factors, and the passenger flow shows a single peak distribution, which shows little difference in performance between the two models.  
In addition, the prediction results of the LSTM model show that the fitting degree of the LSTM single model is not good. The analysis reason is that when the passenger flow and passenger flow characteristic dimensions are increased, the structure of the LSTM model limits the calculation efficiency and accuracy. It also shows that with the increase of the complexity of the actual situation, the prediction model with a simple mathematical model or machine learning algorithm as the main support is difficult to meet practical needs.  
According to the above discussion, the TCN-LSTM combined prediction model proposed in this paper has the following advantages. (1) Compared with a single LSTM model that can only capture the temporal characteristics of passenger flow data, a TCN-LSTM model integrates temporal and spatial prediction, the accuracy and stability of passenger flow prediction have been greatly improved. (2) The TCN-LSTM model is closer to the real prediction situation because it couples external factors such as date attributes, weather conditions, and air quality. (3) The TCN-LSTM model has a stronger ability to capture the peak passenger flow, and the peak passenger flow of rail transit has more reference value for the reasonable planning of passenger travel time and the operation and management of line train numbers.