# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 4 Experimental Results and Discussion
### Comparisons  
To better analyze the forecasting performance of the deep LSTM_NN, four methods were selected for comparisons, namely RF [38], deep neural network (DNN) [18], RNN [20], and GRU [39]. We have discussed the optimized combination of the endogenous input variables using RF in our previous study [26], and the impacts of added weather variables on passenger flow forecasting have been further studied. Therefore, RF was further selected as one of the important comparative methods. Furthermore, the other three neural networks (DNN, RNN, and GRU) are the typical artificial intelligence algorithms that are successfully applied to many different forecasting tasks. The parameters of n_estimators and max_features in RF were chosen with the same values as in Section 4.2. To make an equal comparison among different neural network models, the parameters used in DNN, RNN, and GRU were the same as in the deep LSTM_NN, described in Section 4.3. The training samples and input variables used in the above four methods were identical to those used in Models 17-31. Figure 6 shows the RMSE and MAPE of the forecasting results in the five methods. The deep LSTM_NN is better than the other four methods when incorporating previous one-hour weather variables as the inputs in most scenarios. These results verify that the deep LSTM_NN can better extract and represent the deep variables of passenger flow embedded in the training dataset. It is a more powerful method for time series forecasting problems.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Input Variables**} & \multicolumn{2}{c}{**Deep LSTM\_NN**} \\ \cline{2-7}  & **Optimized** & & & & & & \\ \cline{2-7}  & **Traditional Input** & \(v_{Temp\_Pre\_1}\) & \(v_{Run\_Pre\_1}\) & \(v_{Hun\_Pre\_1}\) & \(v_{Wind\_Pre\_1}\) & **RMSE** & **MAPE** \\  & **Variables** & & & & & & \\ \hline
1 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & 753.88 & 0.0741 \\
17 & \(\bullet\) & \(\bigstar\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & 735.79 & 0.0711 \\
18 & \(\bullet\) & \(\bigtimes\) & \(\bigstar\) & \(\bigtimes\) & \(\bigtimes\) & 718.83 & 0.0752 \\
19 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigstar\) & \(\bigtimes\) & 809.57 & 0.0793 \\
20 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigstar\) & 718.83 & 0.0813 \\
21 & \(\bullet\) & \(\bullet\) & \(\bigstar\) & \(\bigtimes\) & \(\bigtimes\) & 782.14 & 0.0806 \\
22 & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bigstar\) & \(\bigtimes\) & 794.34 & 0.0902 \\
23 & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigstar\) & 722.79 & 0.0734 \\
24 & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bigstar\) & \(\bigtimes\) & 823.00 & 0.0780 \\
25 & \(\bullet\) & \(\bigtimes\) & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) & 713.60 & 0.0757 \\
26 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bullet\) & \(\bigstar\) & 773.20 & 0.0863 \\
27 & \(\bullet\) & \(\bigstar\) & \(\bullet\) & \(\bigstar\) & \(\bigstar\) & 831.89 & 0.0873 \\
28 & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bullet\) & \(\bigstar\) & 802.04 & 0.0852 \\
29 & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bigstar\) & 796.50 & 0.0873 \\
30 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bigstar\) & \(\bullet\) & 810.28 & 0.0798 \\
31 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & 794.13 & 0.0758 \\ \hline \hline \end{tabular}  
* The input variable has been used in the corresponding model; \(\bigtimes\): The input variable hasn’t been used in the corresponding model.  
\end{table}
Table 8: Results of Models 1 and 17–31.