# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 4 Experimental Results and Discussion
### Results of not Using any Weather Variables  
To evaluate the forecasting performance of the deep LSTM_NN, we first compared the experimental results using the deep LSTM_NN, one-layer LSTM_NN, and RF, respectively. Without adding any weather variable, the optimized combination of traditional input variables (\(v_{Dir}\), \(v_{M}\), \(v_{D}\), \(v_{W}\), \(v_{H}\), \(v_{NH}\), \(v_{\overline{Pre_{PF}}}\), \(v_{PF_{Pre_1}}\), \(v_{PF_{Pre_2}}\), \(v_{PF_{Pre_3}}\), \(v_{\overline{Pre_{FFT_{pre_2}}}}\)) used in [26] were implemented in this section first.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Input Variables** & **Encoded Values** & **Descriptions** \\ \hline
1 & \(v_{Dir}\) & 0,1 & 0: inbound; 1: outbound \\
2 & \(v_{M}\) & 1–12 & January–December \\
3 & \(v_{D}\) & 1–31 & 31 days per month \\
4 & \(v_{W}\) & 1–7 & Monday–Sunday \\
5 & \(v_{H}\) & 0–23 & 24 hours per day \\
6 & \(v_{NH}\) & & 3 \\  & & & (1) Normal days: The average value of hourly \\
7 & \(v_{\overline{Pre_{\overline{Pre}}}}\) & calculated & passenger flow on all non-national holidays; \\  & & & (2) National holidays: The average value of hourly \\  & & & passenger flow on all national holidays. \\
8 & \(v_{PF_{Pre_1}}\) & actual & Previous 1-hour passenger flow \\
9 & \(v_{PF_{Pre_2}}\) & actual & Previous 2-hour passenger flow \\
10 & \(v_{PF_{Pre_3}}\) & actual & Previous 3-hour passenger flow \\
11 & \(v_{FFT_{Pre_2}}\) & calculated & (Previous 1-hour passenger flow)-(Previous 2-hour passenger flow) \\
12 & \(v_{Temp}\) & actual & Actual hourly atmospheric temperature (\({}^{\circ}\)C) \\
13 & \(v_{Rain}\) & actual & Actual hourly rainfall (mm) \\
14 & \(v_{Hlim}\) & actual & Actual hourly relative humidity (\%) \\
15 & \(v_{Wind}\) & actual & Actual hourly average wind speed (mile/second) \\
16 & \(v_{PF}^{\prime}\) & actual & Actual hourly passenger flow \\ \hline \end{tabular}
\end{table}
Table 2: Information on the input variables.  
\begin{table}
\begin{tabular}{c c c c} \hline
**National Holiday** & **Order** & **Durations** & **Encoded Values** \\ \hline New Year’s Day & 1 & About 3 or 4 days & 11, 12, 13, 19 \\ Lunar New Year’s Day & 2 & About 6–9 days & 21–29 \\ Peace Memorial Day & 3 & About 3 days & 31, 32, 39 \\ Tomb-Sweeping Day & 4 & About 3 or 4 days & 41, 42, 43, 49 \\ Dragon Boat Day & 5 & About 3 or 4 days & 51, 52, 53, 59 \\ Mid-Autumn Day & 6 & About 3 or 4 days & 61, 62, 63, 69 \\ National Day & 7 & About 3 days & 71, 72, 79 \\ \hline \end{tabular}
\end{table}
Table 3: Durations of the national holidays and their encoded values.  
The tuning parameters for the deep LSTM_NN and one-layer LSTM_NN are shown in Table 4. The best tuning results of parameters for the deep LSTM_NN, one-layer LSTM_NN, and RF are given in Table 5. As shown in Table 5 and the experimental results in [26], the three-layer LSTM_NN with RMSE = 490.77 and MAPE = 0.0547 has the better forecasting results than the one-layer LSTM_NN and RF. The experimental results verify that the deep LSTM_NN enables more accurate results than either one-layer LSTM_NN or RF.