# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 4 Experimental Results and Discussion
### Results of Using Weather Variables
To understand the socio-psychological aspects of the travel behavior of metro passengers, we suppose that individuals may give up their plans to go out if it is raining outside, especially during non-working hours. In this case, the previous one-hour weather variables may be the more important inputs for forecasting tasks than the current hourly weather variables. Therefore, Models 17-31 were built by the various combinations of the previous one-hour weather variables, in order to improve the results of forecasts made using no weather variable on rainy days. Table 8 shows the experimental results for Models 1 and 17-31. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Total Examples**} & \multirow{2}{*}{**Raining Examples**} & \multicolumn{2}{c}{**Ratio**} \\  & & & **(Raining Examples/Total Examples)** \\ \hline Training dataset & 14,164 & 1748 & 12.34\% \\ Testing dataset & 5898 & 524 & 11.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Description of training dataset and testing dataset on rainy days. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{5}{c}{**Input Variables**} & \multicolumn{5}{c}{**Deep LSTM_NN**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Optimized**} & & & & \\  & \multicolumn{2}{c}{**Traditional Input**} & \(v_{Temp}\) & \(v_{Rain}\) & \(v_{Hum}\) & \(v_{Wind}\) & **RMSE** & **MAPE** \\  & \multicolumn{2}{c}{**Variables**} & & & & & \\ \hline
1 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & 753.88 & 0.0741 \\
2 & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & 754.76 & 0.0730 \\
3 & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & 726.76 & 0.0760 \\
4 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & \(\bullet\) & 770.12 & 0.0732 \\
5 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bigtimes\) & \(\bullet\) & 745.83 & 0.0815 \\
6 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & 805.87 & 0.0818 \\
7 & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bigtimes\) & 771.99 & 0.0846 \\
8 & \(\bullet\) & \(\bigstar\) & \(\bigtimes\) & \(\bigtimes\) & \(\bullet\) & 748.08 & 0.0757 \\
9 & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & 820.02 & 0.0787 \\
10 & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & 787.60 & 0.0864 \\
11 & \(\bullet\) & \(\bigtimes\) & \(\bigtimes\) & \(\bullet\) & \(\bullet\) & 793.76 & 0.0760 \\
12 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & 829.60 & 0.0987 \\
13 & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & 855.64 & 0.0801 \\
14 & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & \(\bullet\) & 785.41 & 0.1001 \\
15 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bigtimes\) & \(\bullet\) & 804.82 & 0.0734 \\
16 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & 823.14 & 0.0849 \\ \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 7: Results of Models 1 and 2–16. As shown in Table 8, both RMSE and MAPE in Models 17 and 23 are lower than Model_1. The better experimental results indicate that using the previous one-hour weather data as the input variable can improve the forecasting results of passenger flow on rainy days. These results match our assumptions about travelers' psychology. Using the combined weather variables of \(v_{Temp\_Pre\_1}\) and \(v_{Wind\_Pre\_1}\) in Model_23 results in more accurate forecasts than Model_1 by RMSE. The results indicate that travelers may cancel their travel plans if the weather is cold and windy on rainy days. These two previous one-hour weather variables, namely \(v_{Temp\_Pre\_1}\) and \(v_{Wind\_Pre\_1}\), may be helpful in developing a more powerful forecast model on rainy days.