# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 5 Conclusions  
Inspired by the close relationship between weather conditions and passenger flow forecasting, a detailed experimental analysis of the impacts of weather variables on passenger flow forecasting using a deep LSTM_NN was conceived. The experimental results show that the previous one-hour  
Figure 6: Comparison of forecasting results in the five methods. (**a**) Root mean square error (RMSE) of forecasting results in the five methods. (**b**) Mean absolute percentage error (MAPE) of forecasting results in the five methods.  
temperature (\(v_{Temp\_Pre\_1}\)) and previous one-hour wind speed (\(v_{Wind\_Pre\_1}\)), especially \(v_{Temp\_Pre\_1}\), are the two most important weather variables for passenger flow forecasting on rainy days. Compared to the other four algorithms, the deep LSTM_NN is a more powerful method to make the more accurate forecasts when suitable weather variables are included. Since the previous one-hour weather data are not difficult to obtain, the model developed herein for short-term metro passenger flow forecasting may be performed in practical applications.  
The majority of weather data present general variations, which do not have much impact on passengers' daily commuting, even in the case of heavy rain. This indicates that adding current hourly weather data to build a forecasting model may not be helpful in improving the forecasting results, because the weather data are more likely to be noisy data. The experimental results in Table 7 support this assumption. Fortunately, instead of using current hourly weather data, we used the previous one-hour weather data to develop the model, which yields the better results. These encouraging results demonstrate that understanding the socio-psychological aspects of the travel behavior of metro passengers is truly important to apply appropriate weather data for developing a more powerful passenger flow forecasting model. This special strategy is creative and helpful in data modeling.  
Because the testing data ran from November 2016 to March 2017, the weather during this period is relatively colder and drier. The experimental results can only verify that the proposed model can forecast passenger flows more accurately when weather conditions are broadly similar. As shown in Table 6, the training and testing examples are not rich enough for forecasting passenger flows on rainy days. In the future, we will collect more data to perform these forecasting tasks and further verify the results using days with adverse weather, such as typhoons, heavy rains, or high winds, and for weekends and national holidays with rainy days. In addition, because the weather conditions shift randomly at the hour-level, clustering methods may be used to group the training data and testing data based on different weather conditions before developing a forecasting model. As another possibility of exploring the impact of weather on passenger flow forecasting, the addition of seasonal variations or outdoor thermal comfort may also have an essential impact on travel behavior at specific traffic nodes [40]. All these represent productive directions for our future research.  
[PERSON] performed the experiment and wrote the manuscript; and [PERSON] and [PERSON] gave some suggestions for an improved method, reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C. (Grant Nos. MOST-106-2221-E-324-025 and MOST-107-2221-E-324-018-MY2), in part by the Middle-aged and Young Teacher Education and Research Project of the Education Department of Fujian Province (Grant No. JAT170416), in part by the \"Climbing\" Project of Xiamen University of Technology (Grant No. XPDKQ19008), and in part by the High-Level Talents Research Launched Project of Xiamen University of Technology (Grant No. YKJ19012R).  
The authors declare no conflict of interest.