# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 2 Input Variables
### Analysis of Impacts of Weather Conditions on Hourly Passenger Flow
#### 2.4.2 Impact of Rainfall on Hourly Passenger Flow  
Figure 3 shows the relationship between rainfall and hourly passenger flow on weekdays, weekends, and national holidays. To highlight the impact of rainfall on passenger flow, only the hourly passenger flow on the rainy days is given. The blue bars represent the millimeters of hourly rainfall, and the corresponding hourly passenger flow is marked by the broken black line. To compare the differences between the hourly passenger flow on rainy days, days without rain, and both, we calculated the average hourly passenger flow and marked it with the green dotted line, red dotted line, and yellow dotted line, respectively.  
Figure 2: Annual weather conditions from 1 November 2015 to 31 October 2016 around Taipei Main Station.  
Figure 3: Impact of rainfall on hourly passenger flow in the training dataset. **(a)** Impact of rainfall on hourly inbound passenger flow on all Mondays in the training dataset. **(b)** Impact of rainfall on hourly outbound passenger flow on all Saturdays in the training dataset. **(c)** Impact of rainfall on hourly inbound passenger flow on all national holidays in the training dataset.  
Figure 3a illustrates the impact of rainfall on the inbound passenger flow at different hours on all Mondays during the training dataset. The three peak hours each day are 8:00, 18:00, and 21:00. The red, green, and yellow dotted lines almost overlap with each other. This show that the passenger flow on weekdays is scarcely affected by rain since most trips represent commutes, even if the trips are during the morning and evening peak hours. The lagged impact of rainfall on passenger flow is not obvious after a heavy rain at 6:00.  
Figure 3b illustrates the impact of rainfall on the hourly outbound passenger flow at different hours on all Saturdays. The most prominent peak hours are 16:00 to 18:00. These single-peak hours are quite different with the double-peak hours on weekdays. Clearly, the hourly passenger flow on the rainy days is less than the passenger flow on days without rain, as during 6:00-13:00 in Figure 3b. However, if the rain is not heavy, people still execute their travel plans, especially 16:00 to 18:00. This period has the largest passenger volume on weekends.  
Figure 3c depicts the impact of rainfall on hourly inbound passenger flow on all national holidays. The most obvious peak hours are 17:00 and 21:00. The difference between the passenger flow on rainy days and on days without rain becomes much greater than in the scenarios in Figure 3b. The impact of rainfall on hourly passenger flow at 8:00, 9:00, 11:00, 15:00, 17:00, 19:00, and 20:00 is greater than the impact at other hours. The flow at 17:00 falls dramatically on rainy days. This shows that the impact of rainfall on hourly passenger flow varies across the morning, noon, and evening hours on national holidays.  
In summary, daily trips on national holidays and weekends are more likely to be affected by rainy weather than trips on weekdays, especially on national holidays, findings that are similar to [5]. It is verified that the fluctuation of the black line in Figure 3c is more serious compared to the other two. The possible reason is that travel plans on national holidays and weekends are not necessary, and people are more likely to change or cancel their travel plans due to the rainy days. Although the weather conditions are extremely complex, understanding them is important to improve the hourly passenger flow forecasting results for Taipei Main Station.