# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Data Description  
The passenger flow dataset is collected from the Hangzhou Metro system, which contains approximately 70 million pieces of data from 3 lines covering 80 stations, as illustrated in Figure 9. Due to different operating schedules at different stations, we uniformly analyzed passenger flow data for a total of 18 h from 5:30 to 23:30 every day. The time granularities used in this section are 10, 15, and 30 min. Taking the 10 min time granularity as an example, a station has 144 time slots per day, as shown in Table 2. The original dataset covers metro data from 1 st to 25 th January 2019, which includes time, line ID, station ID, device ID, status, user ID, and payment type. The status in the dataset represents the passenger's entry and exit status, where 0 represents the exit, and 1 represents the entry. A total of 80% of the dataset is chosen as training data and validation data, and the remaining 20% is used as the testing data.  
The passenger flow is preprocessed with the min-max normalization method, as defined in Equation (9).  
\[x_{i}^{{}^{\prime}}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **5:30–5:40** & **5:40–5:50** & **5:50–6:00** & \(\dots\) & **23:10–23:20** & **23:10–23:20** & **23:20–23:30** \\ \hline
0 & 1 & 4 & 3 & \(\dots\) & 1 & 0 & 0 \\
1 & 0 & 5 & 5 & \(\dots\) & 2 & 1 & 1 \\
2 & 0 & 9 & 11 & \(\dots\) & 6 & 4 & 1 \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\
79 & 0 & 0 & 4 & \(\dots\) & 5 & 7 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data format of metro passenger flow.  
Figure 9: Hangzhou metro system map.  
where \(x_{i}^{\prime}\) is the normalized value and \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of passenger flow data, respectively.  
By analyzing the correlation of passenger flow at different stations, the characteristics of the entire metro network can be revealed. These characteristics not only help to understand the interactions between different stations but also help determine if there are common patterns and trends in passenger flow. As shown in Figure 10, the spatial correlation between adjacent stations is significantly strong due to the proximity of their geographical locations, facilitating flow and interchange among passengers. Furthermore, it is crucial to recognize that some stations are not physically adjacent but might exhibit similarities owing to common characteristics. These commonalities may be caused by factors such as population distribution, station planning, and urban structure, resulting in similar trend and demand patterns between non-adjacent stations.  
In order to further visualize the fluctuations in passenger flow, we have chosen three stations for analysis during the same time period, including Fengqi Road Station, Wulin Square Station, and Qingchun Square Station. Among these three stations, Fengqi Road Station and Wulin Square Station are adjacent, while Fengqi Road Station and Qingchun Square Station are not adjacent to each other. As illustrated in Figure 11, in spite of the differences in geographic locations, all of these three stations demonstrate similar passenger inflow and outflow patterns, which suggests that it is essential to consider the inter-station interactions.  
Figure 11: Passenger flow at three stations. (**a**) Flow of Fengqi Road Station; (**b**) Flow of Wulin Square Station; (**c**) Flow of Qingchun Square Station.  
Figure 10: Pearson correlation coefficient between different passenger flows at all stations. (**a**) Passenger inflow; (**b**) Passenger outflow.