## 2 Methodology
### Prediction Block
#### 2.2.1 Spatial Feature Extraction  
(1) Inflow  
As shown in Figure 2, to better capture the spatial features, we extract three data modes from inflow data, namely real-time, daily, and weekly, from different periodicities in different time periods. The three types of data are shown in Equations (19)-(21). \[X^{R}_{S_{i},T}=\left\{ \begin{array}{cccc}x^{R}_{1,\ t-n}&x^{R}_{1,\ t-n+1} &&x^{R}_{1,\ t-1}\\ x^{R}_{2,\ t-n}&x^{R}_{2,\ t-n+1}&&\ldots&x^{R}_{2,\ t-1}\\ \vdots&\vdots&&\ddots&\vdots\\ x^{R}_{S_{i},\ t-n}&x^{R}_{S_{i},\ t-n+1}&\ldots&x^{R}_{S_{i},\ t-1}\\ \end{array} \right\} \tag{19}\]  
\[X^{D}_{S_{i},T}=\left\{ \begin{array}{cccc}x^{D}_{1,\ t-n-DS}&x^{D}_{1,\ t-n+1- DS}&&x^{D}_{1,\ t-1-DS}\\ x^{D}_{2,\ t-n-DS}&x^{D}_{2,\ t-n+1-DS}&&x^{D}_{2,\ t-1-DS}\\ \vdots&\vdots&&\ddots&\vdots\\ x^{D}_{S_{i},\ t-n-DS}&x^{D}_{S_{i},\ t-n+1-DS}&\cdots&x^{D}_{S_{i},\ t-1- DS}\\ \end{array} \right\} \tag{20}\]  
\[X^{W}_{S_{i},T}=\left\{ \begin{array}{cccc}x^{W}_{1,\ t-n-M}&x^{W}_{1,\ t-n+1- M}&&x^{W}_{1,\ t-1-M}\\ x^{W}_{2,\ t-n-M}&x^{W}_{2,\ t-n+1-M}&&\ldots&x^{W}_{2,\ t-1-M}\\ \vdots&\vdots&&\ddots&\vdots\\ x^{W}_{S_{i},\ t-n-M}&x^{W}_{S_{i},\ t-n+1-M}&\cdots&x^{W}_{S_{i},\ t-1-M}\\ \end{array} \right\} \tag{21}\]  
\[I_{Inflow}=X^{R}_{S_{i},T}\Big{|}\Big{|}X^{D}_{S_{i},T}\Big{|}\Big{|}X^{W}_{S_ {i},T} \tag{22}\]  
where \(X^{R}_{S_{i},T}\), \(X^{D}_{S_{i},T}\), and \(X^{W}_{S_{i},T}\) represent the real-time, daily, and weekly inflow data, respectively. We use \(n\) historical time steps data {\(t-n,\ t-n+1,\ldots,\ t-1\)} to predict the \(t\) time step inflow. For example, \(t\) represents 9:00 am on Tuesday in the second week. \(X^{R}_{S_{i},T}\) refers to a series of inflow data of the first \(n\) time intervals before 9:00 am on Tuesday in the second week to predict the inflow at 9:00 am on Tuesday in the second week. \(X^{D}_{S_{i},T}\) refers to a series of inflow data of the first \(n\) time intervals before 9:00 am on Monday in the second week to predict the inflow at 9:00 am on Tuesday in the second week. \(X^{W}_{S_{i},T}\) refers to a series of inflow data of the first \(n\) time intervals before 9:00 am on Tuesday in the first weekto predict the inflow at 9:00 am on Tuesday in the second week. Thus, \(T\) used in \(X^{R}_{S_{i}T}\), \(X^{D}_{S_{i}T}\), and \(X^{W}_{S_{i}T}\) is from the second week of training data. \(X^{R}_{S_{i}T}\), \(X^{D}_{S_{i}T}\), and \(X^{W}_{S_{i}T}\) will be concatenated by column as the input \(I_{Inflow}\) based on Equation (22). As we know, deeper models can extract richer features [20], but it often brings risks of gradient disappearance and gradient explosion [7]. Therefore, some scholars proposed a residual network with jump links to solve this problem [21]. Residual connection reduces the complexity of the model to avoid overfitting, which is shown in Equation (23). \[\left(O_{Inflow}\right)_{RB}=F\Big{(}\left(I_{Inflow}\right)_{RB}\Big{)}+\left( I_{Inflow}\right)_{RB} \tag{23}\]  
where \(\left(I_{Inflow}\right)_{RB}\) and \(\left(O_{Inflow}\right)_{RB}\) refer to the input and output of the residual block, respectively. \(F(\cdot)\) refers to the processing of the residual block, which is shown in Figure 3.  
As shown in Figure 3, the input \(\left(I_{Inflow}\right)_{RB}\) will go through a series of processing: BN represents batch normalization for data normalization, ReLU is an activation function, and Conv denotes a convolutional layer. Figure 2 shows two same-shape residual blocks used for inflow feature extraction. Then, the extracted features will be flattened and sent to a feed-forward network for full connection to extract the features based on Equations (13) and (14). \(O_{p\_i}\) is the final output of inflow processing. (2) Outflow  
The outflow processing is identical to the inflow processing. Hence, its final output is given by \(O_{P\_O}\). \(X^{R}_{S_{i}T}\), \(X^{D}_{S_{i}T}\), and \(X^{W}_{S_{i}T}\) represent the real-time, daily, and weekly outflow data, respectively.