## 3 Methods
### Omnidirectional Attention Module  
The OAM is proposed to fine-tune the importance of each element of the input feature in the channel-spatial dimension by generating a learnable omnidirectional attention map, so as to achieve more effective feature expression. The architecture of the proposed OAM is shown in Figure 2. As shown in Figure 2, the input feature and output feature are, respectively, \(X\in\mathbb{R}^{C\times H\times W}\) and \(Y\in\mathbb{R}^{C\times H\times W}\), where \(C\), \(H\), and \(W\), respectively, denote the number of channels, the height, and the width. In the proposed OAM, the omnidirectional  
Figure 1: Temporal relationship between the historical OD data and the predicted OD matrix. attention map \(P_{1}\in\mathbb{R}^{C\times H\times W}\) is first generated by a dot-product operation between the generated channel attention map \(R_{1}\) and the generated spatial attention map \(R_{2}\), and then used to fine-tune the elements in the input feature. The specific process of the proposed OAM is as follows. Firstly, the channel attention map \(R_{1}\in\mathbb{R}^{C\times H\times W}\) is generated to measure the importance of each feature map in the input feature, which can be formulated as:  
\[G_{1} =w_{1}GAP_{s}(X)\oplus w_{2}GMP_{s}(X), \tag{4}\] \[L_{1} =\sigma_{lr}(g_{fc}(G_{1})),\] (5) \[S_{1} =\sigma_{sig}(g_{fc}(L_{1})),\] (6) \[R_{1} =\psi_{s}(S_{1}), \tag{7}\]  
where \(w_{1}\) and \(w_{2}\) are the trainable weights, and \(\oplus\) represents the adding operation. \(GAP_{s}(\cdot)\) and \(GMP_{s}(\cdot)\) denote the global average pooling operation and the global max pooling operation in the spatial dimension, respectively. Note that the spatial dimension denotes the two-dimensional coordinate space consisting of the height axis and the width axis. \(g_{fc}(\cdot)\) represents the fully connected layer. \(\sigma_{lr}\) and \(\sigma_{sig}\) denote the leaky ReLU activation function and the sigmoid activation function, respectively. \(\psi_{s}(\cdot)\) represents the repeat operation in the spatial dimension. In Equation (4), compared with using only one global pooling operation, using two different global pooling operations in the spatial dimension can obtain two different feature statistics, thus inferring finer channel attention. Thus, \(G_{1}\in\mathbb{R}^{C\times 1\times 1}\) is generated. To reduce parameter overhead while ensuring the flexibility of learnable attention map generation, the fully connected layers in Equations (5) and (6) are used to obtain \(L_{1}\in\mathbb{R}^{C/r\times 1\times 1}\) and \(S_{1}\in\mathbb{R}^{C\times 1\times 1}\), respectively, where \(r\) is the reduction ratio. After the repeat operation in Equation (7), the channel attention map \(R_{1}\in\mathbb{R}^{C\times H\times W}\) is generated. Simultaneously, the spatial attention map \(R_{2}\in\mathbb{R}^{C\times H\times W}\) is generated to measure the importance of each element in the input feature in the spatial dimension, which can be formulated as:  
\[G_{2} =[GAP_{c}(X),GMP_{c}(X)], \tag{8}\] \[L_{2} =\sigma_{lr}(g_{bn}(g_{conv}(G_{2}))),\] (9) \[S_{2} =\sigma_{sig}(L_{2}),\] (10) \[R_{2} =\psi_{c}(S_{2}), \tag{11}\]  
where \([\cdot,\cdot]\) denotes the concatenation operation along the channel axis, and \(GAP_{c}(\cdot)\) and \(GMP_{c}(\cdot)\) denote the global average pooling operation and the global max pooling operation along the channel axis, respectively. \(g_{conv}(\cdot)\) and \(g_{bn}(\cdot)\) denote the two-dimensional convolution and the batch normalization, respectively. \(\psi_{c}(\cdot)\) represents the repeat operation along the channel axis. In Equation (8), compared with using only one global pooling  
Figure 1:0 The architecture of omnidirectional attention module. operation, using the global average pooling operation and the global max pooling operation along the channel axis can infer finer spatial attention. Thus, \(G_{2}\in\mathbb{R}^{2\times H\times W}\) is generated.