Method forward

baselines.py:232–270

(self, occ, prc)

        self.A_dem = a_delta

    def forward(self, occ, prc):  # occ.shape = [batch, node, seq]
        x = torch.stack([occ, prc], dim=3)
        x = self.encoder(x)
        x = torch.squeeze(x)
        x = self.act(self.linear(x))

        # distance-based graph propagation
        #  l1
        x1 = self.distance_gcn_l1(x)
        x1 = torch.matmul(self.A_dis, x1)
        x1 = self.dropout(self.act(x1))
        #  l2
        x1 = self.distance_gcn_l2(x1)
        x1 = torch.matmul(self.A_dis, x1)
        x1 = self.dropout(self.act(x1))
        # gru
        x1 = x1.transpose(1, 2)
        x1, _ = self.gru1(x1)
        x1 = x1.transpose(1, 2)

        # demand-based graph propagation
        #  l1
        x2 = self.demand_gcn_l1(x)
        x2 = torch.matmul(self.A_dem, x2)
        x2 = self.dropout(self.act(x2))
        #  l2
        x2 = self.demand_gcn_l2(x2)
        x2 = torch.matmul(self.A_dem, x2)
        x2 = self.dropout(self.act(x2))
        # gru
        x2 = x2.transpose(1, 2)
        x2, _ = self.gru2(x2)
        x2 = x2.transpose(1, 2)

        # decode
        output = self.alpha * x1 + (1-self.alpha) * x2
        output = self.decoder(output)
        output = torch.squeeze(output)
        return output


# https://arxiv.org/abs/2311.06190