# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 4 GTCN Algorithm
### Gating Control Unit  
As an important feature of RNN varieties, the gating unit can capture the dependence of the time series well. For example, the double gates of GRU and the triple gates of LSTM can solve the problem that RNN cannot deal with, the long-term dependence that is caused by the gradient disappearance and gradient explosion. The function of the Gating Control Unit in GTCN is to save the characteristic information of each segment, adjust the prediction result of each segment and improve its accuracy. In a segmented time sequence, because the distance between each segment and the forecast target is different, the influence of each segment to the forecast target is also different. Generally, the farther the distance of the target segmentation forecast, the influence to predict the target is smaller. Thus, in this study we added a historical information control gate and an input information control gate to control the influence of historical information and current information on the prediction target. The structure of GRU's gating unit is shown in Figure 5.  
The gating control unit consists of two parts: the historical information control gate and the input information control gate. The influence degree of historical subsection information \(G_{t-1}\) on the prediction results was determined through the historical information control gate. The input information control gate is used to determine the contribution degree of the current input segmented information \(x_{t}\) in the prediction. Both the historical information control gate and the input information control gate of the gate control unit depend on  
Figure 5: Gating Control Unit.  
the output value of the Sigmoid excitation function to determine the influence degree of information. The calculation method of the Sigmoid excitation function is as follows:  
\[S(t)=\frac{1}{1+e^{-t}} \tag{4}\]  
where \(t\) is the sequence after weight calculation.  
The output value of the Sigmoid excitation function is between 0 and 1, which is used to control the output value of the historical information control gate and the input information control gate. Overall, 1 means completely reserved, and 0 means completely discarded. The calculation method of the historical information control gate is as follows:  
\[ht=\sigma(W^{(h)}x_{t}+U^{(h)}G_{t-1}) \tag{5}\]  
where \(\sigma\) is the excitation function Sigmoid, \(x_{t}\) is the input sequence, \(G_{t-1}\) is the information of the gate of the previous time step \(t-1\), \(W^{(h)}\) and \(U^{(h)}\) are the weight matrices of the historical information control gate. The historical information control gate provides a final output as an activation result between 0 and 1 to determine the degree of influence of the historical information on the predicted results.  
The calculation method of the input information control gate is as follows:  
\[i_{t}=\sigma(W^{(i)}x_{t}+U^{(i)}G_{t-1}) \tag{6}\]  
The equation of the input information control gate is similar to the historical information control gate. \(W^{(i)}\) and \(U^{(i)}\) are the weight matrices of the input information control gate. Finally, an activation value between 0 and 1 is also output to determine the contribution degree of the current input information \(x_{t}\) to the predicted result.  
Finally, the output value of the historical information control gate and the input information control gate can be used to determine the information to be collected from the current input segment \(x_{t}\) and the previous time step information \(G_{t-1}\). The calculation formula is as follows:  
\[G_{t}=h_{t}\odot WG_{t-1}+i_{t}\odot Ux_{t} \tag{7}\]  
\(W\) and \(U\) are the weight matrices of the new memory content. Thus, the final memory of the current time step of the gated neural unit can be determined.