## 4 Experiments and Results
### Analysis  
The capacity prediction results in Table 8 for the traditional WITI model and the actual traffic verify the feasibility of the present research model. The results are compared in Figure 10.  
The time node of the comparison curve was selected from 09:00 to 23:00 on 24 June, during which six types of bad weather appeared. This began at 11:00, after which the sector capacity began to change accordingly; there was no influence of bad weather in the sector from 09:00 to 11:00. The predicted results for the traditional WITI model were consistent with the actual observations, and the sector capacity was 27 operations/hour. At 11:00 the sector capacity predicted by WITI model changed to 19 operations/hour and then remained constant. Meanwhile, the predicted results for the DNN model varied from 12 to 27 operations/hour according to different sector capacities of severe-weather types. In order to further compare the prediction fit of the sector capacity between the traditional prediction model and the DNN model, representative time segments were selected from the database for data comparisons; the results are shown in Figure 11.  
Figure 11: Comparison of data over multiple days. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Interval** & \
\begin{tabular}{c} **Sector** \\ **Capacity/Route** \\ \end{tabular} & **Time Interval** &
\begin{tabular}{c} **Sector** \\ **Capacity/Route** \\ \end{tabular} \\ \hline
08:00–09:00 & 26 & 20:00–21:00 & 16 \\
09:00–10:00 & 27 & 21:00–22:00 & 12 \\
10:00–11:00 & 27 & 22:00–23:00 & 14 \\
11:00–12:00 & 24 & 23:00–24:00 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._  
Figure 10: Comparison of typical daily data. It can be seen from Figure 11 that when bad weather occurred, the forecasting capacity of the traditional forecasting model was 19 operations/hour, and this did not change with different types of bad weather. The forecasts of the DNN model varied from 12 to 23 operations/hour according to different types of bad weather. This is because, in the traditional WITH model, the weather-influence coefficient only has a value of 1 or 0; that is, it can only identify whether or not there is bad weather in the sector, without quantifying its severity. The prediction result for the sector capacity was 19 operations/hour when bad weather was present and 27 operations/hour when it was not present. The DNN model adds the optimized weather-influence coefficient to predict the sector capacity under corresponding conditions according to different types of bad weather, which makes the prediction result closer to the actual operating situation. By sorting and analyzing the training data, the sector capacity prediction results under different types of bad weather could be obtained; the results are presented in Table 11.  
The input variables of the model are the sector flow, instruction times, instruction duration, optimized weather-influence coefficient, and the number of diverted flights, and the output variable is the controller workload. The first three input variables are basic variables for calculating controller workload, while the weather-influence coefficient reflects the actual sector capacity affected by weather during the specific time period, and the number of diverted flights reflects that controllers need to issue instructions to coordinate aircraft conflicts during this time period. After model training, the controller workload involves predicting the sector capacity for the day, and at the same time, the traditional controller model is used to predict the sector capacity of the day. Comparing the two groups of data with the actual sector values revealed that the results predicted by the controller workload model considering the influence of bad weather were more consistent with the actual situation.