# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 4 Experiment and Results
### Results and Discussion
#### 4.2.3 Prediction for Different Pairing Method  
To study the algorithm's adaptability and the ATT for cross-corridor and non-cross-corridor pairing methods, this section focuses on predictions using different region and runway pairing methods. The results are shown in Table 7 and Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Features Removed** & **MAE** & **RMSE** & **R\({}^{2}\)** & **\(\pm\)3 min Accuracy** & **\(\pm\)5 min Accuracy** & **(+5, \(-10\)) min Accuracy** \\ \hline None & 2.06 & 2.73 & 0.77 & 0.77 & 0.94 & 0.97 \\ Airline category & 2.07 & 2.74 & 0.76 & 0.76 & 0.94 & 0.97 \\ Aircraft category & 2.09 & 2.75 & 0.76 & 0.75 & 0.93 & 0.96 \\ Restricted status category & 2.07 & 2.75 & 0.75 & 0.75 & 0.93 & 0.95 \\ Time category & 2.07 & 2.74 & 0.77 & 0.75 & 0.93 & 0.96 \\ Normal features of scene traffic flow & 2.46 & 3.39 & 0.65 & 0.71 & 0.89 & 0.94 \\ Structure-related features & 2.23 & 2.92 & 0.70 & 0.74 & 0.91 & 0.94 \\ \hline \end{tabular}
\end{table}
Table 6: Prediction performance under different features.  
Figure 10: Prediction error comparison.  
From the Table 7, it can be seen that among the four methods, the NOA-XGBoost model performs the best. The \(\pm\)3 min accuracy for the NOA-XGBoost method is around 80%, \(\pm\)5 min accuracy is around 90%, and (+5, \(-\)10) min accuracy can reach 95%.  
Figure 11: _Cont._  
To investigate the features and prediction effects of the ATT under different pairing methods, we compare the prediction results of the XGBoost algorithm with those of the NOA-XGBoost algorithm, as shown in Figure 11. In the figure, the \(x\)-axis represents the actual values, the \(y\)-axis represents the predicted values, and the red dashed line represents the function \(y=x\). The closer the data points are to this line, the better the prediction performance. Among the four pairing methods, East Control Zone-17L35R and West Control Zone-16R34L are cross-corridor methods, characterized by longer taxiing paths and a higher likelihood of conflicts during taxiing, leading to greater uncertainty and prediction difficulty. As a result, the ATT is longer and the data distribution is more scattered compared to the other two non-cross-corridor pairing methods. Comparing the two algorithms, it can also be observed that for all four pairing methods, the data using the NOA-XGBoost algorithm are closer to the red dashed line than those using the XGBoost algorithm, demonstrating the superiority of the NOA-XGBoost algorithm.  
Figure 11: Prediction performance of different pairing methods under different approaches: (**a**) Prediction of East Control Area—16R34L Using XGBoOST; (**b**) Prediction of East Control Area—16R34L Using NOA-XGBoOST; (**c**) Prediction of East Control Area—17L35R Using XGBoOST; (**d**) Prediction of East Control Area—17L35R Using NOA-XGBoOST; (**e**) Prediction of West Control Area—16R34L Using XGBoOST; (**f**) Prediction of West Control Area—16R34L Using NOA-XGBoOST; (**g**) Prediction of West Control Area—17L35R Using XGBoOST; (**h**) Prediction of West Control Area—17L35R Using NOA-XGBoOST.