# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 5 Conclusions  
Predicting ATT can reduce taxi wait times and overall delays, while optimizing resource allocation by better managing the use of airport runways, taxiways, and gates, thus improving the overall operational efficiency of the airport. Therefore, we proposed the NOA-XGBoost algorithm to predict the ATT.  
First, we established an effective multiple linear regression model by selecting departure and arrival indicators to calculate the UTT. Then, we obtained the true values of ATT through an indirect method. For ATT, we proposed three new features: corridor departure flow, corridor arrival flow, and departure flow proportion of ODP. We then constructed a dataset and proposed the NOA-XGBoost model to predict the ATT. Finally, we conducted an experimental analysis using Shanghai Pudong International Airport as a case study. The comparison of different methods demonstrated the effectiveness of the proposed approach. Ablation experiments on features confirmed the validity of the proposed features. Predictions using different pairing methods provide data features for corridor and non-corridor scenarios, validating the algorithm's adaptability.  
By dividing taxi-out time into the UTT and ATT, we can analyze the influencing factors of different times separately, leading to a more comprehensive and in-depth study and optimization of airport ground operations, thereby improving the overall efficiency and safety of the aviation traffic system. Additionally, predicting the ATT rather than the total taxi-out time allows for a more precise reduction of delay times and provides a new perspective for predicting taxi times.  
Our research still has certain limitations. For example, the analysis and validation are only conducted for Shanghai Pudong International Airport, and the impact of weather factors on departure taxi time is not considered. Severe weather conditions, such as thunderstorms, snowstorms, and heavy rain, may significantly affect taxi times. In future research, we plan to extend the study to more airports with different surface layouts, such as Guangzhou Baiyun International Airport and Beijing Daxing International Airport, while also introducing additional feature variables to further refine the feature set for predicting departure taxi times.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Based** & **MAE** & **RMSE** & \(\pm\)**3 min** & \(\pm\)**5 min** & **(+5, \(-\)10) min** \\  & & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline NOA-XGBOOST & 3.94 & 5.62 & 0.51 & 0.74 & 0.85 \\ XGBoost & 4.04 & 5.82 & 0.51 & 0.72 & 0.84 \\ RF & 4.21 & 6.06 & 0.49 & 0.71 & 0.84 \\ SVR & 4.17 & 6.21 & 0.53 & 0.72 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction performance based on ACDM calculation time.  
**Author Contributions:** Conceptualization, L.Y.; methodology, L.Y. and J.L.; software, J.L.; formal analysis, J.L.; investigation, L.Y. and H.C.; resources, L.Y.; data curation, J.L.; writing--original draft preparation, J.L. and L.Y.; writing--review and editing, L.Y., J.L. and H.C.; supervision, L.Y. and H.C.; project administration, L.Y.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is supported by the National Key R&D Program of China (No.2021 YFB1600500).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We would like to thank the State Key Laboratory of Air Traffic Management System for providing the data used in the model tests described in this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.