# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 4 Discussion and Conclusions
This allows for more accurate selection of features that are relatively independent and provide independent information without reducing predictive effectiveness, while controlling the number of features and reducing model training costs. To improve the reference for further research on taxi time prediction and analysis, the future research outlook is as follows:  
1. Research on scientific feature extraction methods for taxi time prediction: Existing studies mainly consider input features such as surface arrival/departure flow rates, taxiing distance, pushback time, and departure queue length for predicting taxi time based on historical data. However, other factors such as surface congestion, human factors [72; 78], and traffic management strategies that influence taxi time have received limited attention. Future studies should conduct in-depth analyses of factors affecting taxi time to determine the characteristic variables for the prediction model. Additionally, when grouping the data, researchers can explore different classification bases, such as grouping historical data into scenarios for different weather conditions, to establish prediction models for surface taxi time under specific weather conditions. This can enhance feature refinement and improve the predictability of arrival and departure taxiing processes. At the same time, consideration should be given to how to select features more accurately without reducing the effectiveness of forecasting. When selecting features, there is a trade-off between the number of features and the cost of model training. Too many features may lead to dimensionality catastrophe, increasing the training complexity and computational cost of the model. Moreover, choosing appropriate and highly relevant features is crucial for improving prediction. 2. Improvement of prediction performance under incomplete data conditions: The quality of the data is critical to the performance of the model, but the current surface data suffer from low quality and missing data, and consideration should be given to how to improve the accuracy of predictions under incomplete data conditions. In addition to improving data quality through handling outliers and imputing missing values, another viable approach is to focus on constructing more informative features to enhance the model's interpretability. Furthermore, when it comes to model selection, considering the incompleteness of the data, it is advisable to opt for robust models such as tree-based models and deep learning models. 3. Research on distributional uncertain taxi time prediction methods: Current research on surface taxi time prediction mainly revolves around deterministic prediction, providing a single fixed value as the prediction result. However, considering the high dynamics and uncertainty of the surface taxiing process, future research can explore probabilistic prediction methods to address uncertain taxi time distribution [66]. This approach can enhance the robustness and flexibility of surface operation control by considering the probabilistic nature of taxi time predictions. Conceptualization and determination of research direction, design of research framework, J.Y. and M.Z. ; Design and implementation of the entire classification system for the literature related to taxi time prediction, J.Y. ; Feature extraction and analysis of the literature related to taxi time, Y.M. and W.W.; Original draft preparation, J.Y. and W.W.; Classification of research methods, categorization and review of methods in the literature related to taxi time prediction, J.Y. and Y.M. ; Investigation, H.L. and P.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (No. 52002178), States Key Laboratory of Air Traffic Management System (No. SKLATM202206) and Natural Science Foundation of Jiangsu Province (No. BK20190416). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.