# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 4 Discussion and Conclusions  
This paper documents the problem of taxi time prediction in airport operations. Firstly, from the perspectives of taxi time type, movement type, and modeling method, the research framework for taxi time prediction is systematically organized. This includes a review of the relevant literature on reference time and actual taxi time. From the analysis and simulation modeling aspects, the main focus is on categorizing and summarizing the \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Metrics** & **Range of Values** \\ \hline MAE & [0.26, 2.81] \\ RMSE & [0.901, 3.98] \\ \textless{}1 min (\%) & [26.04, 33.80] \\ \textless{}3 min (\%) & [53.98, 84.71] \\ \textless{}5 min (\%) & [76.77, 95.66] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation of model prediction accuracy. current mainstream taxi time prediction models and methods, both domestically and internationally. Currently, the predominant analysis and modeling approaches for taxi time prediction are based on machine learning methods including linear regression, neural networks, support vector machines, ensemble learning, and fuzzy rule systems. Linear regression is a simple yet effective model that assumes a linear relationship between input features and output. In the context of taxi time prediction, it can be used to capture the linear correlation between input features and taxi times. It is suitable for situations where a linear relationship exists between input features and output, yielding favorable results for simple datasets and problems. Neural networks, being a powerful model capable of learning complex nonlinear relationships, are particularly advantageous in taxi time prediction. In this context, neural networks can automatically learn the intricate relationships among input features, making them suitable for various data patterns. They excel in addressing complex taxi time prediction problems characterized by strong nonlinear relationships. However, it is important to note that neural networks require substantial amounts of data and parameter tuning for optimal performance. Support vector machines, on the other hand, leverage the identification of an optimal hyperplane in feature space for classification or regression. In taxi time prediction, support vector machines demonstrate efficiency in handling high-dimensional feature spaces. They are applicable to scenarios involving high-dimensional feature spaces and complex boundaries, performing well even with small sample sizes. Ensemble learning, which is a methodology involving the combination of predictions from multiple models such as random forests or gradient boosting trees, enhances overall performance in taxi time prediction. This approach is versatile, suitable for various data patterns and problems, and it mitigates overfitting risks while improving generalization capabilities. Fuzzy rule systems employ fuzzy logic to model systems with inherent fuzziness. In the context of taxi time prediction, these systems consider uncertainty and fuzziness, providing a more flexible approach to address complex traffic situations. Fuzzy rule systems are particularly suitable for problems requiring the handling of fuzzy and uncertain information, offering adaptable modeling techniques. Similarly, the selection of data is crucial for the performance and generalization ability of machine learning models. Regardless of how powerful a model is, if the quality of input data is poor, the model's performance will be adversely affected. In the feature selection process, highly correlated features can be excluded by correlation analysis to reduce redundant information. Then, feature selection techniques, such as statistical methods (ANOVA and correlation tests) and model-based methods [64] (LASSO regression and feature importance of decision trees), are applied to identify the features that have the greatest impact on the target variable.