# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Performance Metrics  
Regarding the evaluation metrics of prediction, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) are commonly used error metrics to assess the deviation between predicted and actual values. Among them, RMSE is the root mean square value of the difference between the predicted value and the actual value, MSE is the mean value of the square of the difference between the predicted value and the actual value, and MAE is the mean value of the absolute value of the difference between the predicted value and the actual value, and the smaller the value of the three indicates that the prediction accuracy is higher. Furthermore, a smaller error is usually acceptable in actual taxi time prediction. The prediction accuracy in three ranges of 1, 3, and 5 min is also used to measure the accuracy of the prediction results as well. Table 10 lists the model predictors from the literature [4; 17; 61; 64; 65]. Models in the selected literature include Multilayer Perceptron (MP), Linear Regression (LR), Polynomial Regression (PR), Gradient Boosting Regression Trees (GBRT), Random Forest (RF), Generalized Linear Model (GLR), Support Vector Regression (SVR), Adaptive Least Squares Support Vector Regression based on improved Firefly algorithm (IFA-LSSVR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear and Linear Optimized Sequencing (LINOS), Multi Regression (MR), Spatial--Temporal--Environment Deep Learning Model (STEDL), Autoregressive Integrated Moving Average (ARIMA).  
In the literature [64], compared to the other four models, the RF model has the best predictive performance, its handling of outliers is better, and it shows better generalization ability on various datasets. Additionally, the RF model is less prone to overfitting compared to some other models due to the voting of multiple trees. Meanwhile, the literature [64] uses feature importance selection in RF to provide a quantitative measure of the contribution of model features, and also provides guidance for optimizing the model and improving explanatory and predictive performance. For the application of the discrete-event fast-time simulation tool to predict the taxi time, it can be seen that the LINOS prediction accuracy is comparable to that of the SVM [17]. In the literature [4; 61; 65], the combined model showed higher prediction accuracy. For the treatment of abnormal glide time states, the SVR algorithm based on IFA optimization shows good prediction performance. The use of IFA allows the model to learn the data features more efficiently, which improves the accuracy and robustness in predicting the abnormal glide time states [4]. The three main sub-models of the STEDL model consist of a spatial-temporal model and an environmental model (based on a convolutional neural network), and a fully connected spatial model. This structure allows the STEDL model to predict aircraft taxi times more comprehensively and accurately. The spatial-temporal model takes into account the spatial-temporal variations of the aircraft, the environmental model takes into account external factors such as weather and airport traffic, and the convolutional neural network and fully-connected layer help to efficiently learn and integrate the various features, thus improving the prediction performance [61]. Compared with a single prediction model, the slip time prediction model based on the combination of ARIMA and SVR methods can better cope with the linear and nonlinear features of the data. The characterization role of SVR function for nonlinear data is used, and the machine learning approach is applied to the combined model, which effectively reduces the error of single ARIMA model in predicting the time series and improves the prediction accuracy [65].