# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.3 Support Vector Machine

Table 5: Summary of representative literature on support vector machines. the taxi time period can be predicted by using statistical or machine learning methods with actual data. In 2015, [PERSON] et al. [2] conducted real-time and static prediction of taxi time, respectively, and used K-nearest-neighbors and Support Vector Regression methods to forecast the taxi time in real-time. That is, firstly, the K-nearest neighbor (KNN) method is used to predict the number of aircraft taking off from the same runway during the taxi-out time period, which is combined with other influencing factors to form a set of feature variables as model inputs, and the Support Vector Regression method is used to predict the taxi-out time. On this basis, in 2017, [PERSON] et al. [50] similarly divided the taxi time prediction into two stages but optimized the previous study. In contrast to the method of directly constructing the SVR model in the literature [2], this literature grouped the data according to the aircraft's off-block time. The experimental structure shows that the prediction model using the KNN-SVR method is superior to the direct construction of the SVR nonlinear regression prediction model, and KNN is more suitable for predicting the number of aircraft departing from the same runway during the taxi-out time period than the SVR method, while SVR is more suitable for predicting the taxi-out time than KNN, and it has a higher prediction accuracy after grouping the data. The above-mentioned studies have demonstrated the effectiveness of combined models and improved SVR methods in optimizing taxi time prediction. However, some scholars have achieved even greater advancements in prediction accuracy by focusing on enhancing the SVR method itself. In 2019, [PERSON] et al. [4] utilized the least squares Support Vector Regression method to predict taxi-out time and further optimized its parameters using the improved firefly algorithm. Building upon this research, in the same year, [PERSON] et al. [57] established a taxi-out time prediction model employing two improved Support Vector Regression (SVR) methods based on swarm intelligence algorithms: the particle swarm algorithm (PSO) and the firefly algorithm. Historical data validation revealed that departure delay significantly influenced taxi-out time. The improved SVR methods, particularly the optimized SVR method based on the improved firefly algorithm (IFA), exhibited enhanced prediction accuracy. Additionally, these methods demonstrated strong performance in handling abnormal taxi-out time states. Through the application of advanced optimization techniques, such as the improved firefly algorithm and swarm intelligence algorithms, researchers have effectively improved the accuracy of taxi time prediction and enhanced the ability to handle complex and challenging prediction scenarios. From the existing studies on aircraft taxi time, it becomes evident that taxi time exhibits continuity, linearity, and autocorrelation within its time series. However, the intricate nature of airport surface operations often leads to nonlinear fluctuations in taxi time. To address these challenges, [PERSON] et al. [65] conducted an analysis of the time series and modeled its linear components using the ARIMA (Autoregressive Integrated Moving Average) method to obtain predicted values and residuals. They further applied Support Vector Regression (SVR) to model the nonlinear behavior of the residuals. The experimental results revealed that this method significantly improved the prediction accuracy, which reached up to 90%. By adopting a hybrid approach that combines ARIMA for linear modeling and SVR for nonlinear modeling, the researchers effectively captured both the continuous and linear aspects of taxi time series while also addressing the nonlinear fluctuations.