# 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 shows a compilation of recent research on the application of Support Vector Machine (SVM) in taxi time prediction. Scholars commonly approach taxi time prediction in two stages, or they utilize the improved Support Vector Regression (SVR) method for modeling. SVR, being the regression version of SVM, finds application in both nonlinear and linear regression, making it a widely used technique in taxi time prediction. In previous studies, the characteristic variable of the number of arrival and departure aircraft during the taxi time period is usually estimated using the expected takeoff or landing time of the aircraft. This method is reasonable when the airport surface traffic is more stable, but in the case of more flight delays, the number of aircraft calculated by this method may differ significantly from the actual number of arrival and departure aircraft. Therefore, in order to predict the taxi time more accurately, the set of feature variables in the model needs to be optimized, and the number of arrival and departure aircraft during \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Authors** & **Approach** & **Features Considered in the Model** \\ \hline \multirow{4}{*}{[2]} & & & Airport layout: taxiing distance \\  & & & & Surface traffic flow: number of aircraft taxiing out from the same \\  & & & & runway at the off-block time, number of aircraft using the same runway for takeoff and landing during the taxi-out time, average taxi-out time from the same runway in the 15 min before the off-block time, number of aircraft expected to take off and number of aircraft expected to arrive during the taxi-out time \\  & & & Airport layout: taxiing distance \\
[50] & & KNN (K Nearest)-SVR & Surface traffic flow: number of arrival and departure aircraft on the \\  & & & same runway, average taxi time on the same runway for 15 min before the off-block time \\
[54] & & & & Surface traffic flow: 21 characteristics such as surface instantaneous \\  & & & flow indices (SIFIs), surface cumulative flow indices (scfi), aircraft \\  & & & queue length indices (AQLIs), and slot resource demand indices (SRDIs) \\
[4] & & & Airport layout: taxiing distance \\
[28] & & Support Vector Regression & Surface traffic flow: Length of departure queue on the taxiway, number of arrival aircraft during the taxi-out time \\  & & & & Others: scheduled takeoff time \\
[5] & & Support Vector Regression—BP neural network & Airport layout: taxiing distance \\
[57] & & & Regression—BP neural network & Surface traffic flow: number of arrival and departure aircraft during the average pre-departure taxi time at the same stand, surface congestion \\  & & & & Others: minimum taxi time at the same stand \\
[58] & & & Surface traffic flow: departure queue length, number of aircraft that \\
[59] & & & & Surface traffic flow: number of aircraft time, departure delay \\
[65] & & & & Airport layout: taxiing distance \\
[2021] & & ARIMA (differential autoregressive moving average)—SVR & & Others: planned takeoff time, actual pushback time \\  & & & & Surface traffic flow: number of aircraft taxiing on the same runway \\  & & & during taxiing out, the sum of departure and arrival aircraft using the same runway during taxiing out, the sum of the number of aircraft that \\  & & & were taxiing and the number of departure aircraft when the aircraft was pushed back from a stand, sum of aircraft traffic out when the aircraft was pushed back from a stand, the sum of aircraft pushed back from a stand during taxiing out and aircraft arriving on the same runway \\  & & & Surface traffic flow: number of aircraft pushed back from a stand at the same time, number of \\
[68] & & Support Vector Regression, neural network & Airport layout: taxiing distance, number of turns, number of corners \\  & & & Others: average taxi time in 1 h, time slots in which departure aircraft \\  & & & are at the takeoff time \\ \hline \hline \end{tabular}
\end{table}