# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.2 Neural Network
Consequently, using a single prediction method with one model may fail to capture the different behavioral characteristics present in the actual data. To overcome the limitations of a single model and harness the strengths of various models, a combined model approach can be employed to enhance prediction capabilities. In 2018, [PERSON] [5] introduced a Support Vector Regression-BP neural network-based taxi time prediction model. This combined model utilized a feature vector set consisting of variables such as the number of arrival and departure aircraft during the average pre-takeoff taxi time of the same aircraft stand, taxiing distance, minimum taxi time of the same aircraft stand, and the degree of surface congestion. The model applied Support Vector Regression (SVR) to uncover linear relation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Authors** & **Approach** & **Features Considered in the Model** \\ \hline \multirow{4}{*}{[43]} & \multirow{4}{*}{[PERSON] et al. (2012)} & \multirow{4}{*}{Neural network} & \multicolumn{2}{c}{Airport layout: gate to runway distance} \\  & & & Surface traffic flow: average taxi-out delay in the previous 15 min, \\  & & & number of departure aircraft on the surface during departure taxiing, \\  & & & average taxi-out delay on the same runway in the previous 15 min, \\  & & & average taxi-out delay to the same fix in the previous 15 min \\  & & & Others: wind angle, airport arrival rate set by ATC \\  & & & Airport layout: taxiing distance \\
[5] & Yin & Support Vector Regression—BP neural networks & 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 \\
[59] & [PERSON] et al. (2019) & Gradient-boosted trees and neural networks & Surface traffic flow: congestion, delay status \\  & & & Others: weather, time of day \\  & & & Surface traffic flow: airport capacity, number of taxiing aircraft, air traffic control \\
[60] & [PERSON] et al. (2020) & Spatio-Temporal Environment Deep Learning (STEDL) & Airport layout: taxiing distance \\  & & & Surface operation rules: runway configurations \\  & & & Others: different time periods, weather, aircraft type \\  & & & Airport layout: the taxiing distance of departure aircraft \\
[67] & [PERSON] et al. (2022) & Neural network & Surface traffic flow: Number of departure aircraft taxiing at the same time, number of departure aircraft pushed back from a stand at the same time, average taxi time in half-hour time slices, average taxi time in 1 h time slices \\  & & & Surface traffic flow: departure instantaneous traffic index, arrival \\
[33] & [PERSON] (2022) & Neural network & surface traffic flow: departure aircraft queue length, departure slot \\  & & & resource requirement index, departure cumulative traffic index \\  & & & Others: flight arrival-to-departure ratio, visibility, cloud base height, \\  & & & temperature, departure point, wind direction, wind speed \\  & & & Surface traffic flow: number of aircraft pushed back from a stand at the same time, number of same time, number of aircraft taking off at the same time, number of arrivals at the same time, the average taxi time within 1 h, delays \\
[68] & [PERSON] (2022) & Support Vector Regression, neural network & Airport layout: taxiing distance, number of turns, number of corners \\  & & & Others: time slots where departure aircraft are taking off \\
[69] & [PERSON] et al. (2022) & Improved BP neural network based on SSA & Surface traffic flow: number of departure aircraft taxiing at the same time, number of arrival aircraft taking at the same time, number of departure aircraft pushed back from a stand at the same time, half-hourly average taxi-out time \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of representative literature on neural networks. ships within the feature vector set, while using the BP neural network to explore nonlinear relationships.