# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.6 Other Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Authors** & **Approach** & **Features considered in the Model** \\ \hline
[34] & [PERSON] et al. (1995) & Linear model & Surface operation rules: runway selection \\  & (1995) & Linear model & Surface traffic flow: surface traffic flow, arrival and departure \\  & & & capacity requirements \\  & & & Others: airline category \\
[39] & [PERSON] et al. (2010) & Intensive learning & Surface traffic flow: the number of aircraft, departure traffic \\  & (2010) & Intensive learning & flow, and departure queue size \\
[46] & [PERSON] et al. (2013) & Survival and vulnerability analysis & Others: average taxi time, time of day \\  & (2018) & Survival and vulnerability analysis & Surface traffic flow: blockage delays, departure/arrival delays, \\  & (2018) & Analysis analysis & Capacity utilization \\
[55] & [PERSON] et al. (2018) & Ordinary least square (OLS) & Surface traffic flow: number of aircraft ready to take off, takeoff requirements, available airport capacity \\  & (2018) & Ordinary least square (OLS) & Surface operation rules: runway configuration \\  & & & Others: approach conditions \\  & (2018) & Bayesian net & Surface traffic flow: flight density, flight delays, surface traffic \\
[58] & [PERSON] et al. (2018) & Bayesian net & conditions, traffic distribution \\  & (2018) & Sparse operation rules: aircraft stand groups \\  & & & Others: variable taxi times for departure flights \\  & & & Surface traffic flow: congestion level, saturation level, number \\  & (2018) & Decision tree & Surface operation rules: de-icing station, departure station, \\  & (2018) & Density-based & runaway heading \\  & & & Others: reference time, month, actual takeoff time, actual gear removal time \\  & & & Surface traffic flow: number of aircraft taxiing in the same time period, number of aircraft arrive and departure during the taxiing period \\
[3] & Qian (2019) & Gradient boosted tree & Airport layout: taxiing distance \\  & (2019) & Surface operation rules: runway number, terminal, aircraft stand number \\  & & & Others: scheduled takeoff time, airline, destination, registration \\  & & & number, aircraft type, average taxi time for the first 15 min \\  & & & Airport layout: taxiway length \\
[30] & Lin (2020) & Gradient boosting & Surface traffic flow: dynamic departure demand, simultaneous arrival and departure flights \\  & (2020) & Regression algorithm & Others: average taxi time of departure aircraft 15 min before \\  & & & Pubshock, aircraft type \\  & & & Airport layout: taxiing distance \\  & & & Surface traffic flow: congestion variables (number of aircraft \\  & (2021) & Decision tree & departing from the same runway and the instantaneous flow of \\  & (2021) & Decision tree & arrivals and departures) \\  & & & Others: average taxi time of departure aircraft 15 min before \\  & & & pushback, aircraft type \\
[31] & [PERSON] et al. (2022) & Gradient boosting & Surface traffic flow: surface traffic, arrival runways, \\  & (2022) & Surface operation rules: inter-area operations \\  & & & Others: operating hours, aircraft type, carrier attributes \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of representative literature on other methods. delays and high airport capacity utilization were more likely to contribute to excessive taxi times than other influencing factors. Through comparative experiments, [PERSON] et al. (2019) and [PERSON] et al. (2019) found that with different models, the same feature variable inputs may also lead to different perspectives of predicted time result outputs. [PERSON] et al. (2019) analyzed the ordinary least squares and ridge models and concluded that the ordinary least squares and ridge models performed better than the other integrated learning models in predicting the taxi-out time. [PERSON] et al.