# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.6 Other Methods  
Some other advanced machine learning methods have also shown excellent performance in taxi time prediction. As can be seen from Table 8, the research methods have gradually evolved from linear models in the early years to machine learning methods such as reinforcement learning and decision trees. In 1995, [PERSON] et al. [34] first proposed the correlation between different influencing factors and taxi-out time, taking the categories of airlines, surface traffic flow, runway selection, and arrival and departure capacity demand as the characteristic variables, of which surface traffic flow and the number of departure aircraft are the most critical influencing factors, based on which dynamic and static linear models were established to predict the taxi time, respectively. The results show that the dynamic linear model has better prediction performance. In order to better realize the dynamic estimation of taxi time, in 2018, [PERSON] et al. [58] established a dynamic estimation model of the taxi time of departure aircraft based on Bayesian nets by real-time processing of new data. The experimental results show that compared with the static Bayesian net model, the dynamic estimation model has higher accuracy, and the change rule of the taxi-out time can be derived from the analysis of the changes in the indicators. \begin{table}
\begin{tabular}{c c c c} \hline
**Literature** & **Authors** & **Approach** & **Features Considered in the Model** \\ \hline \multirow{3}{*}{[47]} & [PERSON] et al. (2014) & TSK fuzzy rules & Airport layout: taxiing distance, taxiing turn angle \\  & (2014) & & Others: departure or arrival, flight number characteristics, and some \\  & & & less important factors \\  & & & Surface traffic flow: number of aircraft stopped during taxiing, total \\  & & & number of taxiing aircraft, \\
[51] & [PERSON] et al. (2017) & Type-2 fuzzy logic system & Airport layout: taxiing distance \\  & & & Others: arrivals/departures, large aircraft/small aircraft \\  & & & Surface operation rules: mode of operation at the airport when the \\  & & & aircraft starts taxiing \\  & & & Surface operation rules: mode of airport operation (one/two runways \\  & & & Airport layout: total taxiing distance and its logarithmic transformation, \\  & & distance on a straight-line segment, total angle of turn along the route \\
[56] & [PERSON] & The multi-objective fuzzy rule & and its logarithmic transformation \\
[2017] & system & Surface traffic flow: the number of aircraft taxiing on the surface at the \\  & & time of commencement of taxiing by an aircraft, and the number of \\  & & aircraft stopping to taxi while an aircraft is taxiing on the surface \\  & & Others: type of movement (arrival/departure), whether or not a \\  & & pushback maneuver has been performed \\ \hline \end{tabular}
\end{table} 
Table 7: Summary of representative literature on fuzzy rule systems. In 2010, [PERSON] et al. [39] proposed a method that is suitable for the stochastic nature of departure operations, using a nonparametric reinforcement learning algorithm to predict the trend of the average taxi-out time 30-60 min before takeoff. Compared to the traditional parametric regression-based approach, this method has better robustness against stochastic factors during aircraft departure operations. Since the factors affecting taxi time may not be expressed in parametric and semiparametric models, in 2013, [PERSON] et al. [46] developed a survival and vulnerability analysis model to analyze the relevant factors related to excessive taxi time. The results showed that two factors: obstruction