# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.5 Fuzzy Rule System  
Currently, the majority of taxi time predictions focus on representing the dynamics and complexity of taxi time. Among machine learning methods, the fuzzy rule system has shown higher accuracy in taxi time prediction compared to statistical methods and other machine learning approaches. Table 7 summarize some of the studies applying fuzzy \begin{table}
\begin{tabular}{c c c c} \hline
**Literature** & **Authors** & **Approach** & **Features Considered in the Model** \\ \hline
[54] & [PERSON] et al. (2018) & Linear models, support vector machines, random forest & 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) \\
[55] & [PERSON] (2018) & Ensemble Machine Learning, Least Squares, Penalty Algorithms & Surface traffic flow: number of aircraft ready to take off, takeoff requirements, available airport capacity \\
[6] & [PERSON] et al. (2021) & Random forest & Surface operation rules: runway configurations \\
[20] & [PERSON] et al. (2021) & Random forest & Surface traffic flow: number of surface operation periods, number of arrival and departure aircraft \\
[20] & [PERSON] et al. (2021) & Random forest & Airport layout: taxiing distance \\
[64] & [PERSON] et al. (2021) & Random forest & Others: minimum taxi times for the same stand \\
[20] & [PERSON] et al. (2021) & Random forest & Surface traffic flow: characterization of aircraft congestion \\
[20] & [PERSON] et al. (2021) & Others: characterization of average aircraft speed, characterization of weather information \\
[72] & [PERSON] et al. (2021) & Random forest & Surface traffic flow: traffic density mapSurface operation rules: runway configurations: weather features, aircraft type, estimated taxi \\
[20] & [PERSON] et al. (2021) & Surface traffic flow: traffic flow related variables (surface departure instantaneous flow/surface departure cumulative flow/taskoff time gap demand index/departure queuing index/entry queuing index) \\
[20] & [PERSON] et al. (2022) & Random forest & Others: pushback time related variables (pushback month/pushback period), surface flight related variables (flight number/aircraft type/stand) \\  & & & Surface traffic flow: number of aircraft taxiing at the same time on the surface, length of departure queue \\
[32] & [PERSON] et al. (2022) & XGBoost algorithm & Surface operation rules: runway number, stand \\
[32] & [PERSON] et al. (2022) & XGBoost algorithm & Others: the airline, type of aircraft, time period, inclement weather, whether or not crossing the runway, whether or not cross-taxing, number of passes through the HS \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of representative literature on integrated learning. rule system in taxi time prediction. In 2014, [PERSON] et al. [47] compared and analyzed the results of multivariate linear regression, least median squared linear regression, Support Vector Regression, M5 model tree, and TSK fuzzy model, and the results showed that the TSK fuzzy rule system model outperforms the other models in terms of prediction accuracy. In 2017, [PERSON] et al. [51] utilized a type-2 fuzzy logic system to establish a taxi time prediction model with the innovative introduction of speech information and, compared to the traditional one-layer fuzzy system, the method improves the taxi time prediction accuracy and generalization ability, with stronger robustness and accuracy. Subsequently, [PERSON] [56] improved the previous work; after mathematically processing the influencing factors of taxi time delays, a multi-objective fuzzy rule-based system was added to the uncertainty factors in the aircraft taxiing process in the historical data in order to reduce the delays and conflicts in the taxiing process, which in turn made the taxi time prediction more accurate and resilient. For the uncertainty of taxi time, in 2018, [PERSON] et al.