# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.1 Linear Regression Model
Building upon this foundation, [PERSON] et al. [42] in 2010 employed the number of arrival aircraft as an important predictor variable and characterized departure traffic using the number of departing aircraft rather than the number located on the surface. They established a multiple linear regression model to assess the factors affecting aircraft taxi-out time. Experimental results indicated a correlation between the number of arrival aircraft and taxi-out time, which grew stronger with increased interactions between departure and arrival aircraft on the airport surface. However, the correlation between the number of arrival aircraft and taxi-out time was found to be weaker than that between the departure queue and taxi-out time. By replacing the number of departing aircraft on the surface with departure traffic characteristics, the prediction accuracy of the model significantly improved. In 2016, [PERSON] et al. [49] further enhanced the FAA APO method by simultaneously considering both the number of aircraft in the departure queue and the number of arrival aircraft. They developed a multivariate linear regression model, which aimed to capture a more comprehensive understanding of the factors influencing taxi-out time. The selection of characteristic variables for studying taxi time encompasses a wide range of factors, from basic arrival and departure traffic characteristics such as departure queue size to more diverse elements such as runway configuration, turn angle, weather conditions, taxi route, and taxiing distance. In 2010, [PERSON] et al. [40] expanded the scope of characteristic variables by incorporating runway configuration patterns and expected takeoff time, in addition to the departure queue size factor. They developed a multivariate regression equation to better predict taxi time. In 2012, [PERSON] et al. [44] brought innovation to the field by introducing new characteristic variables such as route, carrier, destination and origin airports, congestion levels, and aircraft type. These variables offered a more comprehensive understanding of the factors influencing taxi time. In 2013, [PERSON] et al. [45] analyzed variations in taxi time from the perspective of aircraft taxiing speed. They classified the aircraft departure taxiing distance into three sections: the pushback section, straight-line taxiing section, and turn section. Furthermore, they introduced the concept of the turn angle characteristic for the first time. The turning angle, distance, and surface traffic volume of aircraft passing through these three sections were identified as factors affecting taxiing speed. To predict taxi time accurately, they proposed a prediction model that combined taxiway structural characteristics with historical taxi time information using multiple linear regression analysis. Actual data from experiments demonstrated a strong correlation between the number of arrival aircraft and taxi-out time, with higher correlation levels observed in runway operation modes where the arrival and departure runways crossed more frequently. To improve the accuracy of surface taxi time estimation, researchers have delved into comparing different surface taxi time prediction methods and have employed more complex functions to model the factors affecting taxi time. In 2009, a study by two scholars [38] explored the application of two functional forms: simple linear regression and logarithmic regression, to analyze the relationship between macro-traffic factors and taxi time. The test results revealed that the simple linear model performed better than the log-linear model, with differences staying within 1 min of the observed data. In 2021, [PERSON] et al. [61] conducted a comparative analysis of two prediction models; one based on traditional statistics and the other on Lasso regression, a machine learning approach. The results demonstrated the superiority of the machine learning approach, as it provided a higher model fit.