# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 2 Literature Review
### Related Work
#### 2.1.2 Research on Taxi Time Prediction  
Research on taxi time prediction can be divided into two aspects: features and methods. In the term of features, an increasing number of features are being applied to taxi-out time prediction. [PERSON] et al. [4] proposed a flight taxi time prediction model based on a historical traffic flow database generated from ASDE-X data, considering the number of departure and arrival flights as well as the average departure taxi time of the previous quarter. [PERSON] et al. [5] used multiple linear regression equations to establish an explanatory model for flight taxi-out time, and the experimental results showed that the number of arriving and departing flights, the operating time period, and the departure taxi-out time had a significant correlation. [PERSON] et al. [6] investigated the generalizability of the factors affecting the taxi-out time by selecting a total of three typical airports in Europe and Asia, which are Manchester Airport, Zurich Airport, and Hong Kong International Airport. They used the backward feature elimination algorithm to identify important features affecting taxi-out time, and they applied the random forest (RF) algorithm for prediction. The results indicated that using just a subset of features, such as departure/arrival, distance, total turns, average speed, and number of recent flights, can achieve high prediction accuracy. [PERSON] et al. [7] proposed three new features for the scene operation environment of Guangzhou Baiyun Airport, which are the number of passing hot spots (HSs), whether they are cross taxiing, and whether they are crossing the runway. [PERSON] et al. [8] analyzed the factors affecting taxi-out time prediction at Beijing Capital International Airport by using methods such as feature importance ranking, univariate analysis, and recursive feature elimination. They found that the number of concurrent flights and runway operation modes had a significant impact on the prediction results, while basic features of the flight had a smaller effect. [PERSON] et al. [9] studied in detail the effect of weather conditions (wind direction, wind speed, visibility, barometric pressure, and precipitation) on taxi times. The results of thestudy showed that wind speed, barometric pressure, and precipitation have a significant effect on the taxi-out and taxi-in times of flights. In terms of methods, machine learning techniques are widely used for taxi time prediction, including simple linear regression, neural networks, and decision trees. [PERSON] et al. [10] proposed a nonparametric reinforcement learning method to predict and analyze taxi time at Tampa International Airport and the results show that the method achieves 81% prediction accuracy for individual flights within a margin of \(\pm\)2. [PERSON] et al. [11] proposed a taxi time prediction method based on the fuzzy rule system, which was verified to have better performance than multiple linear regression, least median squared linear regression, support vector regression, and M5 model trees on the data of Stockholm Arlanda Airport and Zurich Airport. [PERSON] et al. [12] used log-linear regression analysis to predict flight taxi-out times and obtained better results. [PERSON] et al. [13] explored linear regression (RL), ridge regression (RR), lasso regression (LR), elasticNet regression (ER), support vector regression (SVR), random forest (RF), adaboost regression (AR), bagging regression (BR), extra trees regression (ETR), and gradient boosting regression tree (GBRT) models for predicting taxi-out time under different datasets. The results show that the ordinary least-squares and ridge models predict relatively well, but no algorithm is suitable for all types of data. [PERSON] et al.