# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 2 Literature Review
### Research Gap  
We summarize the existing studies in terms of features, research airports, and prediction methods, as shown in Table 1. From Table 1, we can obtain the following conclusions:  
1. The existing studies consider taxi time as a whole [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], while not analyzing UTT and ATT separately. 2. The existing studies consider the flow of the scene as a whole [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], without focusing on the flow associated with the microstructure of the scene, such as the flow of the corridor. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Features** & **Airport** & **Prediction Method** \\ \hline [PERSON] et al. (2010) (2010) & Scene traffic flow & Tampa International Airport & Reinforcement learning \\ \hline [PERSON] et al. (2011) & Taxi distance, number of flights, & & \\  & average taxi time of the & & \\  & previous quarter and weather & & \\ \hline [PERSON] et al. (2011) (2014) & Taxi distance, taxi turning angle, & & Multiple linear regression, \\  & departure or arrival and & & Zurich Airport & least median squared linear \\  & number of flights & & \\  & & & \\  & & & \\  & & & \\ \hline [PERSON] et al. (2012) (2016) & Gate, runway, departure or & & \\  & arrival, and number of flights & Barcelona-El Prat Airport & Log-linear regression \\ \hline [PERSON] et al. (2018) (2018) & Scene traffic flow, runway & Seattle-Tacoma International & RL, RR, LR, ER, SVR, RF, AR, \\  & configuration, weather, & Airport & BR, ETR, GBRT \\  & and delay & & \\ \hline [PERSON] et al. (2020) & Weather, air traffic flow control, & & \\  & runway configuration, and & \\  & aircraft category & & \\ \hline [PERSON] et al. (2020) & Scene traffic flow, taxi routes, & & \\  & departure waiting queues, & & \\  & and airlines & & \\ \hline [PERSON] et al. (2019) (2019) & Gate, runway, day-of-week, & & \\  & hour, aircraft type, weather, and & Singapore Changi Airport & RF, LR \\  & number of flights & & \\ \hline [PERSON] et al. (2021) & Airport operational information, & Manchester Airport, Zurich & Random forest, gradient \\  & congestion, average speed, & Airport and Hong Kong & boosting regression trees, \\  & and weather & International Airport & \\  & & \\  & & \\ \hline  & Airline, aircraft type, runway, & & \\  & parking period, number of flight & & \\  & taxiing at the same time on the & & \\  & field, severe weather, length of & & \\  & takeoff queue, cross-taxing or & & \\  & not, and number of passes & & \\  & through HS & & \\ \hline [PERSON] et al. (2022) & Scene traffic, taxiing distance, & & \\  & number of turns, delays, and & & \\  & time of day at takeoff time & & \\ \hline [PERSON] et al. (2022) & Surface traffic flow, departure & & \\  & runway, operating load, taxi & & \\  & distance, air route control, and & & \\  & flight properties & & \\ \hline [PERSON] et al. (2022) & Flight properties, surface traffic, & & \\  & and meteorological conditions & & \\ \hline [PERSON] et al. (2024) (2024) & Congestion level, unimpeded & & \\  & taxi-out time, saturation level, & & \\  & and aircraft type & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of research on taxi time prediction. In order to fix these problems, we study the UTT and ATT separately and apply NOA to optimize the XGBoost model for taxi time prediction. The gaps between our study and existing works are as follows:  
1. Unlike existing studies that examine taxi-out time as a whole, we separate taxi-out time into UTT and ATT, which allows us to provide detailed analyses and give more accurate predictions. 2. Compared with existing studies, we focus on the flow of special structural corridors, in addition to the overall flow of the scene, such as the number of departure flights on the scene and the number of arrival flights on the scene. Combining the micro-flow with the macro-flow enables us to capture the impact of the scene flow factors in a more comprehensive way. 3.