# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 3 Prediction of Additional Taxi-Out Time
### Prediction of ATT  
#### 3.3.1 Construction of Feature Set  
For the better prediction of the ATT, we propose three new features related to the structure of the airport: corridor departure flow, corridor arrival flow, and departure flow proportion of ODP. 1. [label=(0),leftmargin=*]
2. Corridor flow  
As shown in Figure 2, Shanghai Pudong Airport features a unique structure known as the corridor, which is prone to traffic congestion and contributes to an increased ATT. We analyze the trend of the ATT with changes in corridor departure flow and corridor arrival flow, as shown in Figure 6. Corridor departure flow refers to the number of departing flights that taxi from the stand groups in the east (west) control zone through corridor TW3 (TW4) to the departure runways in the west (east) control zone during the taxiing period of the target flight. In contrast, corridor arrival flow refers to the number of arriving flights that land on the arrival runways in the east (west) control zone and then taxi through corridor TW3 (TW4) to the stand groups in the west (east) control zone during the taxiing period of the target flight. As shown in Figure 6, with the increase in corridor departure flow and corridor arrival flow, the average ATT also rises. When the corridor departure flow and corridor arrival flow values are high, there is some fluctuation in the ATT, but the overall trend remains positively correlated. The main reason is that the number of flights that can pass through the corridor is limited, which means that during peak periods, the flight flow can easily exceed the corridor's capacity. When the corridor flow increases, congestion may occur during the taxiing process, resulting in an increase in the additional taxi-out time for flights. Figure 6: Effect of corridor flow on taxi-out time. (**a**) Effect of corridor departure flow; (**b**) effect of corridor arrival flow. When the corridor flow values are too high and the scene is in a busy state with complex and intersecting traffic flow information, the ATT becomes more variable, increasing the difficulty of prediction. (2) Departure Flow Proportion of ODP  
This feature indicates the proportion of the departure flow to the total of both departure and arrival flow, as discussed in Section 4. We analyze the trend of the ATT with the proportion of departure flow, as shown in Figure 7, with a Pearson correlation coefficient of 0.49. From the figure, it can be seen that the majority of flights have a low additional taxi-out time, while only a few flights have a higher additional taxi-out time, with their departure flow proportion concentrated between 0.4 and 0.6. This is because when the departure flow proportion is between 0.4 and 0.6, the number of departing flights is close to that of arriving flights, creating a higher mixed state that easily leads to conflicts between flights. Consequently, the taxiing speed is reduced, and the additional taxi-out time increases. In other scenarios, when the departure flow proportion is above 0.6 or below 0.4, the imbalance in the number of departing or arriving flights leads to fewer conflicts, resulting in a relatively faster taxiing speed and, thus, a lower additional taxi-out time. In addition to the three new features mentioned above, we also use 11 additional features, which are shown in Table 3. In Table 3, aircrafts are classified into four categories: C, D, E, and F, primarily based on the standards set forth in the International Civil Aviation Organization (ICAO) Annex to the Convention on International Civil Aviation. Restricted status refers to whether a flight is subject to route traffic restrictions or control area traffic restrictions. Airline, aircraft, and restricted status are encoded using one-hot encoding, where valid feature values are set to 1 and the rest are set to 0.