# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 1 Introduction  
As an important part of airport scene guarantees, the flight taxiing process directly affects a series of work projects such as the construction of the airport collaborative decision-making system (ACDM) and the airport apron handover. The taxi-out time of a flight is the difference between its actual out of block time (AOBT) and its actual take-off time (ATOT). The corresponding taxi-in time is the difference between the actual landing time (ALDT) and the actual in-block time (AIBT) of the flight. Currently, the off-block time for a flight under the domestic ACDM mechanism is obtained by subtracting the estimated taxi-out time from the calculated take-off time (CTOT). However, this estimated taxi-out time is a fixed value that does not take into account factors such as flow of the scene, stands, and runways. This can lead to large deviations between estimated taxi-out times and actual values, affecting the apron rollout sequence as well as the runway queuing sequence. Therefore, accurately predicting flight taxi-out times is beneficial to efficiently scheduling flight departure sequences, shortening waiting time on the ground, reducing flight delays, and increasing passenger satisfaction. Taxi-out time is divided into unimpeded taxi-out time (UTT) and additional taxi-out time (ATT), as shown in Figure 1. UTT refers to the taxi time of a flight under optimaloperating conditions, e.g., when congestion, convective weather, or other delay causes are not significant [1]. And the ATT is equal to the difference between the total taxi-out time and the UTT, which is an important metric to monitor the taxi performance of an airport. When optimizing taxi times, the goal is to minimize the ATT and fuel consumption, not to reduce it to unimpeded time, which could negatively impact runway throughput. Studying the taxi-out time by dividing it into UTT and ATT not only helps to understand the trend of these two types of time, but also provides a new way of thought for taxi-out time prediction. However, existing studies on taxi time prediction have viewed it as a whole. Moreover, existing research on the factors influencing departure taxi time has only considered macro-level flow and lacks studies on the special structural flow of the airport scene. In terms of prediction methods for departure taxi time, common machine learning algorithms have been utilized without further optimization of their parameters. Additionally, there is a lack of detailed grouping, zonal analysis validation experiments, and ablation experiments in the empirical validation, which limits our understanding of the predictive capabilities for taxi times under different scenarios and conditions. Therefore, it is necessary to conduct more in-depth research to explore the diverse factors affecting departure taxi time, optimize the prediction models, and implement more refined experimental designs to enhance the accuracy and applicability of the predictions. To address the limitations of the existing works, we use a regression model with the highest correlation between departure and arrival flows to calculate the UTT to obtain the ATT, which will be used as the true value in this paper. We then propose three new features related to the structure of the airport for the ATT prediction: corridor departure flow, corridor arrival flow, and departure flow proportion of the ODP, which serve as the basis for constructing the ATT prediction feature set and dataset. Combining the advantages of heuristic algorithms and machine learning algorithms, we propose an ATT prediction model, NOA-XGBoost, in which the NOA algorithm is used to optimize the XGBoost model. Finally, we conduct experiments on the operation data of Shanghai Pudong International Airport to verify the effectiveness of the proposed features and the prediction model.