Data-Driven Method for the Prediction of Estimated Time of Arrival

Abstract

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

Prediction of Flight Delays at Beijing Capital International Airport Based on Ensemble Methods

“…Therefore, we incorporated factors including hour of the day, day of the week, month of the year, seasons, and holidays. In previous studies, timedependent data such as hour of the day were encoded by the one-hot method [25] as discrete features, characterized by numbers [18], or converted to a value between 0 and 1 [26]. However, studies have ignored the periodicity of these features.…”

Data‐driven flight time prediction for arrival aircraft within the terminal area

“…Ye et al [20] considered eight categories of factors that may affect arrival flight time, including airlines, aircraft, status, traffic demand, operation mode, weather conditions, airspace, and controllers. Gui et al [21] proposed a new multiple-stage strategy for arrival flight time prediction, including arrival pattern identification, classification, and flight time estimation. Dhief et al [22] analyze the feature engineering problem to predict Aircraft Landing Time (LDT) in Extended-TMA with machine learning models.…”

“…In such research domain, correctly distinguishing the arrival pattern plays an essential role. The other studies [14,15,17,20,21,24] were dedicated to comparing the different machine learning algorithms or emphasizing the impact of feature selection. For example, Ye et al [20] used machine learning methods to rank the importance of the features to improve the prediction accuracy.…”