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.


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    Title :

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


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Gui, Xuhao (author) / Zhang, Junfeng (author) / Peng, Zihan (author) / Yang, Chunwei (author)


    Publication date :

    2021-09-09




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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