scholarly journals Shape-based Trajectory Clustering

2017 ◽  
Author(s):  
Telmo J. P. Pires ◽  
Mário A. T. Figueiredo
Keyword(s):  
Trajectory Clustering

Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy

2017 ◽  
Vol 22 (5) ◽  
pp. 1433-1444 ◽  
Author(s):  
Huansheng Song ◽  
Xuan Wang ◽  
Cui Hua ◽  
Weixing Wang ◽  
Qi Guan ◽  
...  
Keyword(s):  
Trajectory Clustering ◽  
Vehicle Trajectory ◽  
3D Information ◽  
Coarse To Fine

scholarly journals Trajectory clustering for arrival aircraft via new trajectory representation

2021 ◽  
Vol 32 (2) ◽  
pp. 473-486
Author(s):  
Gui Xuhao ◽  
Zhang Junfeng ◽  
Peng Zihan
Keyword(s):  
Trajectory Clustering ◽  
Trajectory Representation

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

2021 ◽  
pp. 036119812110332
Author(s):  
Xuhao Gui ◽  
Junfeng Zhang ◽  
Zihan Peng ◽  
Chunwei Yang
Keyword(s):  
Pattern Classification ◽  
Trajectory Optimization ◽  
Time Estimation ◽  
Flight Time ◽  
Classification Model ◽  
Time Of Arrival ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Arrival Pattern ◽  
Multiple Stages

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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