Transactions of Nanjing University of Aeronautics & Astronautics
Arrival Pattern Recognition and Prediction Based on Machine Learning
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Abstract:
A data-driven method for arrival pattern recognition and prediction is proposed to provide air traffic controllers （ATCOs） with decision support. For arrival pattern recognition， a clustering-based method is proposed to cluster arrival patterns by control intentions. For arrival pattern prediction， two predictors are trained to estimate the most possible command issued by the ATCOs in a particular traffic situation. Training the arrival pattern predictor could be regarded as building an ATCOs simulator. The simulator can assign an appropriate arrival pattern for each arrival aircraft， just like real ATCOs do. Therefore， the simulator is considered to be able to provide effective advice for part of the work of ATCOs. Finally， a case study is carried out and demonstrates that the convolutional neural network （CNN）-based predictor performs better than the radom forest （RF）-based one.
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Project Supported:
This work was supported by the National Natural Science Foundation of China （Nos. U1933117， 61773202， 52072174）.
Clc Number:
V355
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