Arrival Pattern Recognition and Prediction Based on Machine Learning

基于机器学习的进场模式识别与预测

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.

为了给空中交通管制员提供更好的决策支持,提出了一种数据驱动的进场飞行模式识别与预测方法。对于进场飞行模式识别,提出了一种基于聚类的方法用于发现不同的管制意图。对于飞行模式预测,分别训练了两种预测器用于估计在特定的交通态势下,管制员将会发布的指令。其中对于进场模式的预测器可以当作是空中交通管制员模拟器,其可以像管制员一样为进场的航空器指定合适的进场模式。因此,该模拟器被认为可以为管制员提供有效的建议。最后,通过案例研究证明了基于卷积神经网络的预测器性能优于基于随机森林的预测器。