南京航空航天大学学报（英文版）2021，Vol.38Issue(6)：927-936,10.
基于机器学习的进场模式识别与预测
Arrival Pattern Recognition and Prediction Based on Machine Learning
摘要
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.关键词
空中交通管理/决策支持/进场调度/深度学习/卷积神经网络Key words
air traffic management/decision support/arrival scheduling/deep learning/convolutional neural networks分类
航空航天引用本文复制引用
归旭豪,张军峰,汤新民,康博..基于机器学习的进场模式识别与预测[J].南京航空航天大学学报（英文版）,2021,38(6):927-936,10.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(Nos.U1933117,61773202,52072174). （Nos.U1933117,61773202,52072174）
