4D Track Prediction Methods Based on Deep Learning
In recent years,with the booming development of the aviation industry,airspace congestion and low on-time flight rate caused by the large number of airports and the gradual density of domestic and foreign routes have also emerged.Therefore,in-telligent analysis of routes and timely effective prediction to improve airspace congestion have attracted wide attention.The 4D track prediction method based on deep learning is reviewed.Firstly,the 4D flight path prediction problem is summarized,including basic concepts,basic types,advantages and disadvantages,evaluation methods.Secondly,the existing 4D flight path prediction methods based on deep learning are divided into single model prediction and aggregation model prediction,and the modeling ideas,basic principles,advantages and disadvantages of each model are elaborated.Finally,three kinds of prediction methods based on aggrega-tion model are described,which are model aggregation method,cluster optimization method and self-generated network method.
万方数据
维普
