Air Traffic Prediction as a Video Prediction Problem Using Convolutional LSTM and Autoencoder

被引：5

作者：

Kim, Hyewook ^{[1
]}

Lee, Keumjin ^{[1
,2
]}

机构：

[1] Korea Aerosp Univ, Dept Air Transportat & Logist, Goyang Si 10540, Gyeonggi Do, South Korea

[2] Korea Aerosp Univ, Dept Smart Air Mobil, Goyang Si 10540, Gyeonggi Do, South Korea

来源：

AEROSPACE | 2021年 / 8卷 / 10期

关键词：

air traffic prediction; trajectory prediction; deep learning; video prediction; TRAJECTORY PREDICTION;

D O I：

10.3390/aerospace8100301

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Accurate prediction of future air traffic situations is an essential task in many applications in air traffic management. This paper presents a new framework for predicting air traffic situations as a sequence of images from a deep learning perspective. An autoencoder with convolutional long short-term memory (ConvLSTM) is used, and a mixed loss function technique is proposed to generate better air traffic images than those obtained by using conventional L-1 or L-2 loss function. The feasibility of the proposed approach is demonstrated with real air traffic data.

引用

页数：9

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