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

Kim, Hyewook; Lee, Keumjin

doi:10.3390/aerospace8100301

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Both efforts face a challenging problem for ATM systems since trajectories might change substantially if unexpected events happen (e.g., weather-related events). In addition to this effort, the authors in [139] propose a framework for predicting air traffic situations as a sequence of images using an autoencoder with convolutional Long Short-Term Memory (ConvLSTM).…”

Section: Applications Of Autoencoders In Atmmentioning

confidence: 99%

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

et al. 2023

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Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic solutions have demonstrated the potential to solve complex problems in several domains, and Deep Learning (DL) has attracted attention due to its impressive results and disruptive capabilities. The adoption of DL models in ATM solutions enables new cognitive services that have never been considered before. The main goal of this research is to present a comprehensive review of state-of-the-art Deep Learning (DL) solutions for Air Traffic Management (ATM). This review focuses on describing applications, identifying opportunities, and highlighting open challenges to foster the evolution of ATM systems. To accomplish this, we discuss the fundamental topics of DL and ATM and categorize the contributions based on different approaches. First, works are grouped based on the DL approach adopted. Then, future directions are identified based on the ATM solution area. Finally, open challenges are listed for both DL applications and ATM solutions. This article aims to support the community by identifying research problems to be faced in the future.

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Section: Applications Of Autoencoders In Atmmentioning

confidence: 99%

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

et al. 2023

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Deep learning framework for forecasting en route airspace emissions considering temporal-spatial correlation

Wan,

Zhang,

Zhang

et al. 2023

Science of The Total Environment

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Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

et al. 2022

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Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing lookahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

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

Cited by 5 publications

References 15 publications

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

Deep learning framework for forecasting en route airspace emissions considering temporal-spatial correlation

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

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