Aircraft Trajectory Prediction Using Deep Long Short-Term Memory Networks

Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.

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Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121993471 ◽

2021 ◽

pp. 036119812199347

Author(s):

Lei Lin ◽

Siyuan Gong ◽

Srinivas Peeta ◽

Xia Wu

Keyword(s):

Traffic Flow ◽

Short Term Memory ◽

Road Traffic ◽

Autonomous Vehicle ◽

Trajectory Data ◽

Road Transportation ◽

Trajectory Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

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A model for vessel trajectory prediction based on long short-term memory neural network

Journal of Marine Engineering & Technology ◽

10.1080/20464177.2019.1665258 ◽

2019 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Huang Tang ◽

Yong Yin ◽

Helong Shen

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Trajectory Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Trajectory prediction of hypersonic glide vehicle based on empirical wavelet transform and attention convolutional long short-term memory network

IEEE Sensors Journal ◽

10.1109/jsen.2022.3143705 ◽

2022 ◽

pp. 1-1

Author(s):

Junbiao Zhang ◽

Jiajun Xiong ◽

Xuhui Lan ◽

Yanan Shen ◽

Xin Chen ◽

...

Keyword(s):

Wavelet Transform ◽

Short Term Memory ◽

Trajectory Prediction ◽

Short Term ◽

Term Memory ◽

Empirical Wavelet Transform ◽

Memory Network ◽

Long Short Term Memory

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Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example

Computational Geosciences ◽

10.1007/s10596-021-10037-2 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1005-1023

Author(s):

Wanting Qin ◽

Jun Tang ◽

Cong Lu ◽

Songyang Lao

Keyword(s):

Kalman Filter ◽

Moving Objects ◽

Short Term Memory ◽

Effective Means ◽

Prediction Algorithm ◽

Trajectory Data ◽

Trajectory Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

AbstractTrajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory prediction algorithm that improves the prediction of the simple LSTM is proposed, and the Kalman filter is used to filter the prediction results of the improved LSTM algorithm, which is called LSTM-KF. Through simulation experiments of Atlantic hurricane data from 1851 to 2016, compared to other LSTM and ILSTM algorithms, it is found that the LSTM-KF trajectory prediction algorithm has the lowest prediction error and the best prediction effect.

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