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Ground-based 4d trajectory prediction using bi-directional LSTM networks

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Abstract

Accurate aircraft trajectory prediction in the ground based system remains one of the most challenging prediction problems in air traffic management. In comparison to airborne systems, ground-based systems have less knowledge regarding aircraft performance characteristics and weather, making this task more difficult. In order to improve the trajectory prediction accuracy of the existing state-of-the-art methods, a Bi-directional Long-Short-Term-Memory (Bi-LSTM) neural network model is proposed in this paper. The multi-layered structure of Bi-LSTM is capable of understanding both forward and backward dependencies in the sequential trajectory data. In contrast to previous methods, we employed variable length flight trajectories without interpolation as input data. The sliding window approach effectively preserves adjacent trajectory point relationships. The experimental data set consists of historical ADS-B (Automatic Dependent Surveillance-Broadcast) trajectory data collected from Flight Radar 24. For comparison, the well-known uni-directional LSTM, CNN-LSTM, and Back Propagation Neural Network are used and evaluated on the same data set. The experimental findings indicate that our model outperforms the aforementioned state-of-the-art models. The Root Mean Square Error (RMSE) value of our model is improved by 28.44%, 14.84%, 45.45%, and 19.3% when compared to the second best model for time, latitude, longitude, and altitude respectively. It establishes a solid platform for decision-making in trajectory prediction research.

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Authors and Affiliations

  1. National Institute of Technology Tiruchirapalli, Tamilnadu, India

    Deepudev Sahadevan, Harikrishnan P M, Palanisamy Ponnusamy & Varun P Gopi

  2. Integrated Planning Group-ANS, Airports Authority Of India, NewDelhi, India

    Manjunath K Nelli

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  1. Deepudev Sahadevan
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  2. Harikrishnan P M
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  3. Palanisamy Ponnusamy
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  4. Varun P Gopi
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  5. Manjunath K Nelli
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Correspondence to Palanisamy Ponnusamy.

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Sahadevan, D., M, H.P., Ponnusamy, P. et al. Ground-based 4d trajectory prediction using bi-directional LSTM networks. Appl Intell 52, 16417–16434 (2022). https://doi.org/10.1007/s10489-022-03309-6

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