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Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU.
- Source :
- Aerospace (MDPI Publishing); Apr2024, Vol. 11 Issue 4, p313, 21p
- Publication Year :
- 2024
-
Abstract
- With reference to the trajectory-based operation (TBO) requirements proposed by the International Civil Aviation Organization (ICAO), this paper concentrates on the study of four-dimensional trajectory (4D Trajectory) prediction technology in busy terminal airspace, proposing a data-driven 4D trajectory prediction model. Initially, we propose a Spatial Gap Fill (Spat Fill) method to reconstruct each aircraft's trajectory, resulting in a consistent time interval, noise-free, high-quality trajectory dataset. Subsequently, we design a hybrid neural network based on the seq2seq model, named Attention-TCN-GRU. This consists of an encoding section for extracting features from the data of historical trajectories, an attention module for obtaining the multilevel periodicity in the flight history trajectories, and a decoding section for recursively generating the predicted trajectory sequences, using the output of the coding part as the initial input. The proposed model can effectively capture long-term and short-term dependencies and repetitiveness between trajectories, enhancing the accuracy of 4D trajectory predictions. We utilize a real ADS-B trajectory dataset from the airspace of a busy terminal for validation. The experimental results indicate that the data-driven 4D trajectory prediction model introduced in this study achieves higher predictive accuracy, outperforming some of the current data-driven trajectory prediction methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- PREDICTION models
VIDEO coding
SHIFT registers
Subjects
Details
- Language :
- English
- ISSN :
- 22264310
- Volume :
- 11
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Aerospace (MDPI Publishing)
- Publication Type :
- Academic Journal
- Accession number :
- 176878825
- Full Text :
- https://doi.org/10.3390/aerospace11040313