Abstract
With the rapid development of aviation, the air traffic flow is increasing exponentially, leading to increasingly serious route congestion and route conflicts. Accurate prediction of aircraft trajectories is essential to ensure the safety of airspace. Trajectory data is a time-series sequence, and most of the existing methods for trajectory prediction are based on recurrent neural networks and attention mechanisms. However, these methods fail to effectively accumulate the correlation between the attributes of trajectory points and the time varying attribute features, and suffer from slow model training. In this paper, we propose STA-STCN, a novel track prediction model based on spatio-temporal dual attention (STA) and stacked temporal convolutional network (STCN). STA extracts the spatial and temporal features in parallel based on self-attention. The spatial feature characterizes the relationships between attributes of a track point, and the temporal feature represents the time varying characteristics of an attribute in track points. STA then fuses the extracted features to describe the accumulated time varying attributes correlation. STCN takes the fused features as the input to predict target track point attributes. We conduct experiments on the flight data provided by global flight data services website VariFight. Experimental results show that the model can reduce the root mean square error, mean absolute error, and track prediction model training time, indicating that model STA-STCN can achieve effective track prediction.
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References
Alligier, R., Gianazza, D., Durand, N.: Learning the aircraft mass and thrust to improve the ground-based trajectory prediction of climbing flights. Transp. Res. Part C: Emerg. Technol. 36, 45–60 (2013)
Shevchenko, A., Pavlov, B., Nachinkina, G.: Methods for predicting unsteady takeoff and landing trajectories of the aircraft. In: AIP Conference Proceedings, vol. 1798, no. 1, p. 020142. AIP Publishing LLC (2017)
De Marina, H.G., Pereda, F.J., Giron-Sierra, J.M., et al.: UAV attitude estimation using unscented Kalman filter and TRIAD. IEEE Trans. Industr. Electron. 59(11), 4465–4474 (2011)
Kada, B., Munawar, K., Shaikh, M.S., et al.: UAV attitude estimation using nonlinear filtering and low-cost mems sensors. IFAC-PapersOnLine 49(21), 521–528 (2016)
Jing, X., Cui, J., He, H., et al.: Attitude estimation for UAV using extended Kalman filter. In: 2017 29th Chinese Control and Decision Conference (CCDC), pp. 3307–3312. IEEE (2017)
Song, L., Shengli, W., Dingbao, X.: Radar track prediction method based on BP neural network. J. Eng. 2019(21), 8051–8055 (2019)
Ayhan, S., Samet, H.: Aircraft trajectory prediction made easy with predictive analytics. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 21–30 (2016)
Thipphavong, D.P., Schultz, C.A., Lee, A.G., et al.: Adaptive algorithm to improve trajectory prediction accuracy of climbing aircraft. J. Guid. Control. Dyn. 36(1), 15–24 (2013)
Madhavan, P.G.: Recurrent neural network for time series prediction. In: Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ, pp. 250–251. IEEE (1993)
Navarin, N., Vincenzi, B., Polato, M., et al.: LSTM networks for data-aware remaining time prediction of business process instances. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2017)
Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328. IEEE (2016)
Shi, Z., Xu, M., Pan, Q., et al.: LSTM-based flight trajectory prediction. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Yan, L., Chen, C., Hang, T., et al.: A stream prediction model based on attention-LSTM. Earth Sci. Inf. 14(2), 723–733 (2021)
Suman, S., et al.: Attention based CNN-LSTM network for pulmonary embolism prediction on chest computed tomography pulmonary angiograms. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 356–366. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_34
Qin, Y., Song, D., Cheng, H., et al.: A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2627–2633 (2017)
Liu, Y., Gong, C., Yang, L., et al.: DSTP-RNN: a dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction. Expert Syst. Appl. 143, 113082 (2020)
Fightadsb—VariFlight. https://flightadsb.variflight.com/track-data
Acknowledgments
This work is partially supported by The Young Elite Scientist Sponsorship Program by CAST (2020QNRC001).
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Wang, P., Zhang, J., Zhang, L., Jin, J., Fan, Y. (2022). Track Prediction Based on Spatio-Temporal Attention. In: Proceedings of 2022 10th China Conference on Command and Control. C2 2022. Lecture Notes in Electrical Engineering, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-19-6052-9_32
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DOI: https://doi.org/10.1007/978-981-19-6052-9_32
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