Flight trajectory prediction enabled by time-frequency wavelet transform.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
29 Aug 2023
29 Aug 2023
Historique:
received:
20
04
2023
accepted:
14
08
2023
medline:
30
8
2023
pubmed:
30
8
2023
entrez:
29
8
2023
Statut:
epublish
Résumé
Accurate flight trajectory prediction is a crucial and challenging task in air traffic control, especially for maneuver operations. Modern data-driven methods are typically formulated as a time series forecasting task and fail to retain high accuracy. Meantime, as the primary modeling method for time series forecasting, frequency-domain analysis is underutilized in the flight trajectory prediction task. In this work, an innovative wavelet transform-based framework is proposed to perform time-frequency analysis of flight patterns to support trajectory forecasting. An encoder-decoder neural architecture is developed to estimate wavelet components, focusing on the effective modeling of global flight trends and local motion details. A real-world dataset is constructed to validate the proposed approach, and the experimental results demonstrate that the proposed framework exhibits higher accuracy than other comparative baselines, obtaining improved prediction performance in terms of four measurements, especially in the climb and descent phase with maneuver control. Most importantly, the time-frequency analysis is confirmed to be effective to achieve the flight trajectory prediction task.
Identifiants
pubmed: 37644022
doi: 10.1038/s41467-023-40903-9
pii: 10.1038/s41467-023-40903-9
pmc: PMC10465572
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5258Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 62001315
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : U20A20161
Informations de copyright
© 2023. Springer Nature Limited.
Références
Gui, G. et al. Flight delay prediction based on aviation big data and machine learning. IEEE Trans. Veh. Technol. 69, 140–150 (2020).
Kim, Y. J., Choi, S., Briceno, S. & Mavris, D. A Deep Learning Approach to Flight Delay Prediction, 1–6 (IEEE, Sacramento, 2016).
Huang, C. & Cheng, X. Estimation of aircraft fuel consumption by modeling flight data from avionics systems. J. Air Transp. Manag. 99, 102181 (2022).
Zixuan, W., Ning, Z., Weijun, H. & Sheng, Y. Study on Prediction Method of Flight Fuel Consumption with Machine Learning, 624–627 (IEEE, Chongqing, 2020).
Wu, X., Yang, H., Chen, H., Hu, Q. & Hu, H. Long-term 4D trajectory prediction using generative adversarial networks. Transp. Res. Part C Emerg. Technol. 136, 103554 (2022).
Chen, Z., Guo, D. & Lin, Y. A deep gaussian process-based flight trajectory prediction approach and its application on conflict detection. Algorithms 13, 293 (2020).
Brooker, P. SESAR and NextGen: investing in new paradigms. J. Navig. 61, 195–208 (2008).
Strohmeier, M., Schafer, M., Lenders, V. & Martinovic, I. Realities and challenges of nextgen air traffic management: the case of ADS-B. IEEE Commun. Mag. 52, 111–118 (2014).
Nagaoka, S. & Brown, M. A review of safety indices for trajectory-based operations in air traffic management. Trans. Jpn Soc. Aeronaut. Space Sci. Aerosp. Technol. Jpn 12, a43–a49 (2014).
Zeng, W., Chu, X., Xu, Z., Liu, Y. & Quan, Z. Aircraft 4D trajectory prediction in civil aviation: a review. Aerospace 9, 91 (2022).
Wang, Z., Liang, M. & Delahaye, D. A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area. Transp. Res. Part C 95, 280–294 (2018).
Zhang, M., Chen, S., Sun, L., Du, W. & Cao, X. Characterizing flight delay profiles with a tensor factorization framework. Engineering 7, 465–472 (2021).
Zhang, Y., Zhang, M. & Yu, J. Real-time flight conflict detection and release based on Multi-Agent system. IOP Conf. Ser. Earth Environ. Sci. 108, 032053 (2018).
Jiao, W., Yao, j & Wang, R. Flight conflict detection algorithm based on convex bounding box. China Saf. Sci. J. 31, 32–38 (2021).
Lin, Y., Zhang, J.-w & Liu, H. Deep learning based short-term air traffic flow prediction considering temporal–spatial correlation. Aerosp. Sci. Technol. 93, 105113 (2019).
Liu, H. et al. Research on the air traffic flow prediction using a deep learning approach. IEEE Access 7, 148019–148030 (2019).
Yan, Z., Yang, H., Wu, Y. & Lin, Y. A multi-view attention-based spatial–temporal network for airport arrival flow prediction. Transp. Res. Part E 170, 102997 (2023).
Guan, X. et al. A strategic flight conflict avoidance approach based on a memetic algorithm. Chin. J. Aeronaut. 27, 93–101 (2014).
Lee, J., Lee, S. & Hwang, I. Hybrid system modeling and estimation for arrival time prediction in terminal airspace. J. Guid. Control Dyn. 39, 903–910 (2016).
Thipphavong, D. P., Schultz, C. A., Lee, A. G. & Chan, S. H. Adaptive algorithm to improve trajectory prediction accuracy of climbing aircraft. J. Guid. Control Dyn. 36, 15–24 (2013).
Fukuda, Y., Shirakawa, M. & Senoguchi, A. Development and Evaluation of Trajectory Prediction Model, 1–8 (ICAS, Nice, 2010).
Zhang, J., Liu, J., Hu, R. & Zhu, H. Online four dimensional trajectory prediction method based on aircraft intent updating. Aerosp. Sci. Technol. 77, 774–787 (2018).
Soler, M., Olivares, A. & Staffetti, E. Multiphase optimal control framework for commercial aircraft four-dimensional flight-planning problems. J. Aircraft 52, 274–286 (2015).
Wang, T. 4d flight trajectory prediction model based on improved kalman filter. J. Comput. Appl. 34, 1812 (2014).
Lymperopoulos, I. & Lygeros, J. Sequential monte carlo methods for multi-aircraft trajectory prediction in air traffic management. Int. J. Adapt. Control Signal Process. 24, 830–849 (2010).
Yepes, J. L., Hwang, I. & Rotea, M. New algorithms for aircraft intent inference and trajectory prediction. J. Guid. Control Dyn. 30, 370–382 (2007).
Dalmau, R., Perez-Batlle, M. & Prats, X. Real-time Identification of Guidance Modes in Aircraft Descents Using Surveillace Data, 1–10 (IEEE, London, 2018).
Lovera Yepes, J., Hwang, I. & Rotea, M. An Intent-based Trajectory Prediction Algorithm for Air Traffic Control, 5824 (AIAA, San Francisco, 2005).
Choi, H.-C., Deng, C. & Hwang, I. Hybrid machine learning and estimation-based flight trajectory prediction in terminal airspace. IEEE Access 9, 151186–151197 (2021).
Tastambekov, K., Puechmorel, S., Delahaye, D. & Rabut, C. Aircraft trajectory forecasting using local functional regression in sobolev space. Transp. Res. Part C 39, 1–22 (2014).
Alligier, R. & Gianazza, D. Learning aircraft operational factors to improve aircraft climb prediction: a large scale multi-airport study. Transp. Res. Part C 96, 72–95 (2018).
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 30 (NIPS, 2017).
Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. 1-21 (OpenReview.net, Vienna, 2021).
Lin, Y., Guo, D., Zhang, J., Chen, Z. & Yang, B. A unified framework for multilingual speech recognition in air traffic control systems. IEEE Trans. Neural Netw. Learn. Syst. 32, 3608–3620 (2020).
Wu, H., Xu, J., Wang, J. & Long, M. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Advan. Neural Inf. Process. Syst. 34, 22419–22430 (2021).
Lin, Y., Guo, D., Zhang, J., Chen, Z. & Yang, B. A unified framework for multilingual speech recognition in air traffic control systems. IEEE Trans. Neural Netw. Learn. Syst. 32, 3608–3620 (2021).
pubmed: 32833649
Lin, Y. et al. A real-time atc safety monitoring framework using a deep learning approach. IEEE Trans. Intell. Transp. Syst. 21, 4572–4581 (2020).
Lin, Y., Li, L., Jing, H., Ran, B. & Sun, D. Automated traffic incident detection with a smaller dataset based on generative adversarial networks. Accid. Anal. Prev. 144, 105628 (2020).
pubmed: 32570087
Lin, Y. et al. A deep learning framework of autonomous pilot agent for air traffic controller training. IEEE Trans. Hum. Mach. Syst. 51, 442–450 (2021).
Pang, Y., Zhao, X., Yan, H. & Liu, Y. Data-driven trajectory prediction with weather uncertainties: a bayesian deep learning approach. Transp. Res. Part C 130, 103326 (2021).
Guo, D. et al. FlightBERT: binary encoding representation for flight trajectory prediction. IEEE Trans. Intell. Transp. Syst. 24, 1828–1842 (2022).
Pang, Y., Zhao, X., Hu, J., Yan, H. & Liu, Y. Bayesian spatio-temporal graph transformer network (b-star) for multi-aircraft trajectory prediction. Knowl. Based Syst. 249, 108998 (2022).
Shi, Z., Xu, M., Pan, Q., Yan, B. & Zhang, H. LSTM-based flight trajectory prediction. In International Joint Conference on Neural Networks 8 (IEEE, 2018).
Ma, L. & Tian, S. A hybrid CNN-LSTM model for aircraft 4D trajectory prediction. IEEE Access 8, 134668–134680 (2020).
Shafienya, H. & Regan, A. C. 4d flight trajectory prediction using a hybrid deep learning prediction method based on ads-b technology: A case study of hartsfield–jackson atlanta international airport (atl). Transp. Res. Part C 144, 103878 (2022).
Han, P. A combined online-learning model with K-means clustering and GRU neural networks for trajectory prediction. Ad Hoc Netw. 117, 102476 (2021).
Zhao, Y., Shen, Y., Zhu, Y. & Yao, J. Forecasting Wavelet Transformed Time Series with Attentive Neural Networks, 1452–1457 (IEEE, Singapore, 2018).
Wang, J., Wang, Z., Li, J. & Wu, J. Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis, 2437–2446 (ACM, London, 2018).
Zhou, T. et al. FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. 27268 (PMLR, Baltimore, 2022).
Stéphane, M. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (eds Wallach, H. et al.) 32, 8024–8035 (Curran Associates, Inc., New York, 2019).
Cotter, F. Uses of Complex Wavelets in Deep Convolutional Neural Networks. Ph.D. thesis (University of Cambridge, 2020).
Bai, S., Kolter, J. Z. & Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Preprint at https://arxiv.org/abs/1803.01271 (2018).
Huang, J., Ding, W. et al. Aircraft trajectory prediction based on bayesian optimised temporal convolutional network–bidirectional gated recurrent unit hybrid neural network. Int. J. Aerosp. Eng. 2022 2086904 (2022).
Berndt, D. J. & Clifford, J. Using Dynamic Time Warping to Find Patterns in Time Series. Vol. 10, 359–370 (AAAI, Seattle, 1994).
Ben Mabrouk, A., Ben Abdallah, N. & Dhifaoui, Z. Wavelet decomposition and autoregressive model for time series prediction. Appl. Math. Comput. 199, 334–340 (2008).
Li, Y., Chai, S., Ma, Z. & Wang, G. A hybrid deep learning framework for long-term traffic flow prediction. IEEE Access 9, 11264–11271 (2021).
Zhang, N., Guan, X., Cao, J., Wang, X. & Wu, H. Wavelet-HST: a wavelet-based higher-order spatio-temporal framework for urban traffic speed prediction. IEEE Access 7, 118446–118458 (2019).
de Queiroz, R. Subband processing of finite length signals without border distortions, Vol. 4, 613–616 (IEEE, San Francisco, 1992).
Su, H., Liu, Q. & Li, J. Boundary effects reduction in wavelet transform for time-frequency analysis. Wseas Trans. Signal Process. 8, 169–179 (2012).
Zhang, Z. The framework of wavelet transform-based flight trajectory prediction. Zenodo https://doi.org/10.5281/zenodo.8238768 (2023).