{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-aircraft-trajectories-a-deep","title":"Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach","arxiv_id":"1812.11670","date":"2018-12-31","proceeding":null,"authors":["Yulin Liu","Mark Hansen"],"abstract":"Reliable 4D aircraft trajectory prediction, whether in a real-time setting or\nfor analysis of counterfactuals, is important to the efficiency of the aviation\nsystem. Toward this end, we first propose a highly generalizable efficient\ntree-based matching algorithm to construct image-like feature maps from\nhigh-fidelity meteorological datasets - wind, temperature and convective\nweather. We then model the track points on trajectories as conditional Gaussian\nmixtures with parameters to be learned from our proposed deep generative model,\nwhich is an end-to-end convolutional recurrent neural network that consists of\na long short-term memory (LSTM) encoder network and a mixture density LSTM\ndecoder network. The encoder network embeds last-filed flight plan information\ninto fixed-size hidden state variables and feeds the decoder network, which\nfurther learns the spatiotemporal correlations from the historical flight\ntracks and outputs the parameters of Gaussian mixtures. Convolutional layers\nare integrated into the pipeline to learn representations from the\nhigh-dimension weather features. During the inference process, beam search,\nadaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to\nprune the variance of generated trajectories.","url_abs":"http://arxiv.org/abs/1812.11670v1","url_pdf":"http://arxiv.org/pdf/1812.11670v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-aircraft-trajectories-a-deep","repo_url":"https://github.com/ClementJaccarino/NEC_DeepTP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"predicting-aircraft-trajectories-a-deep","repo_url":"https://github.com/yulinliu101/DeepTP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.11670","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}