arXiv:2608.28981v1 Announce Type: new
Abstract: As air traffic volumes in the National Airspace System continue to expand, in particular in the low altitude airspaces, the need for scalable decision...
By Louis Brusset, Mathurin Petit, Jordan Kam, Alexandre Bayen
arXiv:2609.14374v1 Announce Type: cross
Abstract: Dynamic trajectory prediction has become an important paradigm for data-driven transient stability analysis (TSA), yet most existing predictors remai...
By Chao Shen, Hongwei Zhen, Junyan Shao, Zhenghao Yang, Yifan Zhang, Mingyang Sun
arXiv:2512.08281v2 Announce Type: replace-cross
Abstract: Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the...
By Kyungmin Kim, Seokbin Yoon, Keumjin Lee
arXiv:2509. 21004v3 Announce Type: replace Abstract: Flight trajectory prediction for multiple aircraft is essential and provides critical insights into how aircraft navigate within current air traffic flows.
By Seokbin Yoon, Keumjin Lee
arXiv:2609.16528v1 Announce Type: new
Abstract: Building accurate decision-support tools for next-generation air traffic control requires robust trajectory prediction models. We present a flow-matchi...
By Mathurin Petit, Emir Torun, Louis Brusset, Jordan Kam, Alexandre M. Bayen
M3-Former is a multimodal transformer framework that uses large language models to encode vessel static attributes and navigational intent as semantic priors for long‑term trajectory prediction. It builds a unified multimodal representation space, aligns static semantic information with dynamic trajectory features via self‑attention, and employs a dual‑granularity Mixture‑of‑Experts architecture to capture both global route planning and fine‑grained maneuvering behaviors. A Steering‑Weighted Cross‑Entropy loss further improves accuracy on sparse turning events, and experiments on a Danish AIS dataset show consistent improvements over state‑of‑the‑art baselines, reducing ADE and FDE by up to 5.1% in 4‑hour predictions.
By Wenzhe Jin, Haina Tang