arXiv Machine Learning June 26, 2026 By Hong-ah Chai, Seokbin Yoon, Keumjin Lee

Learning to Explain Air Traffic Situation

Read the original on arXiv Machine Learning →

arXiv:2502. 10764v4 Announce Type: replace Abstract: Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 22

Multi-Agent Inverted Transformer for Flight Trajectory Prediction

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

llms agents

arXiv Machine Learning

Jun 18

Unlocking air traffic flow prediction through microscopic aircraft-state modeling

arXiv:2605. 10083v2 Announce Type: replace Abstract: Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management.

By Bin Wang, Anqi Liu, Jiangtao Zhao, Hina Birahmani, Yanyong Huang, Peilan He, Guiyuan Jiang, Feng Hong, Yanwei Yu, Yuanyuan Hou, Tianrui Li

arXiv AI

Jul 8

LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation

arXiv:2510. 23636v4 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system.

By Thaweerath Phisannupawong, Joshua Julian Damanik, Han-Lim Choi

llms

arXiv AI

Jul 29

RankFormer: A Propose-then-Select Transformer for Multi-Agent Multimodal Trajectory Prediction

arXiv:2604. 07126v2 Announce Type: replace-cross Abstract: Predicting vehicle trajectories plays an important role in autonomous driving, transportation safety analysis, traffic operations, etc.

By Diyi Liu, Zihan Niu, Tu Xu, Xingchen Zhang, Lishan Sun

llms agents robotics multimodal safety

arXiv AI

Jun 12

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.

By Siyu Li, Toan Tran, Lingyi Zhao, Khurram Shafique, Li Xiong

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 29

Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout

arXiv:2605. 05092v2 Announce Type: replace-cross Abstract: Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions.

By Haozhuang Chi, Daosheng Qiu, Hao Su, Haochen Liu, Zirui Li, Haoruo Zhang, Chen Lv

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Multi-Agent Inverted Transformer for Flight Trajectory Prediction

Unlocking air traffic flow prediction through microscopic aircraft-state modeling

LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation

RankFormer: A Propose-then-Select Transformer for Multi-Agent Multimodal Trajectory Prediction

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout