# Probabilistic Multi-Agent Aircraft Landing Time Prediction

arXiv: https://arxiv.org/abs/2512.08281 · HTML: https://mlsanity.com/paper/2512.08281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data](https://mlsanity.com/paper/1810.09568/) (2018)
- [Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures](https://mlsanity.com/paper/2303.09981/) (2023)
- [Learning to Explain Air Traffic Situation](https://mlsanity.com/paper/2502.10764/) (2025)
- [Multi-Agent Inverted Transformer for Flight Trajectory Prediction](https://mlsanity.com/paper/2509.21004/) (2025)
- [Efficient Real-Time Aircraft ETA Prediction via Feature Tokenization Transformer](https://mlsanity.com/paper/2508.09144/) (2025)

## Built on by (most cited first, 1 of 1)

- [DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers](https://mlsanity.com/paper/2610.16959/) (2026)

## Abstract (arXiv)

Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the inherent uncertainty of aircraft trajectories and traffic flows poses significant challenges to both prediction accuracy and trustworthiness. Therefore, prediction models should not only provide point estimates of aircraft landing times but also the uncertainties associated with these predictions. Furthermore, aircraft trajectories are frequently influenced by the presence of nearby aircraft through air traffic control interventions such as radar vectoring. Consequently, landing time prediction models must account for multi-agent interactions in the airspace. In this work, we propose a probabilistic multi-agent aircraft landing time prediction framework that provides the landing times of multiple aircraft as distributions. We evaluate the proposed framework using an air traffic surveillance dataset collected from the terminal airspace of the Incheon International Airport in South Korea. The results demonstrate that the proposed model achieves higher prediction accuracy than the baselines and quantifies the associated uncertainties of its outcomes. In addition, the model uncovered underlying patterns in air traffic control through its attention scores, thereby enhancing explainability.