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AI model predicts aircraft landing times with uncertainty quantification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new probabilistic framework for predicting aircraft landing times, accounting for multi-agent interactions and inherent uncertainties in air traffic. This model provides landing times as probability distributions, offering more trustworthy predictions than simple point estimates. Tested using data from Incheon International Airport, the framework demonstrated superior accuracy and uncertainty quantification compared to existing methods, while its attention scores provided insights into air traffic control patterns. AI

IMPACT Enhances air traffic management efficiency and safety by providing more reliable landing time predictions.

RANK_REASON Academic paper on a novel AI framework for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model predicts aircraft landing times with uncertainty quantification

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32 / 100
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Academic paper on a novel AI framework for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kyungmin Kim, Seokbin Yoon, Keumjin Lee ·

    Probabilistic Multi-Agent Aircraft Landing Time Prediction

    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 inherent uncertainty of aircraft trajectories and traffic flows poses significan…

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