An Exploratory Assessment of LLMs’ Potential for Flight Trajectory Reconstruction Analysis

Qilei Zhang () and John H. Mott
Additional contact information
Qilei Zhang: School of Engineering and Technology, Central Queensland University, Norman Gardens, QLD 4701, Australia
John H. Mott: School of Aviation and Transportation Technology, Purdue University, West Lafayette, IN 47907, USA

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: Large Language Models (LLMs) hold transformative potential for analyzing sequential data, offering an opportunity to enhance the aviation field’s data management and decision support systems. This study explores the capability of the LLaMA 3.1-8B model, an advanced open source LLM, for the tasks of reconstructing flight trajectories using synthetic Automatic Dependent Surveillance Broadcast (ADS-B) data characterized by noise, missing points, and data irregularities typical of real-world aviation scenarios. Comparative analyses against traditional approaches, such as the Kalman filter and the sequence to sequence (Seq2Seq) model with a Gated Recurrent Unit (GRU) architecture, revealed that the fine-tuned LLaMA model significantly outperforms these conventional methods in accurately estimating various trajectory patterns. A novel evaluation metric, containment accuracy , is proposed to simplify performance assessment and enhance interpretability by avoiding complex conversions between coordinate systems. Despite these promising outcomes, the study identifies notable limitations, particularly related to model hallucination outputs and token length constraints that restrict the model’s scalability to extended data sequences. Ultimately, this research underscores the substantial potential of LLMs to revolutionize flight trajectory reconstruction and their promising role in time series data processing, opening broader avenues for advanced applications throughout the aviation and transportation sectors.

Keywords: LLMs; LLaMA; flight trajectory reconstruction; trajectory prediction; ADS-B; time series data prediction; air traffic management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/11/1775/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/11/1775/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:11:p:1775-:d:1665067

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().