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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Flight trajectory prediction method based on large language models

Luo Kaiwei
Zhou Jiliu
School of Computer Science, Sichuan University, Chengdu 610064, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0120

Abstract

Flight trajectory prediction is a critical task in air traffic management systems, where deep learning methods have driven significant progress. However, existing approaches often treat training data in a "black-box" manner, limiting model interpretability. Large Language Models (LLMs) excel in text comprehension and generation, possessing powerful reasoning and thinking abilities. However, few studies have explored the application of LLMs in flight trajectory prediction. This paper proposes a novel approach, FTP-LLM, which reformulates trajectory prediction as a language modeling task and pioneers the potential of LLMs in this field. The method extracts spatiotemporal features from real-world flight data and integrates them with domain-specific prompts to construct an instruction dataset for fine-tuning. To enhance interpretability and transparency, the prompts incorporate a Chain-of-Thought (CoT) reasoning process. The experiments fine-tune various LLMs to evaluate their performance in trajectory prediction tasks, while further investigating their generalization ability in few-shot scenarios. Experimental results show that LLMs achieve certain performance improvements over deep learning methods in trajectory predictions. The LLaMA-3.1 model achieves the highest prediction accuracy, reducing average errors by 7.16% for single-step predictions, 10.71% for 4-step predictions, and 10.15% for 8-step predictions.

Foundation Support

国家自然科学基金资助项目(62371325)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0120
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-18] Accepted Paper

Cite This Article

罗恺玮, 周激流. 基于大语言模型的飞行轨迹预测方法 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0120. (Luo Kaiwei, Zhou Jiliu. Flight trajectory prediction method based on large language models [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0120. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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