用大模型分析船舶轨迹数据，探索四种实用方法
Using LLMs for Analyzing AIS Data
- 设计特定提问评估大模型在船舶数据上的推理能力
- 四种方法中，语义轨迹推理效果最佳，原始数据处理最差
- 适合想用大模型分析航运数据的研究者和从业者
大型语言模型（LLMs）在多个领域产生深远影响，包括移动性数据科学。本文探索并实验了多种利用LLMs分析AIS数据的方法。我们设计了一系列精心构造的查询，用于评估LLMs在此类任务中的推理能力。进一步地，实验比较了四种方法：(1) 将LLMs作为空间数据库的自然语言接口；(2) 对原始数据进行推理；(3) 对压缩轨迹进行推理；(4) 对语义轨迹进行推理。我们分析了这四种方法的优缺点，并讨论了相关发现。目标是为研究者和实践者提供依据，根据具体分析目标选择最合适的基于LLM的方法。
原文摘要 · Abstract (English)
Recent research in Large Language Models (LLMs), has had a profound impact across various fields, including mobility data science. This paper explores the and experiment with different approaches to using LLMs for analyzing AIS data. We propose a set of carefully designed queries to assess the reasoning capabilities of LLMs in this kind of tasks. Further, we experiment with four different methods: (1) using LLMs as a natural language interface to a spatial database, (2) reasoning on raw data, (3) reasoning on compressed trajectories, and (4) reasoning on semantic trajectories. We investigate the strengths and weaknesses for the four methods, and discuss the findings. The goal is to provide valuable insights for both researchers and practitioners on selecting the most appropriate LLM-based method depending on their specific data analysis objectives.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。