  Understanding human mobility patterns is essential for various applications,
from urban planning to public safety. The individual trajectory such as mobile
phone location data, while rich in spatio-temporal information, often lacks
semantic detail, limiting its utility for in-depth mobility analysis. Existing
methods can infer basic routine activity sequences from this data, lacking
depth in understanding complex human behaviors and users' characteristics.
Additionally, they struggle with the dependency on hard-to-obtain auxiliary
datasets like travel surveys. To address these limitations, this paper defines
trajectory semantic inference through three key dimensions: user occupation
category, activity sequence, and trajectory description, and proposes the
Trajectory Semantic Inference with Large Language Models (TSI-LLM) framework to
leverage LLMs infer trajectory semantics comprehensively and deeply. We adopt
spatio-temporal attributes enhanced data formatting (STFormat) and design a
context-inclusive prompt, enabling LLMs to more effectively interpret and infer
the semantics of trajectory data. Experimental validation on real-world
trajectory datasets demonstrates the efficacy of TSI-LLM in deciphering complex
human mobility patterns. This study explores the potential of LLMs in enhancing
the semantic analysis of trajectory data, paving the way for more sophisticated
and accessible human mobility research.
