Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models

TL;DR

This paper introduces TSI-LLM, a framework leveraging large language models to infer detailed semantic information from human mobility trajectories, addressing limitations of existing methods and enhancing understanding of complex behaviors.

Contribution

The paper proposes a novel LLM-based framework for deep semantic inference of trajectories, incorporating spatio-temporal formatting and context-aware prompts, overcoming reliance on auxiliary datasets.

Findings

TSI-LLM effectively infers user occupation, activity sequences, and trajectory descriptions.

Experimental results show improved accuracy in semantic interpretation of mobility data.

The approach outperforms traditional methods in understanding complex human mobility patterns.

Abstract

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.