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LLMs for Human Mobility: Opportunities, Challenges, and Future Directions

Jie Gao, Yaoxin Wu

Abstract

Human mobility studies how people move among meaningful places over time and how these movements aggregate into population-level patterns that shape accessibility, congestion, emissions, and public health. Large language models (LLMs) are increasingly used in this domain because many human mobility problems require reasoning about place and activity semantics, travelers' intentions and preferences, and diverse real-world constraints that are difficult to capture using coordinates and other purely numerical attributes. Despite rapid growth, the literature is still scattered, and there is no clear overview that connects human mobility tasks, challenges, and LLM designs in a consistent way. This survey therefore provides a comprehensive synthesis of LLM-based research on human mobility across five tasks, including travel itinerary planning, trajectory generation, mobility simulation, mobility prediction, and mobility semantics and understanding. For each task, we review representative work, connect core challenges to the specific roles of LLMs, and summarize typical LLM-based solution designs. We conclude with open challenges and research directions toward reliable, grounded and privacy-aware LLM-based approaches for human mobility.

LLMs for Human Mobility: Opportunities, Challenges, and Future Directions

Abstract

Human mobility studies how people move among meaningful places over time and how these movements aggregate into population-level patterns that shape accessibility, congestion, emissions, and public health. Large language models (LLMs) are increasingly used in this domain because many human mobility problems require reasoning about place and activity semantics, travelers' intentions and preferences, and diverse real-world constraints that are difficult to capture using coordinates and other purely numerical attributes. Despite rapid growth, the literature is still scattered, and there is no clear overview that connects human mobility tasks, challenges, and LLM designs in a consistent way. This survey therefore provides a comprehensive synthesis of LLM-based research on human mobility across five tasks, including travel itinerary planning, trajectory generation, mobility simulation, mobility prediction, and mobility semantics and understanding. For each task, we review representative work, connect core challenges to the specific roles of LLMs, and summarize typical LLM-based solution designs. We conclude with open challenges and research directions toward reliable, grounded and privacy-aware LLM-based approaches for human mobility.
Paper Structure (22 sections, 3 figures, 5 tables)

This paper contains 22 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. LLM Preliminaries
  3. Prompt engineering
  4. Agentic LLMs
  5. Fine-tuning
  6. LLMs for Human Mobility
  7. LLMs for travel itinerary planning
  8. LLMs for trajectory generation
  9. LLMs for mobility simulation
  10. LLMs for mobility prediction
  11. LLMs for mobility semantics and understanding
  12. Discussion
  13. Connecting task specific challenges with LLM opportunities
  14. Task specific roles of LLMs in human mobility
  15. Open Challenges and Future Directions
  16. ...and 7 more sections

Figures (3)

  • Figure 1: An illustration of three common paradigms to use LLMs.
  • Figure 2: An Overview of LLMs for Human Mobility, where we summarize the main focus of each human mobility task along with the commonly used LLM-based techniques.
  • Figure 3: The typical opportunities and roles of LLMs for different human mobility tasks