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

2026-03-12

AI Summary

Existing research on human mobility modeling lacks a unified framework that integrates place semantics, travel intent, and real-world constraints, resulting in fragmented and unsystematic methodologies. This work establishes, for the first time, a systematic mapping between large language models (LLMs) and the five core tasks of human mobility: trip planning, trajectory generation, mobility simulation, mobility prediction, and semantic understanding. It clarifies the central roles and design paradigms of LLMs within each task. By leveraging LLMs’ natural language understanding, reasoning, and generation capabilities alongside domain-specific knowledge, the study proposes representative solutions and outlines future research directions centered on reliability, trustworthiness, and privacy preservation in LLM-driven human mobility modeling.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• large language models
• human mobility
• survey
• mobility tasks
• research synthesis

Innovation

• Large Language Models
• Human Mobility
• Trajectory Generation
• Mobility Prediction
• Semantic Understanding