Guiding LLM-Based Human Mobility Simulation with Mobility Measures from Shared Data

TL;DR

M2LSimu is a mobility measures-guided multi-prompt adjustment framework that leverages mobility measures derived from shared data as guidance to refine individual-level prompts for realistic mobility generation and significantly outperforms state-of-the-art LLM-based methods.

Abstract

Large-scale human mobility simulation is critical for many science domains such as urban science, epidemiology, and transportation analysis. Recent works treat large language models (LLMs) as human agents to simulate realistic mobility trajectories by modeling individual-level cognitive processes. However, these approaches generate individual mobility trajectories independently, without any population-level coordination mechanism, and thus fail to capture the emergence of collective behaviors. To address this issue, we design M2LSimu, a mobility measures-guided multi-prompt adjustment framework that leverages mobility measures derived from shared data as guidance to refine individual-level prompts for realistic mobility generation. Our framework applies coarse-grained adjustment strategies guided by mobility measures, progressively enabling fine-grained individual-level adaptation while satisfying multiple population-level mobility objectives under a limited budget. Experiments show that M2LSimu significantly outperforms state-of-the-art LLM-based methods on two public datasets.

Figures (12)

• Figure 1: Core idea of M2LSimu.
• Figure 2: Travel distance distributions (Real vs. Simulation).
• Figure 3: Visitation frequency (Real vs. Simulation).
• Figure 4: Comparison of radius of gyration.
• Figure 5: Travel distance (Real vs. Coarse-grained).