MobGLM: A Large Language Model for Synthetic Human Mobility Generation

Human mobility generation plays a critical role in urban transportation planning. Existing human mobility generation models often fall short of understanding travelers' demographics and integrating multimodal information, including activity purposes, destination choices and transport mode preferences. Recently, mobility generation models leveraging Large Language Models (LLMs) have gained significant attention, while they are limited in directly reproducing spatial information in human mobility profiles. To address these challenges, this paper proposes the Mobility Generative Language Model (MobGLM), a novel approach for generating synthetic human mobility data to support urban planning, transport management, energy consumption and epidemic control. MobGLM addresses these limitations by capturing the complex relationships between agents' mobility patterns and individual demographics. By incorporating personal information, activity types, locations and traffic modes as encoders, MobGLM uniquely identifies and replicates features of human mobility. Our framework is evaluated using a large, real-world mobility dataset and benchmarked against state-of-the-art personal mobility generation techniques. The results demonstrate the effectiveness of MobGLM in producing accurate and reliable synthetic mobility data, highlighting its potential applications in various urban mobility contexts.