  Human mobility is inextricably linked to social issues such as traffic
congestion, energy consumption, and public health; however, privacy concerns
restrict access to mobility data. Recently, research have utilized Large
Language Models (LLMs) for human mobility generation, in which the challenge is
how LLMs can understand individuals' mobility behavioral differences to
generate realistic trajectories conforming to real world contexts. This study
handles this problem by presenting an LLM agent-based framework (MobAgent)
composing two phases: understanding-based mobility pattern extraction and
reasoning-based trajectory generation, which enables generate more real travel
diaries at urban scale, considering different individual profiles. MobAgent
extracts reasons behind specific mobility trendiness and attribute influences
to provide reliable patterns; infers the relationships between contextual
factors and underlying motivations of mobility; and based on the patterns and
the recursive reasoning process, MobAgent finally generates more authentic and
personalized mobilities that reflect both individual differences and real-world
constraints. We validate our framework with 0.2 million travel survey data,
demonstrating its effectiveness in producing personalized and accurate travel
diaries. This study highlights the capacity of LLMs to provide detailed and
sophisticated understanding of human mobility through the real-world mobility
data.
