Agentic Large Language Models for Generating Large-Scale Urban Daily Activity Patterns

Urban daily activity patterns play an important role in fields such as urban planning and traffic management, while the powerful data generation and reasoning capabilities of LLMs (Large Language Models) have sparked a surge of interest in recent years, with applications in various domains. Inspired by their natural language processing and pattern recognition functionalities, we attempted to utilize LLMs to simulate the activity patterns of people of different ages and occupations in metropolitan areas (Tokyo). By leveraging pre-processed Person Trip data, we employed 3 methods to test the ability of LLMs to generate urban daily activity data. The results were evaluated based on metrics such as rationality, diversity, and error rates. Results indicate that the fine-tuned LLaMA-3 model is capable of accurately simulating the activity distribution patterns of metropolitan populations. Among the prompt-based strategies, the few-shot approach yielded the best performance. Although designing the instruction for prompts and post-processing the data required considerable time, the few-shot prompt strategy proved to be an effective option for this task.