Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
This study presents an innovative approach to urban mobility simulation by
integrating a Large Language Model (LLM) with Agent-Based Modeling (ABM).
Unlike traditional rule-based ABM, the proposed framework leverages LLM to
enhance agent diversity and realism by generating synthetic population
profiles, allocating routine and occasional locations, and simulating
personalized routes. Using real-world data, the simulation models individual
behaviors and large-scale mobility patterns in Taipei City. Key insights, such
as route heat maps and mode-specific indicators, provide urban planners with
actionable information for policy-making. Future work focuses on establishing
robust validation frameworks to ensure accuracy and reliability in urban
planning applications.
