Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

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.

@article{song2025_2505.21880,
  title={ Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation },
  author={ Yu-Lun Song and Chung-En Tsern and Che-Cheng Wu and Yu-Ming Chang and Syuan-Bo Huang and Wei-Chu Chen and Michael Chia-Liang Lin and Yu-Ta Lin },
  journal={arXiv preprint arXiv:2505.21880},
  year={ 2025 }
}