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arXiv · 2505.21880

Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

Yu-Lun Song·Chung-En Tsern·Che-Cheng Wu·Yu-Ming Chang·Syuan-Bo Huang·Wei-Chu Chen·Michael Chia-Liang Lin·Yu-Ta Lin
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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.

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Yu-Lun Song, Chung-En Tsern, Che-Cheng Wu, Yu-Ming Chang, Syuan-Bo Huang, Wei-Chu Chen, Michael Chia-Liang Lin, Yu-Ta Lin. 2025-05-28. Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation. https://doi.org/10.17605/osf.io%2Fabyqh

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