Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

• URL: http://arxiv.org/abs/2505.21880v2
• Date: Thu, 03 Jul 2025 06:38:34 GMT
• Title: Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation
• Authors: 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,
• Abstract summary: This study presents an innovative approach to urban mobility simulation by integrating a Large Language Model (LLM) with Agent-Based Modeling (ABM)<n>Unlike traditional rule-based ABM, the proposed framework leverages LLM to enhance agent diversity and realism.<n>Using real-world data, the simulation models individual behaviors and large-scale mobility patterns in Taipei City.
• Score: 0.04517077427559345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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