Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913923418030080 |
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| author | Song, Yu-Lun Tsern, Chung-En Wu, Che-Cheng Chang, Yu-Ming Huang, Syuan-Bo Chen, Wei-Chu Lin, Michael Chia-Liang Lin, Yu-Ta |
| author_facet | Song, Yu-Lun Tsern, Chung-En Wu, Che-Cheng Chang, Yu-Ming Huang, Syuan-Bo Chen, Wei-Chu Lin, Michael Chia-Liang Lin, Yu-Ta |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation Song, Yu-Lun Tsern, Chung-En Wu, Che-Cheng Chang, Yu-Ming Huang, Syuan-Bo Chen, Wei-Chu Lin, Michael Chia-Liang Lin, Yu-Ta Multiagent Systems Artificial Intelligence Computation and Language Computers and Society 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. |
| title | Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation |
| topic | Multiagent Systems Artificial Intelligence Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2505.21880 |