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arXiv Open Access 2025

Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

Yu-Lun Song Chung-En Tsern Che-Cheng Wu Yu-Ming Chang Syuan-Bo Huang +3 lainnya
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Abstrak

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.

Topik & Kata Kunci

cs.MA cs.AI cs.CL cs.CY

Penulis (8)

Y

Yu-Lun Song

C

Chung-En Tsern

C

Che-Cheng Wu

Y

Yu-Ming Chang

S

Syuan-Bo Huang

W

Wei-Chu Chen

M

Michael Chia-Liang Lin

Y

Yu-Ta Lin

Format Sitasi

APA MLA BibTeX
Song, Y., Tsern, C., Wu, C., Chang, Y., Huang, S., Chen, W. et al. (2025). Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation. https://arxiv.org/abs/2505.21880

Akses Cepat

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Tahun Terbit
2025
Bahasa
en
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arXiv
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Open Access ✓