Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

University College London Summary 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. 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. KEYWORDS: Mobility simulation, Agent-Based Modeling (ABM), Large Language Model (LLM), Synthetic profiles, Urban planning 1. Introduction Mobility reflects the unique geographic, economic, and cultural contexts of cities while being shaped by and confined to the urban infrastructure that supports it.