GATSim: Urban Mobility Simulation with Generative Agents

• URL: http://arxiv.org/abs/2506.23306v2
• Date: Fri, 18 Jul 2025 04:20:16 GMT
• Title: GATSim: Urban Mobility Simulation with Generative Agents
• Authors: Qi Liu, Can Li, Wanjing Ma,
• Abstract summary: We introduce GATSim, a novel framework to simulate urban mobility using generative agents with rich, human-like behaviors.<n>Unlike conventional approaches, GATSim agents are characterized by diverse socioeconomic profiles, individual lifestyles, and evolving preferences shaped through psychologically informed memory systems.<n>We implement a prototype system and conduct systematic validation, demonstrating that generative agents produce believable and coherent travel behaviors.
• Score: 12.893057419094932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional agent-based urban mobility simulations often rely on rigid rule-based systems that struggle to capture the complexity, adaptability, and behavioral diversity inherent in human travel decision making. Recent advancements in large language models and AI agent technologies present new opportunities to develop agents with enhanced reasoning capabilities, persistent memory, and adaptive learning. We introduce GATSim (Generative-Agent Transport Simulation), a novel framework that leverages these advancements to simulate urban mobility using generative agents with rich, human-like behaviors. Unlike conventional approaches, GATSim agents are characterized by diverse socioeconomic profiles, individual lifestyles, and evolving preferences shaped through psychologically informed memory systems, tool usage, and lifelong learning. The main contributions of this work are: (1) a comprehensive architecture that integrates an urban mobility foundation model with agent cognitive systems and a transport simulation environment; (2) a hierarchical memory designed for efficient retrieval of contextually relevant information, incorporating spatial and temporal associations, keyword matching, and semantic relevance; (3) innovative planning and reactive mechanisms for modeling adaptive mobility behaviors which integrate a multi-scale reflection process to transform specific travel experiences into generalized behavioral insights. We implement a prototype system and conduct systematic validation, demonstrating that generative agents produce believable and coherent travel behaviors. Experimental results indicate that generative agents perform at least as well as human annotators with 92\% posterior probability, while naturally producing realistic macroscopic traffic patterns. The code for the prototype implementation is publicly available at https://github.com/qiliuchn/gatsim.

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