GATSim: Urban Mobility Simulation with Generative Agents

TL;DR

GATSim introduces a novel urban mobility simulation framework using generative AI agents with cognitive architectures, enabling realistic, adaptive, and diverse travel behavior modeling that surpasses traditional rule-based systems.

Contribution

This work presents a comprehensive AI-driven simulation architecture integrating large language model-inspired agents with cognitive systems for urban mobility modeling.

Findings

Agents perform competitively with human annotators in role-playing scenarios.

Generated traffic patterns are realistic and align with real-world data.

The framework demonstrates adaptability and behavioral diversity in simulations.

Abstract

Traditional agent-based urban mobility simulations often rely on rigid rulebased systems that struggle to capture the complexity, adaptability, and behavioral diversity inherent in human travel decision making. Inspired by recent advancements in large language models and AI agent technologies, we introduce GATSim, a novel framework that leverages these advancements to simulate urban mobility using generative agents with dedicated cognitive structures. GATSim agents are characterized by diverse socioeconomic profiles, individual lifestyles, and evolving preferences shaped through psychologically informed memory systems and lifelong learning. The main contributions of this work are: 1) a comprehensive architecture that integrates urban mobility foundation model with agent cognitive systems and transport simulation environment; 2) a hierarchical memory designed for efficient retrieval of contextually relevant information, incorporating spatial and temporal associations; 3) 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. Experiments indicate that generative agents perform competitively with human annotators in role-playing scenarios, while naturally producing realistic macroscopic traffic patterns. The code for the prototype implementation is publicly available at https://github.com/qiliuchn/gatsim.