用图谱增强的LLM模拟城市出行选择，提升行为预测真实度。
Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
- 构建偏好链，结合图RAG与LLM生成上下文敏感的出行行为。
- 在Replica数据集上，出行模式选择准确率显著优于普通LLM。
- 适合缺乏数据的新兴城市，用于交通建模与个性化出行分析。
理解城市环境中人类行为是城市科学的关键领域。然而，在新开发区域收集准确的行为数据面临巨大挑战。近年来，基于大语言模型（LLMs）的生成代理在无需大量数据的情况下模拟人类行为方面展现出潜力。然而，这些方法常难以生成一致、上下文敏感且真实的输出。为此，本文提出偏好链（Preference Chain），将图检索增强生成（Graph RAG）与LLM结合，以提升交通系统中人类行为的上下文感知模拟能力。在Replica数据集上的实验表明，偏好链在匹配真实世界出行方式选择方面优于标准LLM。该方法所构建的出行代理（Mobility Agent）展示了在新兴城市交通建模、个性化出行分析和动态交通预测中的应用前景。尽管存在推理速度慢和幻觉风险等局限，该框架仍为数据稀缺环境下复杂人类行为模拟提供了有力解决方案。
原文摘要 · Abstract (English)
Understanding human behavior in urban environments is a crucial field within city sciences. However, collecting accurate behavioral data, particularly in newly developed areas, poses significant challenges. Recent advances in generative agents, powered by Large Language Models (LLMs), have shown promise in simulating human behaviors without relying on extensive datasets. Nevertheless, these methods often struggle with generating consistent, context-sensitive, and realistic behavioral outputs. To address these limitations, this paper introduces the Preference Chain, a novel method that integrates Graph Retrieval-Augmented Generation (RAG) with LLMs to enhance context-aware simulation of human behavior in transportation systems. Experiments conducted on the Replica dataset demonstrate that the Preference Chain outperforms standard LLM in aligning with real-world transportation mode choices. The development of the Mobility Agent highlights potential applications of proposed method in urban mobility modeling for emerging cities, personalized travel behavior analysis, and dynamic traffic forecasting. Despite limitations such as slow inference and the risk of hallucination, the method offers a promising framework for simulating complex human behavior in data-scarce environments, where traditional data-driven models struggle due to limited data availability.
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