用大模型生成虚拟行人，模拟真实交通行为。
GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior
- 基于人口数据生成带个性的虚拟出行者，自动规划行程和出行方式。
- 在柏林规模测试中复现了不同社会群体的出行模式差异。
- 适合城市规划、交通政策评估，无需人工设定规则。
人们的出行选择反映了个人偏好、社会规范与技术接受度之间的复杂权衡。大规模预测此类行为对城市规划与可持续交通具有重要意义。传统方法依赖手工假设和高成本数据采集，难以用于新技术或政策的早期评估。我们提出生成式交通代理（GTA），利用大语言模型驱动的、基于人格特征的代理，实现大规模、情境敏感的出行行为模拟。GTA从基于人口普查的社会经济数据生成虚拟人群，模拟活动日程与出行方式选择，无需人工设定规则即可实现可扩展的人类化仿真。我们在柏林尺度上进行实验，将模拟结果与真实数据对比。尽管代理能复现诸如按社会经济地位划分的出行方式分布等模式，但在出行距离和出行方式偏好上存在系统性偏差。GTA为研究未来创新（如自行车道、通勤应用）如何影响出行决策提供了新可能。
原文摘要 · Abstract (English)
People's transportation choices reflect complex trade-offs shaped by personal preferences, social norms, and technology acceptance. Predicting such behavior at scale is a critical challenge with major implications for urban planning and sustainable transport. Traditional methods use handcrafted assumptions and costly data collection, making them impractical for early-stage evaluations of new technologies or policies. We introduce Generative Traffic Agents (GTA) for simulating large-scale, context-sensitive transportation choices using LLM-powered, persona-based agents. GTA generates artificial populations from census-based sociodemographic data. It simulates activity schedules and mode choices, enabling scalable, human-like simulations without handcrafted rules. We evaluate GTA in Berlin-scale experiments, comparing simulation results against empirical data. While agents replicate patterns, such as modal split by socioeconomic status, they show systematic biases in trip length and mode preference. GTA offers new opportunities for modeling how future innovations, from bike lanes to transit apps, shape mobility decisions.
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