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.

翻译：人们的出行选择反映了由个人偏好、社会规范和技术接受度共同塑造的复杂权衡机制。大规模预测此类行为对城市规划和可持续交通具有重要影响，但也是关键挑战。传统方法依赖人工设定的假设和昂贵的数据收集，难以用于新技术或政策的早期评估。本文提出生成式交通智能体（GTA），通过基于大语言模型的角色化智能体，实现大规模、情境敏感的交通选择仿真。GTA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，无需人工规则即可实现可扩展的类人行为仿真。我们在柏林规模的实验中评估GTA，将仿真结果与实证数据对比。虽然智能体能复现按社会经济地位划分的交通方式分布等模式，但在出行距离和方式偏好方面仍存在系统性偏差。GTA为模拟从自行车道到公交应用程序等未来创新如何影响出行决策提供了新的研究路径。