AI自动发现交通规律，揭示城市驾驶行为的隐藏时间记忆。
Autonomous discovery of traffic laws with AI traffic scientists
- 构建智能体系统，通过证据筛选、假设生成与验证闭环发现交通规律。
- 在8个城市2个数据集上发现新时间尺度规律，且验证已知3条交通定律。
- 适合交通规划、城市智能管理领域研究者，推动AI科学发现落地复杂系统。
通用交通规律描述了城市中拥堵、出行行为和移动模式的重复性模式，为交通规划与管理提供科学基础。然而，其发现仍依赖专家经验，需从异构观测数据中识别候选规律或通过干预实验验证。尽管人工智能在受控实验室中已推动科学发现，但将其扩展至复杂交通领域仍具挑战。本文提出TrafficSci——一个智能体式AI系统，将交通规律发现转化为迭代可审计的工作流，包含证据界定、批判性判断的假设生成及观测-干预双重验证。在涵盖人口、网络、控制与轨迹尺度的四项案例研究中，TrafficSci自主重发现了三条已有交通定律，并揭示了一种未报告的城市驾驶行为内在时间记忆尺度，该结果在八个城市的两个轨迹数据集中均具有统计一致性。TrafficSci为将AI驱动的科学发现从受控领域拓展至复杂城市系统提供了可行路径。
原文摘要 · Abstract (English)
Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a scientific basis for transportation planning, management and control. Their discovery, however, remains expert-driven, requiring candidate regularities to be identified from heterogeneous observational evidence or validated through intervention experiments. Although autonomous artificial intelligence (AI) systems have advanced scientific discovery in controlled laboratory settings, extending them to complex transportation domains remains a challenge. Here we present TrafficSci, an agentic AI system that formulates traffic-law discovery as an iterative, auditable workflow integrating evidence scoping, critic-judge hypothesis induction, and observational-interventional validation. Across four case studies spanning population, network, control and trajectory scales, TrafficSci autonomously rediscovers three established traffic laws and identifies an unreported intrinsic temporal memory scale in urban driving behavior, statistically consistent across eight cities and two trajectory datasets. TrafficSci provides a route for extending AI-driven scientific discovery from controlled domains to complex urban systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。