普适性交通规律描述了拥堵、移动性及驾驶行为在不同城市中反复出现的模式，为交通规划、管理和控制提供了科学基础。然而，此类规律的发现目前仍依赖专家主导，需从异构观测证据中识别候选规律，或通过干预实验加以验证。尽管自主人工智能（AI）系统已在受控实验室环境中推动科学发现，但将其拓展至复杂的交通领域仍具挑战。本文提出TrafficSci——一种具有代理能力的AI系统，将交通规律发现建模为一个迭代式、可审计的工作流程，整合证据范围界定、批判—判断式假设归纳，以及观测—干预式验证。在涵盖人口、网络、控制与轨迹四个尺度的四项案例研究中，TrafficSci自主重现了三条既有的交通规律，并发现了一种此前未被报道的城市驾驶行为内在时间记忆尺度；该尺度在八个城市的统计数据及两个轨迹数据集上均表现出统计一致性。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一条可行路径。

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.