New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions. Because such policies generate spillovers across modes and locations, credible control groups are difficult to construct. We address this challenge using time series foundation models to generate probabilistic counterfactual demand forecasts with calibrated uncertainty. Applying this framework to bus, subway, and aggregate trip volume data, we find that post-policy bus and subway ridership increased significantly relative to expected no-policy demand, while overall travel demand decreased modestly. The effects are spatially heterogeneous: while reductions in overall travel demand are concentrated within the Congestion Relief Zone, transit gains extend beyond Manhattan's core. Socio-demographic analyses further reveal uneven adaptation across neighborhoods, highlighting spatial equity implications. Our framework provides a scalable approach for the uncertainty-aware evaluation of system-wide urban interventions when clean control groups are unavailable.

翻译：纽约市于2025年1月实施了全国首个基于区域的拥堵收费计划，为评估全系统城市出行如何响应大规模定价干预提供了契机。由于此类政策会在不同交通方式和区域间产生溢出效应，构建可信的控制组面临困难。我们利用时序基础模型生成具有标定不确定性的概率反事实需求预测，从而解决了这一挑战。将该框架应用于公交、地铁及总出行量数据后，我们发现政策实施后的公交和地铁客流量相对于预期无政策情景显著增长，而总体出行需求略有下降。这一影响呈现出空间异质性：总体出行需求的减少集中在拥堵缓解区内，而公共交通的增长则延伸至曼哈顿核心区以外。社会人口分析进一步揭示了不同社区间的不均衡适应过程，凸显了空间公平性问题。本框架为在缺乏理想控制组的情况下，对系统级城市干预措施进行不确定性感知评估提供了可扩展的方法。