纽约拥堵收费后公交地铁客流上升，整体出行减少但影响不均。
Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
- 用时间序列基础模型预测无政策时的出行需求，构建概率反事实基准。
- 收费区外出行量下降，曼哈顿核心外公交地铁客流显著增加。
- 适合评估缺乏对照组的城市交通政策，尤其关注公平性问题。
纽约市于2025年1月实施了美国首个基于围栏的拥堵收费计划，为评估大规模定价干预对城市交通系统的影响提供了契机。由于此类政策在不同交通方式和区域间产生溢出效应，难以构建可信的对照组。本文采用时间序列基础模型生成带有校准不确定性的概率反事实需求预测，应用于公交车、地铁及总出行量数据。结果显示，政策实施后，公交与地铁客流显著高于预期无政策情形下的水平，而总体出行需求仅小幅下降。影响具有明显的空间异质性：总体出行减少集中于拥堵缓解区，而公共交通增长则延伸至曼哈顿核心区之外。社会人口学分析进一步揭示了不同社区适应差异，凸显空间公平性问题。该框架为在缺乏清晰对照组情况下，进行不确定性感知的系统级城市干预评估提供了可扩展的方法。
原文摘要 · Abstract (English)
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.
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