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This study leverages time series foundation models to evaluate the impact of NYC's congestion pricing on urban mobility, demonstrating how AI can generate probabilistic counterfactuals where traditional control groups are unavailable. The research reveals spatially heterogeneous effects on transit ridership and overall travel demand while highlighting socio-demographic disparities in adaptation across neighborhoods. By providing a scalable framework for uncertainty-aware policy evaluation, this work bridges machine learning capabilities with critical questions of equity in large-scale urban interventions.
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Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

arXiv2 min read

arXiv:2606.17530v1 Announce Type: cross Abstract: 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.

Abstract: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.

Physics and Society (physics.soc-ph); Machine Learning (cs.LG); General Economics (econ.GN); Applications (stat.AP)

arXiv:2606.17530 [physics.soc-ph]

(or arXiv:2606.17530v1 [physics.soc-ph] for this version)

https://doi.org/10.48550/arXiv.2606.17530 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 253 words

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