Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

2026-06-16

AI Summary

This study evaluates the causal effects of New York City’s congestion pricing policy on urban travel demand and public transit usage in the absence of a valid control group. To address this challenge, we propose an uncertainty-aware intervention assessment framework grounded in time series foundation models, which enables robust inference through probabilistic counterfactual forecasting and calibrated uncertainty quantification. Empirical results indicate a modest overall decline in travel demand following policy implementation, accompanied by a significant increase in public transit ridership. These effects exhibit pronounced spatiotemporal heterogeneity: reductions in vehicle trips are concentrated within the congestion pricing zone, while transit benefits extend beyond Manhattan’s core, with notable variation across neighborhoods in their adaptive capacity to the policy change.

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.

Problem

• congestion pricing
• public transit
• travel demand
• spatial heterogeneity
• urban mobility

Innovation

• time series foundation models
• counterfactual forecasting
• congestion pricing
• uncertainty-aware evaluation
• spatial heterogeneity