Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
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 vol
The implementation of cordon-based congestion pricing in New York City in January 2025 provides a real-world case study for evaluating mobility responses to large-scale pricing interventions. The use of time series foundation models represents a new methodological approach for such evaluations.
This research provides crucial insights into the effectiveness of congestion pricing policies, which are increasingly considered by major global cities to manage urban mobility, reduce emissions, and generate revenue. The methodology of using AI for counterfactual forecasting has broader applicability for policy evaluation.
Our understanding of how urban mobility systems respond to economic levers is enhanced, and the tools for robust policy evaluation are advanced through the application of time series foundation models. It offers a standardized framework for analyzing complex policy impacts on multifaceted urban systems like public transit networks.
- · Urban planners
- · Public transportation operators
- · AI/ML model developers
- · Cities implementing congestion pricing
- · Drivers within cordon areas (potentially)
- · Traditional traffic modeling firms (potentially, if AI models become standard)
Congestion pricing in NYC may lead to increased public transit ridership and reduced traffic within the cordon area.
Successful implementation and evaluation could encourage other major cities globally to adopt similar congestion pricing schemes, leading to widespread urban mobility shifts.
The AI-driven counterfactual analysis methodology could become a standard for complex policy evaluations, driving further adoption of advanced analytics in governance.
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