Distributional Impacts of Congestion Charge Policy in New York City

• In Event: Local Transportation Policies

Abstract

This paper investigates the distributional consequences of New York City’s cordon-based congestion pricing policy, the first of its kind in the United States. The policy aims to reduce vehicle traffic in Manhattan’s Central Business District while generating revenue to support public transit improvements. While the efficiency case for congestion pricing is well-established, concerns remain about how the policy affects different demographic and geographic populations—especially low- and middle-income commuters from outer boroughs and those with limited access to public transit.

To address these concerns, I estimate a nested logit model of travel mode choice using rich data sources, including the New York City Citywide Mobility Survey (NYCCMS), the National Household Travel Survey (NHTS), real-time traffic data from INRIX, and travel time estimates from Google Maps. The model captures heterogeneous travel behavior across income groups, age cohorts, racial and ethnic groups, and home–work locations. This structural model allows for policy simulations that assess how travelers would respond to alternative pricing structures and revenue redistribution mechanisms.

A key contribution of this paper lies in simulating a wide set of counterfactual policy scenarios that reflect real-world policy considerations. These scenarios include:

1. Varying toll levels to estimate optimal pricing levels for traffic reduction and welfare outcomes.


2. Daily pass vs. pay-per-entry pricing, to assess how fare structure affects commuter cost burden and behavioral response.


3. Public transit reinvestment strategies funded by toll revenue, including peak-hour service expansion, new subway and bus routes, improved accessibility, and bike/pedestrian infrastructure upgrades.


4. Imperfect targeting of discounts and subsidies, modeling how errors in identifying or reaching eligible low-income commuters affect distributional equity.


5. Inter-jurisdictional revenue sharing, such as allocating funds to adjacent regions like Jersey City that provide feeder access to Manhattan.


6. Hypothetical infrastructure expansion, including a scenario with a new rail tunnel linking Brooklyn and Jersey City.


7. Direct monetary rebates or transit vouchers to low-income households, assessing the trade-offs between targeted transfers and infrastructure investment.

These simulations are designed to capture not only changes in mode choice and traffic congestion but also changes in household-level welfare. The framework allows for evaluating how total and group-specific welfare change under each policy scenario, thereby offering an equity-centered lens on congestion pricing.

By integrating discrete choice modeling with detailed simulation of policy design, this research provides a flexible and empirically grounded framework for analyzing urban mobility policies. The findings aim to inform the ongoing implementation process in New York and provide broader insights for cities considering similar pricing systems. This work contributes to the policy discourse on balancing urban efficiency with distributional equity, demonstrating that congestion pricing need not come at the expense of vulnerable populations—if designed with redistributive goals in mind.

Author

• Yikuan Ji, University of Maryland, College Park
  Presenting Author