Abstract for: System Dynamics Modeling of NYC Congestion Pricing: The Long Run Performance of Toll Schedules 2025-2031

New York City’s 2025 congestion pricing program aims to reduce traffic in the Manhattan Congestion Relief Zone, but its long-term dynamic effects remain uncertain. Because congestion pricing triggers feedback involving traffic diversion, mode shift, transit crowding, and reinvestment, this study uses a System Dynamics approach to examine how these interactions shape policy performance over time. This study develops a preliminary System Dynamics model based on causal loop and stock-flow structures linking toll pricing, relative driving attractiveness, traffic volume, transit ridership, peripheral congestion, and toll-revenue reinvestment. The model is calibrated against observed 2025 CRZ entries and transit ridership, validated through structural and behavioral checks, and simulated over 2023-2033 under phased toll schedules. The calibrated model reproduces observed 2025 monthly CRZ vehicle entries and transit ridership with mean relative error below 5%. Simulation results show that the strongest policy effects occur during the initial implementation phase, with later stages exhibiting a goal-seeking stabilization pattern. Traffic volume and vehicle entries decline, while transit ridership increases as displaced travel demand shifts toward public transit. The findings suggest that congestion pricing should not be evaluated solely by reductions in core-area traffic. Its long-run success depends on how effectively the wider transport system absorbs displaced demand through transit capacity, spillover management, and reinvestment design. The study also highlights the value of System Dynamics for assessing phased urban mobility policies under feedback-rich conditions. For language polishing only