Simulating Public Transit Fare Policies in NYC: An Efficient, Socioeconomic-Aware Framework
For transit planners and policymakers in NYC, this framework enables assessment of trade-offs between ridership, revenue, and equity in fare policy design.
This paper presents a scalable simulation framework for evaluating public transit fare policies in NYC, showing that pricing changes modestly affect total ridership but significantly alter modal composition and produce heterogeneous impacts across income groups, with fare-free bus policies benefiting lower-income riders.
Designing equitable and effective public transit fare policies is challenging due to complex interactions among traveler behavior, multimodal networks, and socioeconomic heterogeneity. This paper presents a scalable, data-driven simulation framework for evaluating transit fare policies in New York City (NYC), integrating a synthetic population, agent-based simulation, multimodal travel-time estimation, and fare-sensitive mode choice modeling. We evaluate multiple fare scenarios, including distance-based pricing, fare increases, and fare-free bus policies. Results show that pricing changes modestly affect total ridership but significantly alter modal composition and produce heterogeneous impacts across income groups. In particular, fare-free bus policies generate substantial benefits for lower-income riders by increasing bus usage and reducing fare burden, while introducing trade-offs in revenue. To support city-scale analysis, we introduce a sampling-based approach that reduces computational cost while preserving aggregate accuracy. The proposed framework provides a practical tool for assessing trade-offs between ridership, revenue, and equity, enabling more informed and equitable transit policy design.