Simulating Public Transit Fare Policies in NYC: An Efficient, Socioeconomic-Aware Framework

2026-06-20

AI Summary

This study addresses the design of public transit fare policies that balance equity and efficiency within complex multimodal transportation systems, explicitly accounting for traveler behavior, socioeconomic heterogeneity, and network interaction effects. To this end, we develop a scalable, data-driven simulation framework integrating synthetic populations, agent-based modeling, multimodal travel time estimation, and a fare-sensitive mode choice model. We further introduce a sampling-accelerated algorithm that achieves a favorable trade-off between computational efficiency and aggregate accuracy, enabling, for the first time at an urban scale, a fine-grained evaluation of how fare policies mediate trade-offs among equity, ridership composition, and fiscal revenue. Empirical results indicate that fare adjustments have limited impact on total ridership but substantially alter modal shares and generate heterogeneous effects across income groups; notably, fare-free transit significantly alleviates mobility burdens for low-income populations, albeit at considerable fiscal cost.

Abstract

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.

Problem

• public transit fare policies
• equity
• socioeconomic heterogeneity
• ridership
• multimodal networks

Innovation

• agent-based simulation
• socioeconomic-aware modeling
• multimodal travel-time estimation
• sampling-based scalability
• fare-sensitive mode choice