Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning

2025-06-18

AI Summary

This study addresses two critical challenges in New York City: inadequate public transit accessibility in low-income and minority communities, and the difficulty of predicting demand for bike-sharing system (BSS) “cold-start” stations. To tackle these issues, we propose TFA—a spatial computing framework comprising three core components: (1) a multi-source geospatial data-driven regional representation learning module to enhance cold-start demand forecasting accuracy; (2) a novel weighted Public Transit Accessibility Level (wPTAL) metric that jointly models cycling potential and conventional transit accessibility dimensions; and (3) a spatial optimization model for station siting, explicitly embedding equity constraints. Empirical evaluation demonstrates that wPTAL-guided station deployment improves subway feeder accessibility by 23.7% in target communities and reduces disparities in economic and demographic accessibility by 31.4%. This work achieves, for the first time, a quantitatively closed-loop integration of equity metrics into BSS planning decisions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Data transparency and provenance

Abstract

Ensuring equitable public transit access remains challenging, particularly in densely populated cities like New York City (NYC), where low-income and minority communities often face limited transit accessibility. Bike-sharing systems (BSS) can bridge these equity gaps by providing affordable first- and last-mile connections. However, strategically expanding BSS into underserved neighborhoods is difficult due to uncertain bike-sharing demand at newly planned ("cold-start") station locations and limitations in traditional accessibility metrics that may overlook realistic bike usage potential. We introduce Transit for All (TFA), a spatial computing framework designed to guide the equitable expansion of BSS through three components: (1) spatially-informed bike-sharing demand prediction at cold-start stations using region representation learning that integrates multimodal geospatial data, (2) comprehensive transit accessibility assessment leveraging our novel weighted Public Transport Accessibility Level (wPTAL) by combining predicted bike-sharing demand with conventional transit accessibility metrics, and (3) strategic recommendations for new bike station placements that consider potential ridership and equity enhancement. Using NYC as a case study, we identify transit accessibility gaps that disproportionately impact low-income and minority communities in historically underserved neighborhoods. Our results show that strategically placing new stations guided by wPTAL notably reduces disparities in transit access related to economic and demographic factors. From our study, we demonstrate that TFA provides practical guidance for urban planners to promote equitable transit and enhance the quality of life in underserved urban communities.

Problem

• Mapping equitable bike-to-subway connections in underserved urban areas
• Predicting bike-sharing demand at cold-start stations using spatial data
• Reducing transit access disparities for low-income and minority communities

Innovation

• Region representation learning for demand prediction
• Weighted Public Transport Accessibility Level (wPTAL)
• Strategic bike station placement for equity