用空间学习预测共享单车需求，帮城市公平扩展地铁接驳站。
Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning
- 用区域表征学习融合多源地理数据，预测冷启动站点需求。
- 提出wPTAL指标，结合骑行需求与传统通达性评估，发现并缩小弱势群体出行差距。
- 适合城市规划者用于推动交通公平，尤其关注低收入与少数族裔社区。
确保公共交通公平可及仍是挑战，尤其在纽约市等高密度城市，低收入和少数族裔社区常面临通达性不足。共享单车系统（BSS）可通过提供低成本首末公里连接缓解这一问题。然而，由于新设站点（冷启动）的骑行需求不确定，且传统可达性指标难以反映真实骑行潜力，扩展策略面临困难。本文提出「全民通勤」（Transit for All, TFA）空间计算框架，包含三部分：(1) 基于区域表征学习，融合多模态地理数据，预测冷启动站点的骑行需求；(2) 引入新型加权公共交通可达性等级（wPTAL），结合预测骑行需求与传统指标进行综合评估；(3) 提供兼顾潜在客流量与公平性的新站点布局建议。以纽约市为例，研究揭示了历史上被忽视社区中低收入与少数族裔群体的通达性显著短板。结果表明，基于wPTAL指导的新站点布局能有效降低经济与人口因素导致的出行不平等。本研究为城市规划者提供了促进交通公平、提升弱势社区生活质量的实际路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。