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

• URL: http://arxiv.org/abs/2506.15113v1
• Date: Wed, 18 Jun 2025 03:31:07 GMT
• Title: Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning
• Authors: Min Namgung, JangHyeon Lee, Fangyi Ding, Yao-Yi Chiang,
• Abstract summary: Bike-sharing systems (BSS) can bridge equity gaps by providing affordable first- and last-mile connections.<n>We introduce Transit for All (TFA), a spatial computing framework designed to guide the equitable expansion of BSS.<n>Using NYC as a case study, we identify transit accessibility gaps that disproportionately impact low-income and minority communities.
• Score: 6.20584161498609
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network (GCN) architecture to model link-level bicycling volumes.<n>We benchmark it against traditional machine learning models, such as linear regression, support vector machines, and random forest.<n>Our results show that the GCN model outperforms these traditional models in predicting Annual Average Daily Bicycle (AADB) counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• Smart Recommendations for Renting Bikes in Bike Sharing Systems [1.5115914900997285]
  Vehicle-sharing systems have become increasingly popular in big cities in recent years. One of their advantages is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. Agglutination problems -- where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others -- are quite common in such systems. We present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems.
  arXiv  Detail & Related papers  (2024-01-22T19:29:33Z)
• Towards Understanding the Benefits and Challenges of Demand Responsive Public Transit- A Case Study in the City of Charlotte, NC [3.678540247562326]
  This study investigates the challenges faced by transit-dependent populations in Charlotte, NC. Our research initially evaluates critical issues such as extended wait times, unreliable schedules, and limited accessibility. This evaluation included an analysis of the existing Charlotte Area Transit System (CATS) mobile applications and the exploration of user acceptance for a proposed smart, on-demand transit technology.
  arXiv  Detail & Related papers  (2023-04-09T03:10:36Z)
• Equity Promotion in Public Transportation [18.057286025603055]
  We propose an optimization model to study how to integrate the two approaches together for equity-promotion purposes. We have designed a linear-programming (LP) based rounding algorithm, which proves to achieve an optimal approximation ratio of 1-1/e. Experimental results confirm our theoretical predictions and demonstrate the effectiveness of our LP-based strategy in promoting social equity.
  arXiv  Detail & Related papers  (2022-11-26T10:06:00Z)
• Equity Scores for Public Transit Lines from Open-Data and Accessibility Measures [0.3058685580689604]
  Current transit suffers from an evident inequity: the level of service of transit in suburbs is much less satisfying than in city centers. To achieve sustainability goals and reduce car-dependency, transit should be (re)designed around equity.
  arXiv  Detail & Related papers  (2022-09-30T22:58:11Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Data [4.869953137750582]
  This paper proposes a new solution to streamline and facilitate the process of such planning by using spatial embedding methods. Based on publicly available data from OpenStreetMap, and station layouts from 34 cities in Europe, a method has been developed to divide cities into micro-regions.
  arXiv  Detail & Related papers  (2021-11-01T14:56:49Z)
• Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing [84.47891614815325]
  This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We introduce novel notions of fairness and stability in P2P ridesharing. Results suggest that fair and stable solutions can be obtained in reasonable computational times.
  arXiv  Detail & Related papers  (2021-10-04T02:14:49Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems [45.45179250456602]
  We propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. By dividing the urban area into regions according to flow density, we first model the bike flows between regions with graph regularized sparse representation. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems.
  arXiv  Detail & Related papers  (2020-04-13T05:31:50Z)
• Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction [116.66657468425645]
  We propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system. A similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation.
  arXiv  Detail & Related papers  (2020-01-14T16:47:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.