Roadblocks to ride: Unraveling barriers to access shared micromobility systems in the United States

Farzana Mehzabin Tuli , Suman Kumar Mitra
{"title":"Roadblocks to ride: Unraveling barriers to access shared micromobility systems in the United States","authors":"Farzana Mehzabin Tuli ,&nbsp;Suman Kumar Mitra","doi":"10.1016/j.jcmr.2024.100055","DOIUrl":null,"url":null,"abstract":"<div><div>Shared micromobility services are experiencing rapid expansion in the United States and Europe, yet certain user groups, particularly low-income and disadvantaged individuals, face significant barriers related to financial, technical, and cultural factors. This study provides a comprehensive analysis of these barriers within the US by examining bike-sharing, shared e-scooters and programs offering both services. Data was meticulously collected from diverse sources, including official bikeshare provider websites, municipal transportation sites, program reports, local news articles, and mobile applications. This comprehensive data collection methodology provides a thorough representation of 458 shared micromobility systems, encompassing all services available since the inception of shared micromobility in the US. To elucidate the specific barriers faced by users, we employed the K-Prototype clustering methodology, an unsupervised machine learning technique capable of handling datasets with both numerical and categorical features. This approach enabled us to uncover distinct patterns and groupings among shared micromobility services based on these barriers. Our analysis identified four distinct clusters: Cluster 1 faces low technical but high financial barriers; Cluster 2 excels in financial accessibility but struggles with technical barriers; Cluster 3 experiences moderate barriers with progress in reducing financial and technical challenges but still needs improvement; and Cluster 4 encounters high barriers across financial, technical, and cultural dimensions. Additionally, an in-depth analysis of these clusters is performed, considering the percentage share of bikesharing and shared e-scooter services, city sizes, regional distribution, fleet size, launching year, deployment, and operations status. The outcomes of this analysis reveal that larger cities exhibit a higher share of 'moderate barrier' (Cluster 3) systems that are currently active in the pilot phase. In contrast, shared micromobility systems from mid-size, small mid-size, and especially small cities in the US experience 'high barrier' (Cluster 4) issues the most, often with smaller fleet sizes (less than 250). Identifying these clusters is crucial for enabling targeted interventions. Rather than applying a broad, one-size-fits-all approach, policymakers and planners can develop tailored strategies that address the unique challenges of each cluster. This targeted approach ensures that interventions are more effective and equitable, ultimately improving access to shared micromobility services for all users.</div></div>","PeriodicalId":100771,"journal":{"name":"Journal of Cycling and Micromobility Research","volume":"3 ","pages":"Article 100055"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cycling and Micromobility Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950105924000469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Shared micromobility services are experiencing rapid expansion in the United States and Europe, yet certain user groups, particularly low-income and disadvantaged individuals, face significant barriers related to financial, technical, and cultural factors. This study provides a comprehensive analysis of these barriers within the US by examining bike-sharing, shared e-scooters and programs offering both services. Data was meticulously collected from diverse sources, including official bikeshare provider websites, municipal transportation sites, program reports, local news articles, and mobile applications. This comprehensive data collection methodology provides a thorough representation of 458 shared micromobility systems, encompassing all services available since the inception of shared micromobility in the US. To elucidate the specific barriers faced by users, we employed the K-Prototype clustering methodology, an unsupervised machine learning technique capable of handling datasets with both numerical and categorical features. This approach enabled us to uncover distinct patterns and groupings among shared micromobility services based on these barriers. Our analysis identified four distinct clusters: Cluster 1 faces low technical but high financial barriers; Cluster 2 excels in financial accessibility but struggles with technical barriers; Cluster 3 experiences moderate barriers with progress in reducing financial and technical challenges but still needs improvement; and Cluster 4 encounters high barriers across financial, technical, and cultural dimensions. Additionally, an in-depth analysis of these clusters is performed, considering the percentage share of bikesharing and shared e-scooter services, city sizes, regional distribution, fleet size, launching year, deployment, and operations status. The outcomes of this analysis reveal that larger cities exhibit a higher share of 'moderate barrier' (Cluster 3) systems that are currently active in the pilot phase. In contrast, shared micromobility systems from mid-size, small mid-size, and especially small cities in the US experience 'high barrier' (Cluster 4) issues the most, often with smaller fleet sizes (less than 250). Identifying these clusters is crucial for enabling targeted interventions. Rather than applying a broad, one-size-fits-all approach, policymakers and planners can develop tailored strategies that address the unique challenges of each cluster. This targeted approach ensures that interventions are more effective and equitable, ultimately improving access to shared micromobility services for all users.
共享微出行服务在美国和欧洲正在快速扩张,但某些用户群体,特别是低收入和弱势群体,面临着与金融、技术和文化因素相关的重大障碍。本研究通过考察共享单车、共享电动滑板车和提供这两种服务的项目,对美国境内的这些障碍进行了全面分析。数据从各种来源精心收集,包括官方共享单车提供商网站、市政交通网站、项目报告、当地新闻文章和移动应用程序。这种全面的数据收集方法提供了458个共享微交通系统的全面代表,包括自美国共享微交通开始以来可用的所有服务。为了阐明用户面临的具体障碍,我们采用了K-Prototype聚类方法,这是一种能够处理具有数值和分类特征的数据集的无监督机器学习技术。这种方法使我们能够发现基于这些障碍的共享微移动服务之间的不同模式和分组。我们的分析确定了四个不同的集群:集群1面临低技术但高金融壁垒;集群2在金融可及性方面表现优异,但仍面临技术障碍;第三组在减少财政和技术挑战方面取得进展,遇到了适度障碍,但仍需要改进;集群4在金融、技术和文化方面遇到了很高的障碍。此外,考虑到共享自行车和共享电动滑板车服务的百分比份额、城市规模、区域分布、车队规模、推出年份、部署和运营状态,对这些集群进行了深入分析。这一分析的结果表明,目前在试点阶段活跃的“中等障碍”(集群3)系统在较大城市中所占比例更高。相比之下,来自美国中型、中小型城市,尤其是小城市的共享微型交通系统遇到的“高障碍”(第4类)问题最多,通常车队规模较小(少于250人)。确定这些集群对于实现有针对性的干预至关重要。决策者和规划者可以制定量身定制的战略,解决每个集群的独特挑战,而不是采用广泛的、一刀切的方法。这种有针对性的做法确保干预措施更加有效和公平,最终改善所有用户获得共享微型交通服务的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信