Document Type

Article

Publication Date

3-2025

Keywords

Shared micromobility ; Financial barrier ; Technical barrier ; Cultural barrier ; K-prototype clustering

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.

Creative Commons License

Creative Commons Attribution-No Derivative Works 4.0 International License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 International License.

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