Ridesharing, Active Travel Behavior, and Personal Health: Implications for Shared Autonomous Vehicles
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Summary
This study investigates the complex relationship between ridesharing, active travel behavior, and personal health, specifically to understand the implications of future Shared Autonomous Vehicle (SAV) deployment. The research is motivated by the dual nature of AVs: while they promise significant safety improvements and reduced emissions, they also risk decreasing physical activity by replacing walking and biking with door-to-door service. This reduction in active travel could exacerbate non-communicable diseases, which account for two-thirds of global deaths. The study aims to identify health-related outcomes associated with AV adoption and determine which built-environment settings are likely to host early adopters, thereby informing policy to maximize benefits and mitigate adverse health impacts. The methodology combines a comprehensive literature review with empirical analysis using data from two metropolitan statistical areas: Chicago and Indianapolis. The literature review, guided by a conceptual model of travel behavior and health, synthesized 74 studies on factors such as personal characteristics, residential choice, physical activity, well-being, environmental impacts, and safety. For the empirical component, the authors utilized survey data to examine the relationship between ride-hailing, active travel, and health status. They employed Multivariate Probit (MVP) models to estimate the associations between shared/active travel modes and conventional modes. Additionally, multivariate cluster analysis and spatial tools were used to identify individual and location-based characteristics—such as health status and built-environment features—that facilitate AV adoption, particularly in highly transportation-disadvantaged areas. The findings highlight that personal characteristics significantly influence travel behavior and health outcomes. The literature review indicates that AVs could benefit rural populations, the elderly, carless households, low-income groups, and individuals with special needs by providing affordable, accessible mobility. However, the empirical results underscore a critical trade-off: while shared mobility can reduce vehicle-miles traveled (VMT) and congestion, it often correlates with reduced walking time due to direct pick-up and drop-off services. The study identifies that built-environment factors, such as density, green space, and parking availability, play a crucial role in residential choice and travel behavior. The cluster analysis revealed distinct adoption categories, showing that early adopters are influenced by specific health and environmental contexts. The research suggests that without careful design, SAVs may encourage suburban sprawl and reduce opportunities for active travel, potentially leading to increased obesity and related health issues. The significance of this work lies in its comprehensive framework for understanding the health externalities of autonomous mobility. The authors conclude that while SAVs offer substantial benefits in safety, equity, and environmental quality, their potential to displace active travel poses a serious public health risk. The study recommends specific strategies to internalize societal externalities, such as designing built environments that encourage active travel alongside shared mobility and ensuring equitable access for disadvantaged populations. By linking transportation innovations to health determinants, the research provides critical insights for policymakers and planners to structure SAV markets in ways that preserve physical activity levels and promote overall well-being, rather than inadvertently worsening health outcomes.
Key finding
Ridesharing is associated with reduced active travel, suggesting that SAV deployment could negatively impact public health by limiting physical activity opportunities.
Methodology
mixed_methods
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: observational prevalence