Inclusive autonomous shuttles as public transportation options for older people: A qualitative study of user and service provider perspectives
Bibliographic record
Abstract
Background and objectives Barrier-free access to transportation allows older adults, especially those with mobility impairments, to maintain independence and community participation. Electric autonomous shuttles can enhance accessibility and energy efficiency of public transportation used by older riders. Little is known about potential users' and transportation service providers’ perceptions of the factors influencing use of shared autonomous vehicles by older adults, particularly those in regions with extreme weather. Research design and methods In this qualitative study, adults aged 60 years and older and service providers (e.g., administrators, staff) from local mobility organizations involved in supporting transportation services used by older adults participated in focus groups or individual interviews about community mobility and use of public transit, as well as their knowledge, beliefs, and understanding of shared autonomous shuttles. Data collection and analyses were informed using an adapted version of the Unified Theory of Acceptance and Use of Technology-2. Results Sixteen older adults and nine service providers participated. Findings suggest older adults’ perceptions and potential use of autonomous shuttles can be influenced by tiered and intersecting factors within the following contextual concepts: (1) Rider Characteristics (Micro-level); (2) The Envisioned Autonomous Shuttle (Meso-level); (3) Features of the Local Context (Macro-level); and (4) Societal Discourses (Mega-level). Discussion and implications Autonomous shuttles have the potential to be feasibly integrated into current public transportation systems. Such integration should consider vehicle and service design, including policies and implementation, built environment infrastructure, and public awareness of autonomous shuttle function to meet older adult needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".