Factors That Influence Older Canadians’ Preferences for using Autonomous Vehicle Technology: A Structural Equation Analysis
Bibliographic record
Abstract
Accessible and safe mobility is critical for those aged 65 years and older to maintain their health, quality of life, and well-being. Being able to move beyond one’s home and participate in activities in older adulthood requires consideration of both transportation needs and preferences. This paper aims to address a gap in evidence with respect to understanding factors that can affect older adults’ perceptions and willingness to use autonomous vehicles. In addition, it examines how these factors compare with those of younger adults to better understand the potential implications of this technology on mobility and quality of life. Using responses of those aged 65+ to a national survey of Canadians, structural equation modeling (SEM) was used to identify and quantify factors significantly associated with older adults’ willingness to use autonomous vehicles. The SEM results suggest that factors such as using other modes of transit (e.g., sharing rides as passenger, bicycle, public transit, commuter rail, ride and car sharing) as well as distance traveled by automobile, income, gender (being male), and living in urban areas, were all positively associated with older adults’ perceptions of using autonomous driving features. The findings also suggest that older Canadians are more concerned about autonomous vehicles than younger Canadians. This study provides valuable insights into factors that can affect the preferences of Canadians when it comes to autonomous technology in their automobiles. Such results can inform the way in which transportation systems are designed to ensure the needs of users are considered across both age and ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".