What influences intention to use a first-mile/last-mile automated shuttle service in a suburban area? A case study in Toronto, Canada
Bibliographic record
Abstract
We surveyed the public in 2021 about a temporary first-mile/last-mile (FMLM) automated shuttle trial (planned for operation on public roads) in Toronto, Canada, before its deployment for public use. Our objectives were to investigate predictors of intention-to-use in a mixed traffic context in Canada and whether factors affecting the likelihood of trying the shuttle differed from those affecting the intended frequency of use. Our results showed that higher perceived usefulness, positive attitude towards the service, and higher trust in the shuttle capabilities significantly predicted both measures, but age was a significant (negative) predictor only for the intended frequency of use. This difference in demographic effects for the two examined measures suggests that future research should assess intention-to-use in more detail. Our results can also inform strategies to promote future automated shuttle trials. For example, informational campaigns to promote trust in the shuttle’s capabilities and highlight the benefits of the service may improve intention-to-use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".