Shifting gears for the automated vehicle: findings from focus groups in the Greater Toronto and Hamilton area
Bibliographic record
Abstract
The emergence of automated vehicles (AVs) may potentially transform the ways in which individuals travel, and integrating the impacts and opportunities of AVs into travel demand forecasts and transportation planning will be important for wise decision-making. This paper presents findings from focus groups designed to explore Greater Toronto and Hamilton Area (GTHA) residents’ interest and responses to AVs. Results suggest that the general public is interested in AVs and eager to learn more, and that individual travel habits carry significant weight. The findings from this study emphasize the planner’s responsibility to engage in consultations internally, within organizations and across departments, as well as externally, with stakeholders and members of the community. On-going internal and external engagements will first allow organizations to prepare and consolidate appropriate strategies for this disruptive technology, and second, will keep the public sphere informed and engaged in the implementation of AVs. Key words: automated vehicles, planning, transportation, travel behaviour, focus groups.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".