Bibliographic record
Abstract
While market segmentation is widely used to study travel behaviour (Beirao & Cabral, 2008; Kieu et al., 2015), it has not been widely applied to study the public opinion of automated vehicles (AVs). Previous research has primarily used inferential statistics and has found that younger males living in urban areas who have higher income are likely to be more interested in AV use and more willing to pay (WTP) for such technology (Bansal et al., 2016; Hohenberger et al., 2016; Hudson et al., 2018; Schoettle & Sivak, 2014). This study differs from those in that it applies Two-Step Clustering Analysis, a market segmentation approach which highlights more complex linkages between consumer segments and adoption than is portrayed in conditional bivariate estimates. We identified five sample market segments: Retirees, Rural Workers, Students, Working Class, and Torontonians. These segments significantly differ one from another when cross-tabulated with intention of use and WTP for AV variables. Results suggest that market segmentation may be a viable technique for studying and targeting sub-groups of individuals to shape automated vehicle use, while also providing policymakers with means and insights for intervention. Since we found no differences in attitudes regarding AV modes, we conclude that AV adoption will highly depend on public opinion regarding AVs, and that interventions should focus on raising awareness and exposure to AV modes with the most societal benefits.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".