Analysis of Individuals’ Acceptance and Influencing Factors for Young Users of Autonomous Vehicles Using the Hybrid Choice Model
Bibliographic record
Abstract
Autonomous vehicles (AVs) are a vital direction for intelligent transportation; nevertheless, the current research is insufficient, and the aspects and mechanisms that influence individuals’ adoption of AVs require additional investigation. This study examines the acceptance of the popularity of AVs from three perspectives: personal-psychological attributes, travel characteristics attributes, and latent variables. The descriptive statistical analysis revealed that the acceptance rate of AVs was 54.6% based on 304 valid questionnaires received through online questionnaires. And the proportion of the 18∼50 group in the article reached 92.8%; thus, this study takes the young group as the object to investigate the acceptance of AVs. A quantitative analysis of each factor’s impact on the acceptability of AVs was conducted using the hybrid choice model (HCM), which was utilized to observe the link between latent variables. Results from parameter estimates demonstrate that the HCM’s fitting impact when latent factors are taken into account is superior to that when latent variables are not taken into account. When latent variables are taken into account, the associated goodness ratio coefficient rises by 0.2337 to 0.2898, which is greater than the model as a whole. The three factors with the highest impact on AV acceptability among the five latent variables were attitude toward use, sense of use gain, and perceived trust, with matching z-test values of 2.42, 2.44, and 2.12, respectively. The development and marketing of AVs by pertinent businesses and government agencies would benefit greatly from this research as a source of reference.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".