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Record W4405370585 · doi:10.1080/19427867.2024.2433337

Users’ perceptions toward autonomous vehicles: case study in Alberta, Canada

2024· article· en· W4405370585 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransportation Letters · 2024
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsCarleton UniversityUniversity of Calgary
Fundersnot available
KeywordsTRIPS architectureWillingness to payPreferenceAffect (linguistics)PerceptionAutomationBusinessPsychologyDemographic economicsTransport engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

This study investigates perceptions and attitudes toward autonomous vehicles (AVs) using an online stated preference (SP) survey conducted in Alberta, Canada. It explores the effect of different sociodemographic, external, and psychological factors on users’ attitudes toward AVs. Additionally, factors contributing to people’s willingness to pay for AVs were evaluated. The results indicate that sociodemographic factors, external factors, and people’s perceptions significantly affect people’s willingness to pay for automation. Level 3 of automation is shown to have a positive effect on the drivers’ utility of driving for commuting and non-commuting trips, while other levels of automation were found negatively affecting the utility of driving. Men were generally more willing to pay for AVs, particularly for commuting trips, while weather conditions, especially icy roads, posed significant concerns about AV reliability. Middle-aged drivers exhibited the highest willingness to pay (WTP) for higher levels of automation, emphasizing the potential early adoption among this group.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.025
GPT teacher head0.325
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it