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Record W4384408061 · doi:10.1080/03081060.2023.2230969

Communication and mobility issues of visually impaired pedestrians with connected autonomous vehicles

2023· article· en· W4384408061 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.

Bibliographic record

VenueTransportation Planning and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsCNIB FoundationUniversity of Toronto
Fundersnot available
KeywordsPedestrianStructural equation modelingContext (archaeology)Computer scienceVisually impairedConfirmatory factor analysisLatent variableEconometricsTransport engineeringArtificial intelligenceHuman–computer interactionEngineeringMachine learningMathematicsGeography

Abstract

fetched live from OpenAlex

This paper presents an econometric modelling framework to unravel the communication and mobility issues of visually impaired pedestrians in the context of connected autonomous vehicles (CAVs). The research uses a dataset collected through a tailor-made stated-preference survey given to visually impaired pedestrians and provides evidence-based recommendations on communication techniques. The recommendations are based on the findings of a structural equation model (SEM) estimated using the survey data. The latent factors ‘safety and security’ and ‘importance of hearing’ are generated using a confirmatory factor analysis embedded in the SEM. The results from the model show that these two factors have negative influences on how much a visually impaired pedestrian trusts the use of CAVs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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