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Record W2471645431 · doi:10.1016/j.trpro.2016.05.232

Public Views towards Implementation of Automated Vehicles in Urban Areas

2016· article· en· W2471645431 on OpenAlexaboutno aff
Jinan Piao, Mike McDonald, N.B. Hounsell, Matthieu Graindorge, Tatiana Graindorge, Nicolas Malhéné

Bibliographic record

VenueTransportation research procedia · 2016
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsTaxisTransport engineeringPublic transportHeadwayQuarter (Canadian coin)Computer sciencePoolingComputer securityBusinessEngineeringGeography

Abstract

fetched live from OpenAlex

In this paper, a study on public opinions towards implementation of automated vehicles in urban areas is reported which is based on a survey in La Rochelle. This was a part of the evaluation activities on the automated vehicle demonstrated in the city. According to the surveys, public attitudes were positive towards implementation of automated buses in urban areas. The most attractive benefit of automated buses would be lower bus fares because of no driver costs. About two thirds of people surveyed would consider taking automated buses if both automated and conventional buses were available on a route. Passenger security would be one of most concerned issues for automated buses especially during night time services. The public attitudes towards automated cars in urban areas were also positive. More than half of the people surveyed stated that they would consider using automated cars if they become available, with three quarters being interested in owning an automated cars, and one quarter in sharing automated cars through services such as car-sharing, car-pooling, or taxis.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.123
GPT teacher head0.386
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations246
Published2016
Admission routes1
Has abstractyes

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