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Record W7025287780

What about free-floating carsharing? A look at the Montréal case

2014· article· en· W7025287780 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2014
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityPublic transportService (business)Relation (database)Car ownershipCentral business districtLevel of service
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT: In recent years carsharing has become a practical, ecological, and economical alternative to private car ownership all around the world. Traditional carsharing is station-based, but recently new types of shared services have appeared, and one of these is free-floating carsharing. It is more flexible, but what is its impact on user behavior? This paper aims to characterize the use of the free-floating carsharing service in central Montreal (Canada). We compare the use of the traditional, station-based service and the new service. Some people are members of both services, so we are able to examine the specific contribution of each service to meeting travel needs. We also explore the impact of the introduction of this new transportation alternative. The results show that, compared with traditional carsharing, more women are members of the free-floating service, and the trip distances and durations are much shorter. Shopping is the most important activity, and there is a concentration of trip ends near the central business district in the midday period. When asked what mode they would have used in the absence of the free-floating service, the users mentioned public transit, taxis, and walking; the popularity of these alternatives varies, probably in relation to seasonal changes. Further studies are required to measure the environmental impact of this new transportation mode.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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