Propensity to participate in a peer‐to‐peer social‐network‐based carpooling system
Bibliographic record
Abstract
Summary This study examines the potential for a social network peer‐to‐peer‐based carpooling system called FacePorter for the University of Calgary staff and students. In this study, a survey that combined both revealed and stated preferences was designed and distributed randomly among students and staff. The survey consisted of a sample of 210 responses, which were divided into two groups of stated preference respondents: (i) auto drivers, who were given the choice between driving alone and carpooling as drivers; and (ii) transit riders, who were given the choice between public transport and carpooling as passengers. A binomial logit model and two ordinal logit models (one for ride offerors and one for ride seekers) were calibrated to examine the impacts of various examined socio‐economic, psychological, and travel characteristic variables on the propensity to participate in the hypothetical carpooling program. The results of the models clearly demonstrated that many factors have significant impacts on FacePorter demand: occupation, income, marital status, working schedule flexibility, trip characteristics (i.e., distance, travel time, and number of required transfers when riding transit), weather condition, carpooling fee, perceived rider and driver profiles, and carpooling fee would significantly influence the market demand of the examined carpooling system. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".