Sustainable mobility solutions: a pre-implementation questionnaire study for carsharing
Bibliographic record
Abstract
This article presents a pre-implementation questionnaire study for a new carsharing system in La Rochelle, France. The questionnaire seeks responses on four main factors: profile of the users, transportation behaviour of users, willingness to use carsharing and preferences for new carsharing stations in La Rochelle. The target groups surveyed were city residents, tourists, businessmen, students and city transport organisations namely city of La Rochelle, CdA La Rochelle and Conseil Generale du Charente Maritime. A total of 500 questionnaires were distributed in-person and a response rate of 81.4% was received. The findings of this study reveal useful information on the feasibility of the offer, user motivations and service design aspects for the new carsharing system. It was found during the study that appropriate marketing strategies and awareness campaigns are required to inform people about carsharing. Other areas of improvement are ensuring availability of vehicles at times, easy access to carsharing stations, easy return procedure for vehicles after use, low trip costs and providing vehicles suited to user needs. These findings have strengthened the need for a new carsharing system in the city and have identified appropriate operational factors for its implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".