Carsharing Versus Bikesharing
Bibliographic record
Abstract
Shared mobility services such as carsharing and bikesharing have gained significant traction in recent years. The services offer efficiency and flexibility to their members while providing benefits to society. In fall 2013, two origin–destination web surveys were carried out on carsharing and bikesharing members in Montreal, Quebec, Canada. These data were used to analyze the typical travel behaviors of members of one or both services. Service provider data were supplied to complement the analyses. The study controlled for factors such as age, gender, home location, and intensity of use of the service. Person and household characteristics showed that bikesharing users differed by being younger, more often male, and more connected (smartphones), and having a higher income. Carsharing users possessed more transit passes, had driving licenses in a higher proportion, and belonged to households with more children and fewer cars. Differences were also found when the intensity of the use of the service was accounted for. On travel behaviors, the study analyzed mode share when the bikesharing service was in operation and when the service ceased operations. On the former, both groups had high shares of public transit and walking, but bikesharing users were more car (driver)-oriented and carsharing members had a higher use of bikes. On the latter, carsharing users increased their use of walking, and bikesharing users increased their use of cars (driver). Finally, the study used a multinomial logit model to evaluate the performance of several variables on the odds of being a carsharing-only member, a bikesharing-only member, or a member of both services.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".