Understanding When Carsharing Displaces Vehicle Ownership
Bibliographic record
Abstract
In this paper, the authors analyzed the factors and conditions associated with car owners who chose carsharing (CS) over car ownership. The authors used over 3,400 responses to a survey by Metro Vancouver directed at members of three different carsharing services. Our bivariate analysis showed that out of 883 respondents who reduced their vehicle ownership after joining a carsharing service, 70% became zero vehicle households. This suggests that, in these cases, access to CS services fully substituted private vehicles. According to the regression analysis, households who tended to reduce their vehicles after joining CS were 1) single households 2) those owning multiple vehicles prior to joining CS and, 3) those living in rental housing. On the other hand, households who kept their vehicles, even after joining CS, tended to have family members working outside of home. In addition, our logit regression analysis showed that households picking up cars at locations within apartment/townhouse complex, locations close to work/school, and locations close to transit stations were less likely to relinquish personal vehicles. The expansion of CS services in these locations has been a policy target by municipal governments. If the goal of public policy is to reduce private vehicle ownership, the authors might need to explore other strategies. The authors recommend conducting follow-up surveys to capture the effect in longer time frame and in other area, such as outside of urban core. The regression analysis also found that households who reduced vehicle ownership were twice as likely to report environmental awareness as a motivation than other CS households. Considering a strong positive relationship between the intention of cost saving by CS and vehicle ownership reduction (over 3.0 odds ratio), advertising CS as a cost effective and environmental friendly transportation option would be a potent strategy to implement CS as a vehicle ownership reduction measure.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".