Is access enough? A spatial and demographic analysis of one-way carsharing policies and practice
Bibliographic record
Abstract
For low-income individuals, carsharing services that provide short-term, on-demand access to a fleet of shared vehicles may be a viable, low-cost alternative to personal vehicle ownership, yet the demographics of carsharing users often reflect higher income groups. Our objectives in this research study are: 1) quantify the benefits of carsharing for low-income users; 2) evaluate the effectiveness of policies targeting spatial access (e.g., parking policies, designation of service areas); and 3) review other social equity initiatives taken by carsharing operators (e.g., low-income discounts, educational training) and impacts on user demographics. We use two one-way, free-floating carsharing services as case studies: a survey of GIG (Get In and Go) Car Share users in Oakland, CA from 2018 (n = 218), and a survey of car2go users (now called ShareNow) in five North American cities from 2015 (n = 9497). We analyze these surveys using descriptive statistics, hypothesis testing, and geographic information systems (GIS) mapping. We find significantly more low-income respondents in Oakland do not have a personal vehicle (70%) compared to high-income respondents (44%). Meanwhile, 62% of our sample was Caucasian, and 41% earned an annual household income of more than $100,000, similar to results of the car2go survey from 2015, indicating that the demographics of one-way carsharing users have not changed much over time. We find that policies expanding carsharing service areas into equity priority zones can potentially attract new members. Washington, D.C. and Oakland included equity service area requirements in carsharing permits and had a higher percent of users residing in equity priority zones (21% and 33%, respectively), compared to Calgary and Vancouver that did not (12% and 4%, respectively). However, the demographics of carsharing users still do not reflect the demographics of equity zones. Policies to increase spatial access of carsharing are insufficient on their own to improve social equity in carsharing. Based on findings from previous carsharing equity pilots, policy initiatives such as discounted memberships for low-income users and hands-on educational outreach are needed to advance social equity.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".