Participation in Shared Mobility: An Analysis of the Influence of Walking and Public Transport Accessibility to Vehicles on Carsharing Membership in Montreal, Canada
Bibliographic record
Abstract
In the context of sustainable mobility policies, carsharing services have gained importance as an alternative to personal vehicles. In an effort to increase the adherence to and use of such services, several studies have explored the key factors that determine use and membership. Although the ease with which individuals can access shared vehicles appears to be a central determinant, few studies have specifically investigated how to measure station and vehicle accessibility. To fill this gap, this study seeks to systematically assess and compare the contribution of different accessibility indicators to modeling carsharing membership rate, using 2016 data from the Montreal carsharing company Communauto and from the Canadian census. Three indicators of accessibility to in-station vehicles are generated: walking only, public transport only, and multimodal accessibility (walking and public transport), considering a variety of travel time thresholds and cost functions. A linear regression model is then generated to assess the contribution of the different indicators to modeling membership rates, while controlling for socio-economic and commuting characteristics. The results show that walking accessibility, within 20 minutes, and public transport accessibility, within 40 minutes, are both key determinants of membership rate and in a complementary manner. The influence of public transport accessibility is positive and highest when walking accessibility is low. The results also demonstrate that the use of a cumulative or weighted-opportunity indicator is equally sound from an empirical perspective. The study is of relevance to researchers and planners wishing to better understand and model the influence of vehicle accessibility.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".