Understanding the Factors Affecting Vehicle Usage and Availability in Carsharing Networks:A Case Study of Communauto Carsharing System from Montreal, Canada
Bibliographic record
Abstract
The novelty of carsharing as an alternative to private car in dense urban areas raises a number of questions regarding the logistics of operating a carsharing network.Subscribers of carsharing networks have been growing at a fast rate in recent years.This increase was accompanied by more complex problems due to changes in the demand and shortages in supply.This study seeks to determine the factors affecting vehicle usage and availability in the Communauto carsharing network in Montréal, Québec.Using data provided by the carsharing operator, a multilevel regression model focussing on vehicle usage and a logistic regression model focussing on vehicle availability were devised.The study determined that a number of factors have a major impact on either availability or usage.The number of vehicles parked at a station has the most effect on availability, with a great variation during the seasons.Vehicle usage is affected by average vehicle age, and by member concentration in the vicinity of the station.The findings from this research can be beneficial to transportation planners and engineers working with carsharing operators to build or expand their network.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".