Object-Oriented Analysis of Carsharing System
Bibliographic record
Abstract
Carsharing systems are gaining new members every month. However, few studies formally define the system and illustrate the systematic processing of administrative data sets to estimate indicators regarding both demand and supply objects. The first outcome of this research is the definition of the object model for a carsharing system. Rich transaction data sets, generally used for the production of monthly bills, are used to estimate indicators describing how the carsharing system is used in the Montreal area of Quebec, Canada. Indicators describing the main objects of the system—members, trip chains (transactions), cars, and stations—are estimated by using continuous data. The demand object analysis focuses on the study of members and their trip chains using the shared cars. The analysis shows that carsharing members are younger than the overall population, with an overrepresentation of 25- to 39-year-olds. The persistency of active members within the carsharing system is estimated at around 60% after 4 months and 50% after 12 months. The supply–objects analysis focuses on the study of cars and stations. Spatial dispersion of members with respect to stations used and typical use of cars is illustrated over long periods. With the increase in the number of members, transactions, cars, and stations, carsharing organizations need to find new ways to manage growth and optimize their networks. A clearer understanding of how their systems are used will help them develop enhanced planning and modeling abilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".