Bibliographic record
Abstract
Carsharing systems are the focus of an increasing number of researches. In addition to gaining new members every week, new carsharing systems are being launched around the Globe. In Montreal, carsharing in now part of the transportation strategies to alleviate congestion and contribute to the overall aim of reducing the dependency towards the individual car. Thanks to a continuous partnership with Communauto, the Quebec carsharing operator, it has been possible, in the recent years, to provide quantitative assessment of various aspects of the system, both regarding supply and demand. This paper builds on these previous researches and concentrates on the systematic analysis of the behaviors of members, in terms of transactions and kilometers travelled. Data mining techniques are used to classify members according to various temporal units expressing their behaviors in order to propose a typology. Results show that, with respect to frequency of use, there are two main types of carsharing members in Montreal, high frequency users (≈ 2.2 transactions per week) and low frequency users (≈ 0.4 transactions per week), the later gathering 86% of the members. Results also show, still based on frequency, that there are five types of weekly patterns and that members have a dominant weekly pattern that is, in average, representative of 62% of their weeks. This study shows that weekly patterns change namely during the holiday periods (summer months, December-January). With respect to weekly distance travelled by members, two clusters are also identified, one gathering 87% of the members with an average of 14.3 km travelled per week and the other ones related to higher averages (76.8 km per week). Other classifications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".