Unraveling the Travel Behavior of Carsharing Members from Global Positioning System Traces
Bibliographic record
Abstract
As carsharing becomes increasingly popular throughout the world, it would be of interest to understand better the underlying characteristics of the trips made by members when they use the cars. To date, few studies have reported carsharing trip details. This paper presents a methodology to analyze three components of a station-based carsharing member's journey: the locations of the stops, the attributes of the trips, and the characteristics of the trip chains. The method is based on the processing of Global Positioning System traces collected onboard car-sharing vehicles; a 5-min stop criterion is used to divide the trip chains into separate trips. The case study involves the Communauto system in the Greater Montreal area, Quebec, Canada. The study shows that carsharing members make more trips within their trip chains than typical car owners do. However, those trips are shorter and are often made for purposes other than work (shopping or visiting, for example). Members tend to maximize the use of the cars during the rental period (the members are on the move up to 50% of the time for short trip chains and 30% of the time for longer trips).
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".