Exploring Service Usage and Activity Space Evolution in a Free-Floating Carsharing Service
Bibliographic record
Abstract
This paper proposes to empirically investigate the members’ behaviors over time in a free-floating carsharing system. With a continuously evolving service in terms of service area and fleet size, member usage intensity and activity space are explored with passive data streams. Members are labeled according to their usage intensity for various periods of analysis. Results show an increase in higher usage intensity classes and a change in demographic composition of new members adopting the service over time: new members are younger and a parity between both genders is reached. Activity space investigation shows that members perform a fair share of their trips to return home and that ultra-frequency members seem to perform a substantial number of symmetric trips, meaning they are more inclined to use free-floating cars for commute-like trips. Interaction with the metro network is also investigated, with a proportion of members using free-floating carsharing to access stations. When looking at the activity space formed by members’ trip ends, users seem to constantly discover new portions of the service area. Multiple clusters of activity locations are determined for each member, with a recurrence level showing a fair amount of variability among members.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".