A new stochastic model for carsharing suited to free-floating
Bibliographic record
Abstract
In car-sharing, free-floating is becoming increasingly popular. It means that the shared cars are parked in the public space without dedicated parking spaces. For the operator, this solves the problem of parking space requirements. But the acute imbalance problem shows the need of stochastic modelling and analysis. In this paper, a new stochastic model adapted to free-floating is proposed, taking into account the sharing of public space between private and free-floating cars. As is generally the case, the model consists of dividing the service area into small zones, with free-foating car dynamics adapted to usage, meaning car reservation, one-way trip and no parking space reservation. The originality of our model is that, due to the presence of private cars, the capacity of a zone seen by free-floating cars is random. We show that, unlike in station-based car-sharing systems, it is not limited. In addition, a stochastic averaging principle governs the behavior of free-floating cars. We exhibit a phase transition between a non-saturated regime where free-floating cars can always be parked and a saturated regime where free-floating cars cannot find an available parking space with positive probability. This probability is entirely determined by the environment - parameters of private cars and public space- which means that the operator cannot act on the proportion of zones without available parking spaces. The solution of the dimensioning problem -finding the optimal feet size to minimize the number of zones without available free-floating cars or parking spaces- is completely different from that of station-based car-sharing which is a trade-of. It consists in claiming that the more free-floating cars there are in the system, the more satisfied users are, assuming always that private cars are much more numerous that free-floating cars.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".