Data-driven analysis of optimal repositioning policy in bike sharing systems
Bibliographic record
Abstract
In this study, we analyze the impact of optimal repositioning policy on the efficiency of bike sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of a sharing system. We further extend this model to consider demand uncertainty under a stochastic setting. The proposed model is then implemented on the real-world bike-sharing data of New York City, Toronto, and Vancouver. After finding the optimal repositioning policies, we analyze the effect of repositioning on the profit, fulfilled demand, number of required vehicles, and utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of a sharing system under these performance measures. In particular, our analyses show that 32% more profit can be obtained and up to 41% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size by up to 61% and increase the average utilization rate of the vehicles by up to 21% by employing repositioning.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".