Planning and Design of Equitable Free-Floating Bike-Sharing Systems Implementing a Road Pricing Strategy
Bibliographic record
Abstract
Promoting a more sustainable development of urban realities is one of the most important goals of the recent decades. One possible strategy to undertake in order to achieve this objective is the implementation of a road pricing: tolling private cars when passing by certain roads of the network could be a way to tone down the traffic congestion and, at the same time, encourage the shifting towards more sustainable means of transport. In this context, we suggest a method to distribute in a fair way the outcomes/revenues of this pricing strategy. In particular, we propose to design a free-floating bike-sharing system whose resources could be allocated in the territory according to spatial and social equity principles. The relation between the amount of the tolls, the number of days of application of the policy, and the pursued equity is investigated, and both a numerical application (to a test network and to a real case study) and a sensitivity analysis in support of the method are enclosed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".