Can Monetary Incentive Regulate Boarding Passenger Distribution on the Metro Station Platform?
Bibliographic record
Abstract
To equilibrate the passenger distribution on the metro platform and carriage, a monetary incentive policy was explored in this paper; a discount on travel fare was provided to motivate metro passengers to queue for boarding in the noncrowded areas on the platform. The congested state is evaluated combined with the passenger distribution in the upcoming metro carriage. The utility of metro passengers and companies caused by the monetary incentive policy was analyzed, and the binary logit model was used to relate the utility to the passenger’s willingness to move from crowded areas to noncrowded ones. With data acquired from the questionnaire survey, a regression analysis was employed to explain the variation in passengers’ willingness to move as a function of discount level as well as personal and trip characteristics. The regression results show that effect of incentive discount is greater on female passengers and elderly passengers. A 10% discount can motivate most passengers aged over 40, and a 30% discount works on most female passengers. According to the different levels of passenger sensitivity, a particular discount can be determined to motivate a specific proportion of passengers to move and achieve the regulation of passenger distribution on the metro station platform and metro carriage.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".