Bibliographic record
Abstract
The time that buses spend waiting for passengers to board can be a significant portion of a bus route's overall running time. A key determinant of boarding time is the number of doors through which passengers are permitted to board. Transit agencies that allow boarding through all doors, instead of just through the front door, typically enjoy decreased boarding times and decreased running times. This study focused on the feasibility of an all-door boarding policy for La Société de transport de Montréal (STM), the public transit agency of Montreal, Canada. The potential benefits of such a policy were assessed through three main steps. First, a selection methodology was developed to determine which of STM's bus routes would benefit most from various all-door boarding strategies. Second, a multivariate regression analysis was implemented with STM's archived automatic vehicle location and automatic passenger counter data to estimate the dwell and running-time savings that would result under various implementation scenarios. Third, a sensitivity analysis was developed to demonstrate the savings associated with implementing the policy. The findings showed that all-door boardings could yield substantial savings in running time, with morning peak savings as much as 15.8% on the best routes. In many cases, the running-time savings were enough to remove a bus from a route while still maintaining existing frequencies. The findings from this research may be beneficial for transit planners and operators since the presented methodologies show substantial savings from all-door boarding and can be adopted by other transit agencies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.017 |
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".