Don’t stop just yet! A simple, effective, and socially responsible approach to bus-stop consolidation
Bibliographic record
Abstract
Bus-stop consolidation is one of the most cost-effective ways for a transit agency to improve the quality of their bus services. By removing unnecessary stops, buses will have reduced runtimes, which can lead to higher frequencies and/or fewer buses on a route. Unfortunately, current research on bus stop consolidation and stop spacing focuses on complex mathematical models that are difficult for agencies to apply, and that overlook many important real-world considerations. The goal of this paper is to propose a new bus stop consolidation methodology that is realistic, simple, and effective, while at the same time being sensitive to people with reduced mobility. The new methodology is tested on the bus network of the Societe de transport de Montreal (STM), Montreal, Canada. Adopting this simple methodology is expected to remove 23% of the network’s stops while only reducing the system coverage area by 1%. The removal of these stops could result in morning-peak savings of 109 hours of operating time and the elimination of a bus from 42 routes at the system level. This methodology can be applied to any urban bus network, and thus can be of interest to transit agencies and transportation researchers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".