The far side story: Measuring the benefits of bus stop location on transit performance
Bibliographic record
Abstract
Determining the proper location of bus stops is an important planning decision in the transit planning field.While previous efforts in the literature have suggested several advantages and disadvantages regarding certain bus stop placements, there has been little effort towards understanding the impacts of bus stop location on the transit system performance at the stop level of analysis.This article aims to evaluate the impact of bus stop location on bus stop time and stop time variation.It uses stop-level data collected from the Société de transport de Montréal (STM)'s automatic vehicle location (AVL) and automatic passenger count (APC) systems, in Montréal, Canada.The study findings show that stop times occurring on the near side of intersections are in average 4.2 to 5.0 seconds slower than stop times occurring on the far side of intersections, with no impact on stop time variation.A validation model is utilized to confirm the impacts of bus stop placements on stop time, using data from TriMet's automated bus dispatch system, in Portland, Oregon.This study offers transit planners and policy makers a better understanding of the effects of bus stop location on stop time and its variation in order to improve service quality while minimizing service variation at the stop level.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".