Out of Service: Identifying Route-level Determinants of Bus 2 Ridership over Time in Montreal, Quebec, Canada
Bibliographic record
Abstract
As many cities in North America, Montréal has been seeing shrinking bus ridership trends over the past few years. Nevertheless, most of the recent literature has focused on the broader causes for ridership decline at the metropolitan or city level, none have considered ridership at the route level. As service adjustments take place at the route level and are felt by riders at this level, our study explores the determinants of STM bus route ridership between 2012 and 2017 using two longitudinal multilevel mixed-effect regression models. Our findings suggest that increasing number of daily trips and increasing the average route speed are keys to bus ridership gains. In contrast, an increase in bus stop spacing decrease bus ridership, while controlling for the impact of a few important external variables related to built environment, residents’ socioeconomics, and gas prices. Our models also show that reducing service frequency along a parallel route will lead to an increase in ridership along the main route. This study can be of use to transit planners and policy-makers who are striving to increase bus ridership, by exploring the factors affecting ridership at the route level, where most of the policies are implemented and where riders actually feel them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".