Towards a better understanding of changes in cost per riders for bus routes before and after the COVID-19 pandemic in Montréal, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has severely impacted the finance of transit agencies by reducing farebox revenues. Combined changes in ridership and service operation levels have further transformed the financial efficiency of public-transit services. Understanding how these changes vary between routes is crucial to inform service optimization processes to reduce transit agencies' operational deficits. Using data from the bus network in Montréal, Canada, for 2019 and 2022, we assessed changes in cost per rider at the route-level before and right after the COVID-19 pandemic. We categorized daytime multi-stops bus routes ( N = 184) based on the income of the areas they served and their cost per rider across both years to assess diverging temporal and spatial patterns. Our results highlighted that high cost per rider routes were mostly located in the periphery of the study area and in the downtown core and that such patterns worsened following the pandemic, particularly for the downtown core. We observed that routes which served higher income areas tended to have higher cost per rider on average than middle- or low-income ones. We further confirmed this finding by categorizing bus routes by their cost per rider, finding that high cost routes in both 2019 and 2022 tended to be serving higher income areas than other routes. The consideration of both temporal, spatial and socio-economic variation of the cost of bus services provides nuance insight to transportation planners as they aim to optimize bus services while being mindful of potential ridership loss and vertical equity issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".