Measuring the impacts of a major metro disruption in Montréal, Canada, on riders’ satisfaction and willingness to recommend the service to others
Bibliographic record
Abstract
On October 3rd, 2024, three stations along the east end of Montreal’s blue metro line were closed, resulting in a seven-day service disruption. While previous studies have examined the operational impacts of such disruptions, their effects on user experiences remain underexplored. To address this gap, we measure the impacts of the closure on user satisfaction and their willingness to recommend transit services. Using data from a bilingual online survey launched the day after the disruption began, we analyzed responses from blue line users (N = 655) by employing ordered probit models. The survey included a treatment group of riders directly impacted by the closure (N = 361) and a control group of those unaffected (N = 294). Additionally, we incorporate data from a secondary survey conducted one prior to the closure, which included riders living close to blue line stations (N = 161), as a secondary control. Our findings reveal a significant decrease in both user satisfaction and willingness to recommend transit services among those impacted by the metro closure. However, these negative impacts can be mitigated when users perceive the availability of reliable and suitable transit alternatives. The findings from this research can be of interest to practitioners and policymakers as they highlight the broader implications of metro disruptions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".