Measuring the impacts of a major metro disruption in Montréal, Canada, on riders’ satisfaction and willingness to recommend the service to others
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Science and technology studies | 0.001 | 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 it