Analysis of travel pattern changes due to a medium-term disruption on public transit networks using smart card data
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
This study aims to analyze the travel behavior changes due to medium-term disruption on public transit networks by using smart card data, as a potential substitute to before-after surveys. The case studies are metro station closures in Montreal, Canada. The study examines the effects of the closures at the aggregate and disaggregate levels, in order to examine the travel pattern changes due to the presented disruption event. The study shows that even a medium-term disruption could have long-term impact on the travel patterns of frequent users of the impacted infrastructure. This study presents a first attempt to use passive data for analyzing the impacts of public transit service disruption on transit customers’ behavior in Montreal. Several limitations and some of the ongoing and future research topics to address the limitations are also discussed.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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