Changes in the Public Transit Market for a New Light Rail System: A Before-and-After Study in Montréal, Canada
Bibliographic record
Abstract
The Réseau express métropolitain (REM), Montréal’s new 67-kilometre automated light rail network, opened its first branch between downtown and the South Shore in August 2023. As one of the largest public transit investments in Canadian history, the REM is expected to have a significant impact on mobility patterns across the metropolitan region. This research contributes to assessing the effects of this new infrastructure on public transit behavior, using a market segmentation approach. Drawing on data from the Montréal Mobility Survey collected in 2022, prior to the REM’s opening, and again in 2024 after its launch, the study applies exploratory factor analysis and k-means clustering to identify distinct user segments and track their evolution over time. While user profiles remained generally stable, new segments emerged and changes were observed in travel behavior. The results show that the REM’s initial phase of operation introduced new patterns of use and revealed a notable gap between intended and actual usage. Continued monitoring is essential to adapt transit services and better respond to the changing needs of diverse user groups.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".