Market-Segmentation Study of Future and Potential Users of the New <i>Réseau Express Métropolitain</i> Light Rail in Montreal, Canada
Bibliographic record
Abstract
Goals for public transit agencies and new public transport infrastructure projects include attracting new riders and retaining existing system users. An understanding of the public transport market and its preferences, habits, and attitudes can help public transit agencies reach these goals by shedding light on how to increase customer satisfaction. To understand potential users of one of Montreal’s most recent major transport projects, the Réseau express métropolitain (REM), we conducted a survey in Fall 2019 while the light-rail system was under construction. Drawing on vetted transport market-segmentation frameworks, this study employs an exploratory factor analysis to reveal factors that affect respondents’ propensity to use the REM. A k-means cluster test is applied to the factors to articulate market segments. The analysis returned four clusters that form a clear spectrum of least likely to most likely REM users: car-friendly non-users, urban core potential users, transit-friendly users, and leisure and airport users. Positive opinion, proximity, and desire to use the REM for leisure or non-work trips are three key characteristics of likely users. There is a visible relationship between clusters who are likely to use the REM and clusters who agree that the REM will benefit their neighborhood. Improving people’s perception of the potential benefit of the REM to their neighborhood, better accommodating leisure use, emphasizing and communicating appealing destinations, and highlighting transit connections are four core ways that planners could work to potentially increase the number of people who are likely to use the REM.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".