Assessment of the Impact of New Subway Stations on Public Transit Mode Share Using a Quasi-Experimental Design (Montreal, Canada)
Bibliographic record
Abstract
The use of public transit has been associated with increased physical activity through increased walking compared to car use, and thus to increased health benefits. Yet how increased public transit access has modified the use of public transit has received very limited attention.We aimed to assess the impact of three new subway stations in 2007, on the use of public transit in the greater Montreal region (Canada), with a quasi-experimental design.We used information from Origin-Destination (OD) surveys of 2003, 2008 and 2013 to assess public transit mode shares in 96 OD survey areas. We compared mode shares in the OD area where the subway stations was added (intervention area), to those of comparable control areas with no new subway stations. Those control areas were identified with a cluster analysis based on the 2003 characteristics of the areas related to transport behaviors (i.e. mode shares), built environment characteristics (e.g. population density, proximity to subway stations) and the socio-economic status of the residents (estimated with information from the 2001 Census). Mode shares were compared using difference-in-difference models, with matching of individuals from the OD surveys based on age, sex and income with propensity scores.Six out of 96 control areas were selected with the cluster analysis. The public transit mode share increased from 13% (2003) to 18% (2013) in the intervention area. This increases (+5%) is significantly greater than in control areas with no new subway stations (-1.9% to 1.9%). Thus, after 10 years, the differences in the evolution of public transit mode share between the intervention and each control areas ranged from +3.1% to +6.9%.The addition of new subway stations is associated with a non-negligible increase in public transit mode share, and future assessments should quantify its influence on physical activity and health.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".