Exploring the role of the built and natural environment in encouraging active travel for different trip purposes in Montreal
Bibliographic record
Abstract
Transportation research has extensively examined the influence of both the built and natural environment on active travel. While most studies assume linear relationships, some evidence indicates that this might not always be the case. This paper addresses this by identifying the nature of the relationship between the built and natural environment (BNE) and active travel (AT) across several trip purposes: school, shopping, work, and leisure trips in Montreal, Canada. We also identify areas with low and high potential for active travel. Using Generalized Linear Models with the Tweedie family and including a spatial lag covariate, we found that the relationship between BNE and AT is not always linear. In some cases, higher access levels to sidewalks, bike lanes, walkable destinations, and transit stops, constantly increase AT but with cubic or logarithmic relationships. Other variables, such as dwelling density, intersection density, park access, tree coverage, industrial diversity, and proximity to water bodies, also encourage active travel but only up to a certain threshold, beyond which further increases do not increase AT, and in some cases, can lead to a decline, forming an inverted "U" relationship. These relationships vary across trip purposes. Central areas in Montreal show the best potential to support active travel, while the rest of the city displays low levels of support, depending on the trip's purpose. The findings highlight the importance of accounting for non-linear relationships, as improvements in the BNE do not always translate into higher levels of active travel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".