Walkability indices and travel behavior: Insights from Montréal, Canada
Bibliographic record
Abstract
Walkability indices are developed to evaluate the quality of the built environment and its suitability for walking. Over the past decade, several walkability indices were developed and promoted by public and private entities around the world. Comparing and validating these indices are essential to ensuring their reliability for adoption in practice. One method to validate such indices is to examine their predictive power for utilitarian and discretionary walking behavior. This study uses data from a large-scale travel survey (N=4,715), conducted in Montréal, Canada, to examine the predictive power of six region-specific walkability indices on weekly walking mode share for various purposes, namely work, school, shopping, leisure, and healthcare. We find that the Canadian Active Living Environments (Can-ALE) index and its extended version, Can-ALE/Transit, are the best predictors of overall weekly walking mode share for all purposes combined, shopping, and leisure activities. Walk Score® had the highest predictive power on walking behavior for healthcare purposes. While the cumulative opportunities measure (30-minute travel time) was the most effective for predicting commute walking behavior. This research provides valuable insights for practitioners and policymakers, guiding them in selecting the most suitable walkability indices to promote walking behavior in the Canadian context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".