Walkability indices and travel behaviour: Insights from Montréal, Canada
Bibliographic record
Abstract
The emergence of the 15-minute city concept brought walkability to the forefront of sustainable transport research. Walkability is a measure that evaluates the quality of the built environment and its suitability for walking. Over the past decade, several walkability indices were developed and promoted around the world. Comparing and validating these indices is essential to ensure their reliability for adoption in practice. 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 work, school, shopping, leisure, healthcare, and all purposes combined. The indices include Walk Score®, Spatial Access Measures, Canadian Active Living Environments (Can-ALE) index and its extended version, Can-ALE/Transit, as well as cumulative opportunities accessibility within 15 and 30 minutes of walking. We find that Can-ALE and Can-ALE/Transit are the best predictors for the percentage of walking trips performed per week for all purposes combined, as well as shopping and leisure trips. These two measures are the only ones that explicitly consider dwelling, street intersection, and destination density. The developers of these indices provide a dataset containing detailed values for each component incorporated in their calculations per analysis unit. The overall index can help highlight areas for potential enhancements, while the detailed components enable the development of targeted strategies and interventions. Gravity-based measures such as Spatial Access Measures and Walk Score® were shown to be adequate in predicting overall walking mode share. However, their design makes them less interpretable, hindering their applicability in practice. Cumulative opportunities measure (30-minute travel time) was the most effective for predicting commute walking behaviour. This research provides valuable insights for practitioners, guiding them in selecting the most suitable walkability indices to promote walking.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| 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".