Walkability: Which Measure to Choose, Where to Measure It, and How?
Bibliographic record
Abstract
The urban structure of neighborhoods has a decisive impact on active mobility, but this impact is hard to evaluate in a mode choice model because of the high collinearity between urban form variables and the uncertainty surrounding adequate spatial measurement parameters. Several composite scores, or walkability measures, have emerged from the literature, each using its own method and including different variables. No consensus has been reached on the size of the catchment area that should be used to measure walkability and most studies only measure walkability at the origin of the trip without considering other spatial units. In this paper, a series of four walkability measures: the Pedestrian Index of the Environment (PIE), the Walkability Index (WI), the Pedestrian Potential Index (PPI) and the Neighborhood Destination Accessibility Index (NDAI), are applied to the Greater Montréal Area to examine their correlation with the choice of walking for short trips. Several definitions of a neighborhood are tested for each measure, as well as several spatial units of measurement. A series of binary logistic regressions are then estimated using observed trip data from the 2013 Montreal Origin–Destination Survey to identify which measure, using which spatial parameters, offers the best performance in a mode choice modeling context. Measures using a diversity of urban form variables, the WI and the PIE, are found to offer the best performance, especially when measured at a scale smaller than a 1.6 km radius, while the spatial unit offering the best model fit varies between trip purposes.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".