Assessing the Spatial Transferability of the Pedestrian Index of the Environment (PIE)
Bibliographic record
Abstract
The urban structure and the built environment have been found to exert a determining impact on active transportation behavior. However, a lot of the variables related to the urban structure are strongly correlated when measured at the neighborhood or trip level. Composite walkability measures, which combine several of these neighborhood variables into a single score, are increasingly popular solutions to circumvent the problem. This paper uses trip data from the 2013 Origin-Destination survey to analyze the transferability of a composite walkability measure, the Pedestrian Index of the Environment (PIE), to the Greater Montréal Area (GMA). Developed in Portland, Oregon, the PIE is a grid-based measure combining six neighborhood variables into a score ranging from 20 to 100. Mode choice models are estimated on different subsets of short trips to study the impact of the PIE on predicting walking behavior. Significant correlation is found between the PIE and the choice of walking for short trips, for all purposes. The inclusion of the PIE also improves the accuracy of the modelling process. The PIE can therefore be used in the GMA, and potentially in other metropolitan areas, to explain active travel behavior for short trips and help researchers and practitioners to better understand the effect of the urban form on walk trips.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".