The influence of the built environment on household vehicle travel by the urban typology in Calgary, Canada
Bibliographic record
Abstract
Most land use and travel studies have addressed the area-wide impact of land use and transportation policies on vehicle travel, yet few studies have examined the varying impact of those policies on vehicle travel in different spatial settings. The aim of this study is to investigate how land use and transportation factors influence household vehicle travel in Calgary according to the city's urban typology, defined by its development form and functions in Calgary's Municipal Development Plan (MDP). This study employed a segmented regression method, also known as a piecewise regression, to examine the impact of various land use and transportation characteristics on household vehicle kilometers of travel (VKT) in four areas of the city including the center city, inner city, established area, and greenfield sector. The main data sources for the study include the 2011 Calgary and Region Travel and Activity Survey (CARTAS) in conjunction with spatial datasets from the City of Calgary. There is no additional benefit of VKT reduction in the center city found by the intensification efforts tested in this study. However, densification and provision of light rail transit (LRT) may be key to reducing household vehicle travel in the established area and greenfield sector of Calgary. The study results also suggest that households tend to drive significantly more as they live further from the center city, where more than half of the city's employment is clustered. This implies the need to have sub-centers across the city.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".