Stated Preference Analysis of Sensitivities to Elements of Transportation and Urban Form
Bibliographic record
Abstract
A total of 1,277 randomly selected households in Edmonton, Alberta, Canada, were successfully interviewed concerning their attitudes to a range of elements of urban form and transportation. These elements included times and costs for trips to work and shopping by automobile and transit, taxes, air quality, traffic noise, walking conditions to local schools, street type in front of dwelling, and housing type. A stated preference technique was used, in which each respondent was asked to rank in order of preference a set of hypothetical future alternatives involving the elements. Additional direct questions were then asked about the influences of the elements in the ranking process. Logit choice analysis was used to establish the relative importance of each element for the “typical” household represented by the full sample and for various groups in the population represented by different subsamples. Overall, it was found that housing type is the most important of the elements considered, followed by municipal taxes, air quality, and traffic noise. Also, among many other things, there is less sensitivity to money spent for travel than to money paid for taxes. These indications, together with the various specific trade-off rates that were obtained, provided useful guidance in the development of a new transportation master plan. They can also support a more formal evaluation system that reflects the sensitivities of different groups of households regarding a wide range of elements of concern to transportation and urban planners. The techniques used are flexible and could be used to consider various other elements of concern in different contexts.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".