Stated Preference Survey Pivoted on Revealed Preference Survey for Evaluating Employer-Based Travel Demand Management Strategies
Bibliographic record
Abstract
This paper presents a study of commuters’ responses to various employer-based transportation demand management (TDM) strategies that was conducted in the Region of Peel, Ontario, Canada. The study involves design and implementation of a web-based survey of daily commuting mode choices and an efficient design-based stated preference (SP) experiment on the mode choice effects of potential employer-based TDM strategies. For the SP experiments, the survey also collected an elicited confidence rating from the respondents. The survey of 835 random commuters was conducted in fall 2014 and spring 2015. The paper uses empirical models of mode choices (revealed and stated) and an ordered probability model of the elicited confidence rating information to evaluate the data quality. The empirical models reveal that parking cost, monthly parking scheme, indoor parking facilities, emergency ride home, and bike share had higher impacts on commuting mode choices than did bike access facilities and a carshare strategy at the workplace. In relation to respondents’ confidence on SP responses, commuters with a higher number of cars in the household and with longer commuting distances seemed more certain and confident in their responses than did others. In addition, females were found to be more confident when answering SP choice questions.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".