Application of the “Foot-in-the-Door” Compliance Technique for Traveler Intercept Surveys
Bibliographic record
Abstract
Traveler intercept surveys are used to obtain essential data for transportation planning purposes in a variety of contexts and situations. This paper describes an application of the foot-in-the-door (FITD) compliance technique for traveler intercept surveys as a way to increase participation and improve the overall experience for both the traveler and the organization conducting the survey. Outcomes related to survey participation and response characteristics for three such surveys are presented. The two-stage FITD method described in this paper was successful at collecting a small amount of data from a majority of travelers at the locations being studied while minimizing the inconvenience to the traveler. A follow-up survey conducted via the Internet had a response rate that varied between the different case studies and also among different groups of travelers. Approximately one-third of follow-up survey responses were provided on the same day as the initial interaction between the traveler and the researcher, whereas one-quarter of respondents utilized a mobile device to complete the follow-up survey. The results of these case studies are informative for the development of traveler intercept surveys. Transportation planners are encouraged to consider the FITD technique described in this paper when developing traveler intercept surveys within their jurisdictions.
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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 | 0.065 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".