Who drops off web-based travel surveys? Investigating the implications of respondents dropping out of travel diaries during online travel surveys
Bibliographic record
Abstract
Abstract Household travel surveys collect core datasets for modelling passenger travel demand. However, decline in survey completion rate is becoming a concern in recent years. One major cause is the transition from computer-assisted telephone interviews (CATI) to computer-assisted web interviews (CAWI) surveys, where respondents need to self-complete the surveys without any active help from an interviewer. Among all components, the travel diaries are the most challenging part of CAWI travel surveys and suffer significant dropouts of participation. Therefore, an investigation is necessary to understand the implications of such participation dropouts in CAWI-based household travel surveys on travel survey data quality. This study reports two travel diary designs developed to ease response burdens in travel surveys. Empirical investigations are conducted to understand survey participation drop-off behaviour while filling out travel diaries. In proxy household travel surveys, diary designs with stable repetitions outperform sophisticated diary designs. In a survey deployed with supposedly advanced diary designs, respondents with higher travel demand are more likely to drop out. Estimated using a generalizable analysis framework proposed by this study, the travel demand reflected in the final dataset collected using the sophisticated travel diary design might be underestimated by 10.2%. This study also proposes strategies and recommendations for future travel surveys.
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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 | 0.078 | 0.357 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".