An inductive experimental approach to developing a web-based travel survey builder: developing guidelines to design an efficient web-survey platform
Bibliographic record
Abstract
The household travel survey (HTS) is the most widely used passenger travel data collection method, and web-based HTS is currently the most dominant survey mode. However, there is a lack of proper understanding on how much the web-based approach can be used without over-burdening respondents. This study investigates methods to improve web-based HTS data quality and to reduce response burdens. It presents the lessons learned from the development and field experiment of a web survey builder. A particular focus is on designing and testing a trip diary interface through usability tests. These tests include a mouse-movement tracking study, mock web-based HTS experiments with responsive designs, and the use of a route planner application programming interface (API). Results show that creating responsive designs for web-surveys based on screen size can significantly increase completion rates and improve the usability. Collecting detailed routes with a route planner API suggesting most likely routes does not significantly increase respondent fatigue. However, it significantly improves data quality. Household size and the age of the survey respondent are significant contributing factors to survey drop-off rates and respondent fatigue. The paper contributes to the literature on household travel surveys by providing evidence-based design guidelines for web-survey interfaces.
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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 | 0.139 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".