Analyzing the efficacy of transportation survey recruitment methods
Bibliographic record
Abstract
Collecting essential data through surveys is critical for shaping our understanding of travel demand, which influences important transportation planning decisions. However, conventional data collection methods face challenges, such as declining completion rates, under-representation of specific population sub-groups, confidentiality, and significant investment of resources. By employing diverse strategies in recruitment and data collection, it is possible to reach a broader and more representative sample of the population, thereby improving the overall effectiveness and accuracy of the surveys. With this motivation, this thesis examines various requirements for enhancing transportation surveys, emphasizing recruitment methods. First, an expert workshop was organized to understand the importance of transportation surveys. The event highlighted the need for a flexible and multi-faceted approach to sample collection, recognizing the dynamic nature of technology, societal preferences, and participant demographics in making informed decisions and developing effective transportation plans. Next, the implementation of a transportation survey deployed in British Columbia, Canada, is discussed, utilizing various recruitment strategies (mail, e-mail, SMS and call). Finally, the thesis presents analyses of the efficiency of each recruitment method used. To further identify factors that influence survey completion rates, an ordered logit model is developed to examine households' need for frequent communication to complete the survey, providing insights into the efficiency of different communication strategies. Additionally, a hazard model is formulated to investigate the time it takes for the households to complete the survey without frequent communication. Results indicate that both recruitment methods, mail and self-registration, were effective in creating a representative sample of the population. SMS was the most effective reminder method. For example, among the self-registrants, about 69% of households that received an SMS completed the survey on the same day, with approximately 40.5% finishing within one hour. Results also found that self-registrants, older adults and single individuals need more frequent communication to finalize the survey. In contrast, without reminders, males and residents of Metro Vancouver tend to complete surveys more quickly, while single individuals and full-time workers may require more time.
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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 | 0.592 | 0.780 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".