Modernizing Montreal’s household travel survey: adapting to evolving travel trends and technological shifts
Bibliographic record
Abstract
Despite the rise of new data sources from smartphones and the internet, household travel surveys (HTS) are still crucial for transportation and urban planning. Traditional methods, especially Computer-Assisted Telephone Interviews (CATI), face declining participation and demographic biases, especially underrepresenting younger, tech-savvy individuals. The COVID-19 pandemic has further complicated accurate tracking of travel demand due to rapid shifts in mobility patterns, underscoring the need to modernize survey methods. In response, the Autorité régionale de transport métropolitain (ARTM) conducted pilot surveys in Montreal in 2021 and 2022, testing innovations such as postal invitations, web-based surveys, and a redesigned questionnaire to simplify flow and integrate new mobility topics like remote work. The blend of Computer-Assisted Web Interview (CAWI) and CATI increased response diversity and data quality, resulting in more representative data that better captures travel behaviors in the Montreal region.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".