Survey of Transportation Agencies on the Current State of Transportation Data Collection Practice in Canada
Bibliographic record
Abstract
High quality, comprehensive data on travel behaviour, transport network performance and land use characteristics are essential to the planning and design of urban transport systems. Such information is derived from a variety of sources. In order to gain an understanding of current Canadian collection practice, issues and needs, a survey of transportation agencies was undertaken. Based on the survey results, 57% of respondents either conduct or commission household travel surveys, while another 18% either plan to, or would like to use, such surveys. Of the agencies that conduct or commission household surveys, 94% have undertaken at least one survey over the past 10 years. Telephone-based methods dominate Canadian survey practice, with 55% using telephone listings for their sampling frame, and 57% using telephones as their interview medium. However, web-based methods are emerging as the second most popular form of interview medium (24%). In addition to surveys, many types of counts and inventories are also conducted to provide supplemental data. It was clear, however, that all agencies encounter challenges, both fiscal and methodological. The main concerns included selecting properly representative sample frames, given the declining use of land-line phones, and the associated issues of low response rates, under-reporting of trips and insufficient sample sizes. Canadian agencies recognize that emerging technologies and new data sources can play a role in addressing these challenges. This survey provides a baseline of current methods upon which to construct a standardized framework for the collection of data on personal travel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".