Transport Survey Data: What in the World Is Happening?
Bibliographic record
Abstract
This paper provides a synopsis, from a Canadian perspective, of priorities, issues and trends from around the world that were discussed at the 9th International Conference on Transport Survey Methods in Chile in November 2011. The dominant contemporary issues that cut across 14 thematic workshops included: the maturing of new technological supports to transport surveys; the alignment of surveys with administrative data; cognitive and social processes affecting survey response, especially in surveys about potential behaviour; shifts in the total design of surveys; the needs of integrated regional models; and survey designs addressing specific policy questions in passenger and freight transport. These issues were discussed in the context of collection's being an easy target for budget cuts, while public and private agencies are more and more dependent on decision support systems using analysis tools and models that are relatively data hungry. This last is not just a question of the quantity of data, but also of its quality and comprehensiveness, and of the inclusiveness of user groups that are reached by survey samples. Moreover, while metropolitan household travel surveys remain the bread and butter of urban transportation planning, a number of other types of survey on the past, current and anticipated behaviour of passenger and freight transport users have become increasingly valuable to decision makers around the world. This trend was seen as particularly important to scoping shifts in the transport system, while maintaining comparable indicators of transport demand over long periods. (A) For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.046 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".