Use of Subway Smart Card Transactions for the Discovery and Partial Correction of Travel Survey Bias
Bibliographic record
Abstract
Although the theoretical sources of bias in travel surveys have been documented, data that describe an entire population of travelers rarely permit the reliable detection and measurement of bias. The existence of large databases of smart card transactions in public transit systems presents an opportunity to do so. In this paper, a typical average weekday of travel demand data from the Montreal, Canada, household travel survey is confronted with a single, specific day of smart card transactions. The object of comparison is the Montreal subway system, which is involved in 10% of all daily trips within the metropolitan area. The results of the initial analysis indicate that although the survey accurately reproduces daily subway ridership, it overestimates subway boardings by 24% during peak periods. This overestimation can be corrected by adjusting the weights of home-based trips to match entry volumes at subway stations during the morning peak period. The results of the reweighting procedure suggested that francophone households that use transit had a greater propensity to respond to the survey compared with other households. Furthermore, even after reweighting, the travel survey underestimated off-peak demand by roughly 21%. The underestimation was likely attributable to underreporting of non–home-based trips by respondent households and nonresponse of specific population groups.
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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 | 0.101 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".