Strict and Deep Comparison of Revealed Transit Trip Structure between Computer-Assisted Telephone Interview Household Travel Survey and Smart Cards
Bibliographic record
Abstract
Large sample household travel surveys (HTSs) are an essential tool for the planning of urban transit systems. The progressive adoption by transit agencies of fare collection systems based on smart cards (SCs) has, for the first time, provided opportunities to compare the survey data with detailed, population-level data collected independently. These comparisons have produced some surprising results. Although the underreporting of non-home-based and off-peak trips was to be expected, the significant overestimation of transit use during peak periods was not anticipated. Using the Greater Montreal Area as a case study, this paper performs a strict and deep comparison of computer-assisted telephone interview (CATI) HTS data and SC data across several dimensions: transit agency usage, departure time from home, number of trips per traveler, and activity durations. The analysis reveals that the HTS constitutes a simplified portrayal of transit usage patterns. Non-home-based trips and trips made for activities of short duration are underrepresented in the survey data, leading to an underestimation of off-peak travel by transit. In addition, the systematic overestimation of peak period transit use appears to be because of the corrective weighting of the 20–29 demographic which is notoriously difficult to reach in a telephone-based household survey.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".