Bibliographic record
Abstract
A new concept in transit travel surveys, called the driver-assisted bus interview, is proposed. The survey uses data that are passively gathered by smart card automatic fare collection systems on public transit. Its superiority lies in the resolution of the data as well as the continuous geographic and temporal coverage of the network and cardholders. The paper first discusses the quality of survey data. It then describes a totally disaggregate object-oriented approach as a method to understand, validate, correct, and enrich the data. The study uses one month of archived smart card boarding data from a medium-size transit agency. The data go through a validation and correction process that makes use of planned service and cardholders’ historic travel pattern. Trip data not collected by the survey are obtained through enrichment techniques. The anchor points of a cardholder can be inferred from the derived employment status, multiday travel pattern, and a trip-generator database. The procedure that infers trip destination and trip purpose for the student subgroup is explained. Advanced analysis and visualization techniques demonstrate the versatility of the data, which can be scrutinized as a travel demand survey, a special trip generator survey, a resource allocation and consumption survey, and a multiday survey.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".