Making Automatic Passenger Counts Mainstream
Bibliographic record
Abstract
Although automatic passenger counters (APCs) have been used for many years, significant obstacles have hindered their becoming a mainstream source of data for monitoring ridership and peak load, estimating passenger miles, and other measures of passenger use important for transit management. The key to APC usefulness is the automatic, routine conversion of the APC data stream into a database of accurate counts. On the basis of case studies of transit agencies, five issues important to achieving this goal are analyzed: data structures, data accuracy, accuracy need and sampling requirements, controlling drift, and balancing algorithms. Balancing algorithms deal with routes with loop ends, negative loads, and rounding. Sampling and accuracy requirements related to passenger miles estimates for National Transit Database (NTD) reporting are also analyzed. The analysis shows that, for most agencies, NTD precision requirements can be met with a small level of fleet penetration, provided that measurement, screening, parsing, and balancing methods keep bias in load measurement below 8%.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".