Passenger origin–destination estimation in public transit using boarding count data
Bibliographic record
Abstract
Passenger boarding and alighting data are critical for understanding travel patterns in transit systems, yet many systems only record boarding, making it difficult to track alighting. This study introduces a method to estimate alighting counts from boarding data using transit symmetry at a cluster level. Bus stops are grouped into clusters, assuming equal inflow and outflow, and a gravity model is calibrated to estimate an origin–destination (OD) matrix. Using data from three bus routes in Regina, Canada (2017), the method achieved a symmetry ratio of 60% across clusters and a mean structural similarity measure score of 0.84, validating the accuracy of the OD estimates. This approach provides a practical solution to the challenge of missing alighting data. While effective for single-mode transit systems, future research will expand its application to multi-modal systems and address factors like seasonal and land-use variations, offering a cost-effective alternative for OD estimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".