Estimating the Destination of Unlinked Trips in Transit Smart Card Fare Data
Bibliographic record
Abstract
Smart card automated fare collection systems have been effective for the collection of data about the travel behavior of users on public transit networks. Because some systems record only the boarding (origin) locations, a method is needed for estimating the alighting (destination) locations. Existing algorithms can estimate the destination for most trips. However, unlinked trips, which are not part of a trip chain during the day, are more difficult to analyze. The proposed improvement to the existing model for destination estimation, especially for unlinked trips, is based on kernel density estimation of the spatial and temporal probabilities of each destination. The Société de Transport de l'Outaouais, a medium-sized bus service near Ottawa, Ontario, Canada, provided data for a 1-month period in 2009 (908,303 total transactions). Existing algorithms can handle only 80.64% of the trips; the proposed method handles an additional 10.9%. These results are analyzed, and future research directions are discussed.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".