Estimating destination of unlinked trips in public transportation smart card fare collection systems
Bibliographic record
Abstract
ABSTRACT: Smart card automated fare collection (SCAFC) systems have proved effective in collecting data on the travel behavior of users of public transit networks. Since some systems record only the boarding locations, we need a method for estimating destinations. Existing algorithms can find an estimate for most trips. However, unlinked trips, which are trips that are not part of a trip chain during the day, are more difficult to analyze. We present an improvement to the existing model for estimating destinations, in particular for unlinked trips. The method is based on kernel density estimation of the spatial and temporal probabilities of each destination. The data used for this study comes from the Société de transport de l’Outaouais, a mid-size bus service near Ottawa, Canada (for a one-month period in 2009 with 908,303 transactions). Existing algorithms can handle only 80.64% of the trips; our method handles an additional 10.9%. We present an analysis of these results and discuss future research directions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".