Enriching Archived Smart Card Transaction Data for Transit Demand Modeling
Bibliographic record
Abstract
Transaction data from public transit smart cards represent a continuous stream of detailed travel information for transit demand modeling. Although certain aspects of information are incomplete in unprocessed data, efforts are devoted to deriving a more comprehensive understanding of the system and its users from partial information through data enrichment processes, with a long-term goal of establishing a dynamic model of demand. On the basis of previous work, methods are proposed to estimate the arrival time of bus runs at the stop level by using temporal constraints and to identify linked trips by using spatial–temporal concepts. These enrichments lead to the reconstruction of individual itineraries, the analysis of transfer activity, and the synthesis of vehicle load profiles. The latter provide planners with a detailed spatial–temporal progression of each run, origin and destination stops for each individual transaction, and boarding and alighting activity at each stop. The study draws on more than 37,000 smart card boarding transactions of an average weekday from a midsize transit agency. Results suggest that linked trips represent slightly above 10% of the total number of transactions in the network and the smart card system overestimates the proportion of linked trips by nearly 40%. The outcome is promising and lays a foundation to further enrich the itineraries by associating the boarding and alighting stops with trip generators, deriving trip purposes, and performing multiday analysis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".