Transit Itinerary Calculation on the Web: Based on a Transit User Information System
Bibliographic record
Abstract
Transit path calculation is not yet widely available on transit authority websites. This is because the calculation of complete transit itineraries requires the integration of underlying components such as Geographic Information Systems (GIS) and operational database and, unlike a simple road application, involves a complex transportation network. In this article, a hybrid algorithm based on heuristics and optimization is presented for the calculation of urban transit itineraries including information on pedestrian access and egress paths, route sequences, schedules, and stops. The use of the Transit User Information System (TUIS) to support the calculations is emphasized. The TUIS uses the Totally Disaggregate Approach (TDA) and Transportation Object-Oriented Modeling (TOOM) in transportation to gather data on territory (for origin and destination specifications and for the pedestrian network) and transit operation (route geometry, schedules). Websites that have been implemented are referenced to demonstrate the applicability of the hybrid algorithm. These websites make use of some special techniques for disseminating user information over the Internet.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".