P-TRANE: Modeling Bus Transit Network Evolution in a GIS-Based Framework
Bibliographic record
Abstract
This paper describes the development and the main components of a simulation framework, named P-TRANE (Prediction Model of Transit Network Evolution), for modeling the bus transit network evolution. Using a GIS-based interface, P-TRANE develops predictions of changes in the bus transit network & service functionality at future time steps. These changes, triggered by the periodic service review process, are influenced by several variables such as changes in future transit ridership and future land-use. P-TRANE was developed as a transit supply prediction component for the Integrated Land Use, Transportation, & Environment (ILUTE) simulation framework, aiming to produce future bus network information for ILUTE. However, it could usefully function as a decision support system tool for transit planners and transit agencies. Preliminary model results, using the Toronto Transit Commission bus network as a case study, show that it is successful in identifying transit routes prone to frequency changes (poor performance and/or overcrowding). Furthermore, results show that the P-TRANE GIS module is a promising tool capable of proposing new bus lines, including feeder lines serving rapid transit projects/stations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".