Transit Holding Control Model for Real-Time Connection Protection
Bibliographic record
Abstract
The time required to transfer between different transit lines is a critical component of passenger travel time. Although transit agencies attempt to design well-coordinated timetables among intersecting lines at the scheduling stage, an operational control method is necessary to maintain the coordinated transfers that may occasionally be disrupted due to unexpected delays of public transit (PT) vehicles. One possible and practical approach is Connection Protection (CP), which involves holding a transit unit in order to wait for another transit unit that is planned to provide a coordinated transfer but has been delayed. This study develops a CP model to apply holding control to a receiving vehicle trip for the purpose of protecting the scheduled connection against delay of a feeder trip. The study incorporates the probabilistic nature of transit operations in formulating the cost function of the model, and accordingly makes more robust decisions for controlling the PT system. The developed model is evaluated by means of a sensitivity analysis. The results show that the model improves transfer efficiency and reduces the waiting times of affected passengers while minimizing delays to other passengers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".