Comparison of static vehicle flow assignment methods and microsimulations for a personal rapid transit network
Bibliographic record
Abstract
SUMMARY This article describes a static assignment method for a class of emerging public transport systems called personal rapid transit (PRT). PRT is a fully automated public transportation system where small‐size vehicles run on exclusive guideways. Because of its automated, on‐demand service, PRT must be able to automatically reroute empty vehicles after use to supply stations with waiting passengers. Consequently, any PRT traffic assignment must take into account the flow of empty vehicles as well as the flow of vehicles with passengers. The PRT assignment methods described in this work are based on linear programming models. One of the assignment methods has been applied to a realistic PRT network, and the statically assigned flows have been compared with averaged link flows produced by a PRT microsimulation. It is shown that the proposed static assignment method is not only useful to identify capacity bottlenecks of the planned PRT network but also serves to benchmark the microsimulator's vehicle management algorithms. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".