Penalization and augmented Lagrangian for O-D demand matrix estimation from transit segment counts
Bibliographic record
Abstract
In this paper we consider some penalized quadratic models to update Origin-Destination (O-D) matrices in transit networks from observed flows. These models look for the closest O-D matrix to an outdated one, which reproduces some observed segment flows. We demonstrate that the solution of these penalized models converges to the solution of the Spiess model when the penalty parameter increases to infinity. Another contribution is the introduction of an augmented Lagrangian model and its iterative solution by a dual ascent technique and the method of multipliers. This approach yields high-quality solutions with low CPU time and it is tested with two networks: the Winnipeg transit network, which has 23716 O-D pairs; and the transit network of the metropolitan area of the Valley of Mexico with more than 2 million of O-D pairs. For some instances, extracting the null coefficients from the old O-D matrix reduces the computational cost even further.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".