Formulation and solution method of bounded path size stochastic user equilibrium models – consistently addressing route overlap and unrealistic routes
Bibliographic record
Abstract
Bounded Path Size (BPS) route choice models (Duncan et al, 2021) offer a theoretically consistent and practical approach to dealing with both route overlap and unrealistic routes. It captures correlations between overlapping routes by including correction terms within the probability relations, and has a consistent criterion for assigning zero probabilities to unrealistic routes while eliminating their path size contributions. The present paper establishes Stochastic User Equilibrium (SUE) conditions for BPS models, where the choice sets of realistic routes are equilibrated along with the route flows. Solution existence/uniqueness are addressed. A generic solution algorithm is proposed, where realistic route choice sets are equilibrated from a pre-generated approximated universal set of routes. Numerical experiments on the Sioux Falls and Winnipeg networks show that BPS SUE models can be solved in feasible computation times compared to nonbounded versions, while providing potential for significantly improved robustness to the adopted master route choice set.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".