Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies
Why this work is in the frame
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Bibliographic record
Abstract
Advances in information and communications technologies, connected vehicle technologies, and Big Data have made it viable for public agencies to offer efficient flexible transit services for travel demand that is predominantly dynamic to the system. There is a clear gap in methodologies to evaluate the user equilibrium for flexible transport services (FTS). In this study we lay the groundwork for studying the equilibrium of these systems and propose an agent-based adjustment process to evaluate the properties of a stable state as an agent-based stochastic user equilibrium. To validate the proposed process and illustrate its effectiveness in measuring the effect of changes in FTS operating parameters on ridership three sets of experiments are conducted: (1) illustration with a simple 2-link network, (2) evaluation of a dynamic dial-a-ride problem, and (3) illustration using real data from Oakville, Ontario consisting of 57 zones and 2000 commuters.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it