Calibration of the aggregate transit assignment model of EMME/2 using genetic algorithms
Why this work is in the frame
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Bibliographic record
Abstract
The study objective is to calibrate the aggregate transit assignment model such that the assignment output volumes match ridership volumes obtained by the on board counts. Specifically, calibration involves the determination of the optimal values of the following parameters: Boarding time, Wait time factor, Wait time weight, Auxiliary time weight and Boarding time weight. Conventionally, we use default values of EMME/2 or use a trial and error method for calibration. But such conventional methods do not ensure the optimal set of values. A more efficient method to determine the optimal values of the combinatorial parameters is to use Genetic Algorithms. Each chromosome in GA represents a specific set of parameter values that has a specific goodness of fit value. Finally, the best set of values was obtained through GA optimizer. This automatic method will help to get rid of tedious conventional methods for calibration of transit assignment models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it