G-EMME/2: Automatic Calibration Tool of the EMME/2 Transit Assignment Using Genetic Algorithms
Bibliographic record
Abstract
This research presents an automatic procedure for calibrating transit-assignment model parameters. The calibration process targets the optimal set of parameter values by ensuring that the assignment output volumes match ridership volumes obtained from on-board surveys. Due to the combinatorial nature of the problem of interest, the proposed calibration process is automated using genetic algorithm techniques to find the best values for parameters through minimizing a “misfit” function. This study presents the new G-EMME/2 tool, which is an automatic calibration tool designed to find the optimal set of values for the transit-assignment model parameters implemented in the EMME/2 transportation planning software. The G-EMME/2 tool was applied to the Toronto transit network, and the five EMME/2 aggregate transit-assignment model parameters were estimated. The results are very encouraging. This research is an attempt to help automate the tedious process of calibrating transit assignment models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".