G-EMME/2: Automatic Calibration Tool of the EMME/2 Transit Assignment Using Genetic Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it