Calibration of microscopic traffic model for simulating safety performance
Bibliographic record
Abstract
A multi-criteria calibration procedure is proposed for parameter calibration of microscopic traffic simulation platforms. The impetus for this paper is provided by increased usage of microscopic simulation models in transportation safety studies. Before such models can be adopted in safety research it is important that we obtain parameter values that reflect real world traffic conditions. Current state-of-the-art Genetic Algorithm calibration procedures only allow for one measure of performance in parameter calibration. In safety studies, the fitting function used in calibration has been safety performance. Therefore, the underlying traffic-related factors, such as speed, volumes and density have not been directly considered in calibration. Since these models are based on simulating traffic and calibration needs to be based observed traffic attributes. The proposed multi-criteria procedure would allow for the direct calibration of these traffic attributes while at same time producing accurate estimates of safety performance. The multi-criteria procedure is applied to a sample of vehicle tracking data and the results are compared to parameter values suggested by a single-criteria approach and platform defaults.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".