Simultaneous calibration of microscopic traffic simulation model and estimation of origin/destination (OD) flows based on genetic algorithms in a high-performance computer
Bibliographic record
Abstract
The objective of this paper is the development of a multi-criteria optimization framework for the simultaneous calibration of demand and supply parameters in DTA. The presented approach estimates origin-destination (OD) flows and calibrates the driver behavioral and route choice parameters in a complex network modeled in Paramics microscopic traffic simulation model. Genetic Algorithm (GA) is chosen as the solution method for solving the stochastic nonlinear optimization problem. A high-performance computing cluster is used to run GA in parallel computer processing engines. The application of the framework on a large case-study network showed that the incorporation of speed data from in-vehicle navigation systems improves significantly the calibration performance in terms of improved accuracy of estimated counts and speed. In addition, the incorporation of speed data makes the calibration problem less dependent on the starting OD flows. Finally, the application of a distributed GA was shown to significantly reduce the computational time of the calibration of DTA systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".