An optimal downsampling procedure for microscopic simulation modeling of transportation networks: a proof-of-concept study
Bibliographic record
Abstract
This paper investigates and demonstrates the concept of scalability of microscopic traffic simulation systems as a means of reducing the associated computational requirements and maximizing their potential support for real-time traffic control and management functions. The primary goal of this research is to examine the feasibility of transforming the original simulation environment into a downsampled simulation environment, where fewer representative entities are simulated. This must be achieved, however, while retaining maximum fidelity to microscopic simulation properties and preserving most of the macroscopic characteristics. The methodology is presented as an optimization problem whose objective is to minimize the errors resulting from the transformation process and to seek optimal values of the behavioral parameters in the downsampled environment. In this proof-of-concept stage, experimental analysis was conducted on a homogeneous freeway segment using one of the well-known, and arguably sufficiently calibrated, car-following models developed by General Motors Laboratories (GM3). The results were promising and showed that optimal relationships between the behavioral parameters in both environments can be established to minimize the information loss associated with the transformation process.Key words: microscopic simulation, scalability, computational efficiency, downsampling, car-following 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".