Two‐fold calibration approach for microscopic traffic simulation models
Bibliographic record
Abstract
Calibrating the microsimulation traffic models can be defined as a black‐box optimisation problem with some non‐concave objective functions. In this regard, the stochastic optimisation algorithms are suitable choices to explore the search space and prevent getting stuck in local optimums. However, considering only the traffic attributes‐related objectives may fail to calibrate the model in terms of safety. Therefore, by defining two different objectives, a two‐fold calibration approach is proposed such that the simulation model reproduces the real‐world transportation network more accurately, both in terms of safety and operation. Moreover, the performance of two different approaches to solve this multi‐objective optimisation problem are evaluated. It is shown that by aggregating the objectives in one single formula (i.e. a priori methods), the information exchange among solutions is not captured, which may lead to non‐optimal solutions. While this limitation is overcome by a posteriori methods since different objectives can be optimised separately and simultaneously. In this regard, the performance of posteriori‐based multi‐objective particle swarm optimisation (MOPSO) algorithm in calibrating VISSIM is compared with some priori‐based optimisation algorithms (e.g. PSO, genetic algorithm, and whale optimisation algorithm). The results show that posteriori‐based MOPSO leads to a more accurate solution set in terms of both objectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".