Two‐fold calibration approach for microscopic traffic simulation models
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
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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