Calibration of Driving Behavior Models using Derivative-Free Optimization and Video Data for Montreal Highways
Bibliographic record
Abstract
Traffic simulation software is commonly used for traffic impact assessment in transportation projects, allowing the selection of adequate solutions to existing complex problems without having to test them in the field. Such software is highly adaptable to different road conditions and driving behaviours by letting engineers modify a large number of parameters. However, this flexibility requires the model to be calibrated for each application in different regions, conditions and settings. Calibration requires data, which can be time-consuming and costly to collect. The authors propose a calibration procedure for the driving behavior models, which describe how vehicles interact with each other. These calibrated behaviors should be generic for the region regardless of the specific site geometry and the proposed procedure seeks to allow this generalisation by allowing simultaneous simulations on many networks. To achieve this calibration, a state-of-the-art derivative free optimization algorithm, the mesh-adaptive direct-search algorithm, is used to fit simulations to real world microscopic data acquired via automated video analysis. The authors propose an implementation of this procedure for the Wiedemann 99 model in the VISSIM traffic micro-simulation software in a case study for the City of Montreal using data collected on a major Montreal highway.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".