Calibration and Validation of Micro-Simulation Models of Medium-Size Networks
Bibliographic record
Abstract
This paper describes a procedure for the calibration and validation of medium-sized traffic simulation network models, which are widely used to quantify the benefits and limitations of traffic control options. The authors emphasize that these models must be accurate and reliable in order for the resulting traffic control decisions to be useful and appropriate. Microsimulation traffic models have often been based primarily on driver behavior and lane-changing parameters. In contrast, medium and large scale models often included scant attention to the calibration of driver behavior parameters. The procedure that the authors present in this paper provides a comprehensive calibration of the micro-simulation model in a sequential manner. They offer a case study of a VISSIM model of downtown Vancouver, British Columbia, which is an urban condensed grid network of more than 100 signalized intersections. The calibration procedure included origin-destination (OD) matrix estimation using recent traffic volumes, route choice calibration by manipulating link surcharges, and driver behavior parameters calibration using an experimental design and multiple runs. Real-life travel time data were collected to calibrate and validate the model. The authors conclude that the observed travel times were found to reasonably match simulation travel times of the calibrated model and is thus a valid model to use.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".