Method for Assessing Effect of Input Parameters on Multiobjective Optimization of Signal Control
Bibliographic record
Abstract
There is an increasing interest in signal-timing optimization methods that can consider mobility, safety, and emissions measures simultaneously. The introduction of new models increases the complexity of the required inputs and the relationships between inputs and outputs. This study developed and implemented such a method in an existing computational engine, presenting a sensitivity analysis conducted to provide insight on the effects and order of relevance of 20 key variables on the model’s outcomes and the associated trade-offs among mobility, safety, and emissions. This insight will help the designer, signal control engineer, and traffic analyst when designing intersection geometry and signal control. The statistical analysis of the results showed that the effect of each variable on the overall performance of the model is highly dependent on the combination of other variables. The traffic demand and the size of the intersection, defined by the number of lanes on the arterial, were found to be the most significant variables, affecting all performance measures. Mobility improvement performance usually coincides with emissions improvements, but sometimes occurs at the expense of safety.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".