Multi‐criteria analysis for evaluating the impacts of intelligent speed adaptation
Bibliographic record
Abstract
Abstract Road safety is a policy priority due to the high casualties and costs associated with road accidents. Since speed is a major cause of road accidents, in‐vehicle speed limiters or Intelligent Speed Adaptation (ISA), seems a promising solution. ISA implementation, however, is hindered by large uncertainties, for example about the impacts of ISA, the way users might respond to ISA, and the relationship between speed and accidents. Traditional Multi‐Criteria Analysis (MCA) has limitations in handling these uncertainties. We present an MCA approach based on exploratory modeling, which uses computational experiments to explore the multiple outcomes of ISA policies (safety, emissions, throughput, and cost) across a range of future demand scenarios, functional relationships for performance criteria, and user responses to ISA. As an illustration, by testing the impacts of different ISA penetration levels on two driver groups, we show that when compliance with ISA is expected to be low, a policy aimed only at novice drivers outperforms other ISA policies on safety improvement.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".