Two‐stage multi‐criteria analysis and the future of intelligent transport systems‐based safety innovation projects
Bibliographic record
Abstract
This study outlines a new two‐stage multi‐criteria analysis (MCA) methodology to facilitate assessing and selecting investments in intelligent transport systems (ITSs). The authors focus on ITS‐based safety innovation projects (SIPs) in the realm of road transport infrastructure, namely, those conducive to more ‘forgiving roads’ and ‘self‐explanatory roads’. Stakeholders interested in improving road safety can use this MCA tool to assess alternative options for improving road safety, based on how each option contributes to each stakeholder group's objectives. The preferences of each stakeholder are fully taken into account in a first stage through partial MCAs, which determine how each SIP contributes to each separate stakeholder's specific objectives. In the second stage, the preferences of all stakeholders are bundled, with more emphasis on societal preferences. This second stage analysis paradoxically allows identifying policy areas where government incentives could address strong concerns voiced by particular stakeholder groups. In other words, an implicit feedback loop is generated to the SIPs’ design, with ‘redesign’ intended to reduce the gap between societal preferences and specific‐stakeholder ones, thereby increasing the probability that the support of all stakeholder groups involved could still be ascertained.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".