Scenario Co-Design for Systemic Evaluation of Connected and Automated Mobility Setups
Bibliographic record
Abstract
Connected and automated vehicles are today at various stages of development. Due to their transformative potential, both on the existing transport system and on urban spaces, it is essential to investigate their impacts from a systemic perspective. A multi-factor evaluation cannot be based only on experimental setups. Projections of up-scaled, operational connected and automated mobility (CAM) services are required. In this article we propose TRESSY, a scenario-building approach for CAM service up-scaling. TRESSY follows a four-step model that aims to generate mid-term projections of relevant services based on foreseeable technological and infrastructure developments. We applied TRESSY in a multi-stakeholder CAM pilot project where experts collaborated on the design of a range of up-scale service scenarios and their associated technical systems and infrastructures. The results obtained show how TRESSY can facilitate the collaboration between heterogeneous stakeholders working on representative niche, critical technical systems of future mobility.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".