Voices of the Vulnerable: Insights from A German Road Safety CoCreation Workshop
Bibliographic record
Abstract
Global Road traffic incidents account for approximately 1.19 million fatalities annually, with an additional 50 million people sustaining injuries.Vulnerable Road Users (VRUs) constitute a significant proportion of these statistics (almost half).The scenario is akin to Europe, where 70% of the overall road fatalities are attributed to VRUs.Initiatives like the European Commission's Vision Zero aim to eliminate road accident fatalities and enhance transportation safety.Therefore, understanding the specific challenges VRUs face is crucial to address this issue effectively.While prior studies have explored challenges among specific VRUs, this study comprehensively gathers a wide range of stakeholder groups and analyses the various attitudinal and subjective concerns of VRUs in Germany.By adopting a co-creation approach through online workshops, insights and common challenges faced by VRUs were identified.Key themes such as infrastructural issues, behaviour and attitude, requisite for training and awareness, law and regulation enforcement, personal safety concerns, leveraging technological advancement, environmental concerns, and potential solutions emerged from thematic analysis of qualitative data.This provides valuable information for those instrumental in driving change and for those delivering policy.Moreover, this study underscores the importance of involving end-users in planning mobility infrastructure to tailor safety measures to diverse VRU needs.Adopting a human-centric design approach is critical to reducing road injuries and fatalities and ensuring equitable access and safety for all VRUs in Germany.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".