Integrating Communication with Conspicuity to Enhance Vulnerable Road User Safety: ArroWhere Case Study
Bibliographic record
Abstract
This paper presents findings from a research study into the role that communication plays in the safety of vulnerable road users (VRUs), including a literature review, a hypothesis, and a case study testing our hypothesis. Many governments and road authorities lack capital or have not made it a priority to implement full VRU safety measures, with many gaps in VRU infrastructure and networks. These gaps leave VRUs to take safety into their own hands, including use of conspicuity aids such as high-visibility wear, helmets, bells, and lights with differing levels of effectiveness. The knowledge gap regarding the conventional wisdom, “be safe, be seen,” is the absence of communication and comprehension between road users (VRUs and vehicles). We hypothesize that communication aids are equally, if not more important than visibility aids for VRU safety. A case study was conducted to measure the effectiveness of several Hi-Viz safety vest designs including online surveys and separate in-field experiments using Instrumented Probe Bicycles. The results suggest that Hi-Viz safety vests using arrow designs (ArroWhere’s proprietary products and designs) similar to those found in the Manual on Uniform Traffic Control Devices (MUTCD) can increase VRU safety until road authorities can fully fund and complete proper and sustainable VRU networks.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".