Motorcyclist Is the Right-of-Way Violator: A Population-Based Study of Motorcycle Right-of-Way Crash in Taiwan
Bibliographic record
Abstract
The most typical and catastrophic car-motorcycle crash occurs when a car manoeuvres into the path of an approaching motorcycle at an intersection, which involves a car driver violating motorcycle’s right of way (ROW). In Taiwan, however, motorcyclists are frequently the ROW violator—they are observed to frequently infringe upon the ROW of oncoming vehicles at intersections. Such a ROW crash in which a left-turn motorcyclist crosses in front of approaching traffic appears to be a safety problem in terms of its frequency and accident consequence. Using the National Taiwan Crash Database, the present study estimates a logistic regression model to predict the likelihood of an approach-turn motorcycle-turning crash (relative to a car-turning crash). Results indicate that given a ROW crash where the rider was female, old, drunk, unlicensed, riding a moped, and on a NBU roadway, the likelihood of a motorcycle-turning crash tends to increase. Our study contributes to the existing motorcycle safety research by reporting the determinants of the unique crashes in which the motorcyclist is the ROW violator.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".