Glancing and Stopping Behavior of Motorcyclists and Car Drivers at Intersections
Bibliographic record
Abstract
For the past decade, motorcycle fatalities have risen while other motor vehicle fatalities have declined. Many motorcycle fatalities occurred within intersections after a driver failed to see a motorcyclist. However, little is known about the behavior of motorcyclists when they negotiate an intersection. A study was undertaken to compare the behavior at intersections of an experienced group of motorcyclists when they were operating a motorcycle with their behavior when they were driving a car. Each participant navigated a course through low-volume, open roads. Participants wore eye-tracking equipment to record eye-glance information, and the motorcycle and car were instrumented with an onboard accelerometer and Global Positioning System apparatus. Results showed that participants were more likely to make last glances toward the direction of the most threatening traffic before they made a turn when they were driving a car than when they were riding a motorcycle. In addition, motorcyclists were less likely to come to a complete stop at a stop sign than car drivers. These results suggested that motorcyclists were exposing themselves to unnecessary risk. Specifically, motorcyclists frequently failed to make proper glances and practice optimal riding techniques. The behavior of the motorcyclists was compared with the current Motorcycle Safety Foundation curriculum. The results suggested that threat-response and delayed-apex techniques should be added to the training curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".