Motorcycle Conspicuity and the Effect of Fleet DRL: Analysis of Two-Vehicle Fatal Crashes in Canada and the United States 2001-2007
Bibliographic record
Abstract
This study involved testing the Fleet DRL Hypothesis that widespread use of daytime running lights (DRL) among the motor vehicle fleet is associated with an increased risk for certain types of multi-vehicle motorcycle crashes. This hypothesis is based on the assumption that the conspicuity of motorcycles (which normally run with their headlamp illuminated all the time) is effectively reduced during the daytime when a high proportion of other vehicles have DRL illuminated. To test the hypothesis, crash data from Canada where DRL use was mandatory were compared to crash data from 24 northern U.S. states where DRL use was not mandatory and fleet penetration of DRL was modest. Crash data from the Fatality Analysis Reporting System (FARS) for the period of 2001 – 2007 were compared to fatal crash data from the Canadian National Collision Data Base (NCDB) for the same years. Crash scenarios that were plausibly relevant to frontal conspicuity of the involved vehicles were defined as DRL-relevant. The proportion of DRL-relevant crashes was modeled by country, year, and whether the crash involved a motorcycle. The authors fit separate models for crash data that occurred in four groups defined by time of day (Day, Night) and location (Rural, Urban) of the crash. The results supported seven of ten predictions indicating that the Fleet DRL Hypothesis may be true for urban roadways (but may not be true for rural roadways). These results support the Fleet DRL Hypothesis for urban roadways, that widespread use of DRL in the vehicle fleet increases the relative crash risk for certain types of motorcycles crashes. This conclusion should be interpreted cautiously in light of the limitations of the analysis approach.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".