Investigating Total Annual Average Daily Traffic as a Surrogate for Motorcycle Volumes in Estimating Safety Performance Functions for Motorcycle Crashes
Bibliographic record
Abstract
Data on traffic volumes are required to estimate the safety performance functions (SPFs) used to develop crash modification factors and for various safety management applications. Estimation of SPFs for motorcycle crashes can be especially challenging because few jurisdictions collect motorcycle traffic volume data systematically. To address this challenge, analyses with data from Florida, Pennsylvania, and Virginia were conducted to explore how much predictive power for an SPF was lost when motorcycle traffic volumes were not known. The results of the analyses showed that when motorcycle volumes were unknown, the use of total annual average daily traffic on its own was sufficient to develop motorcycle crash SPFs. The potential bias from missing motorcycle-specific annual average daily traffic was sufficiently negligible where it existed, not to preclude SPF development. A more significant issue in the development of motorcycle crash SPFs is to work with a crash type that is relatively rare, so that SPFs cannot be developed for all motorcycle crash types or site types.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".