Analysis of Crash Precursors on Instrumented Freeways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traffic flow characteristics that lead to crashes on urban freeways are examined. Since these characteristics are observed prior to crash occurrence, they are referred to as “crash precursors.” The objectives are ( a) to explore factors contributing to changes in crash rate for individual vehicles traveling over an urban freeway and ( b) to develop a probabilistic model relating significant crash precursors to changes in crash potential. The data used to examine crash precursors were extracted from 38 loop detector stations on a 10-km stretch of the Gardiner Expressway in Toronto for a 13-month period. An aggregate log-linear model was developed relating crash rates to the selected crash precursors observed upstream of the crash site. The results of this analysis suggest that the variation of speed and traffic density are statistically significant predictors of crash frequency after controlling for road geometry, weather, and time of day. With the model, crash potential can be established based on the precursors obtained from real-time traffic data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it