Non-Intersection-Related Crashes at Midblock in Urban Divided Arterial Road with High Truck Volume
Bibliographic record
Abstract
This study analyzes the crashes that occur at mid-block called “mid-block crashes” in an urban arterial road. The association of mid-block crashes with various factors was examined using the 7-year (2000-2006) crash data on a section of a divided arterial road in Windsor, Ontario, Canada. To account for difference in traffic volume and road geometric factors between two directions of travel in a divided road, the data were collected for two directions separately. The results of log-linear models using these bidirectional data show that mid-block crashes are more likely to occur on the road sections with access point and high percentage of truck (> 20%). It was also found that the effects of access point and truck percentage were not statistically significant when the unidirectional data were used. A sensitivity analysis was also performed to identify the bidirectional variables affecting crash frequency by direction. It was found that the difference in truck percentage between two directions can most effectively reflect the difference in crash patterns by direction. The results of logistic regression models show that median opening, driver age/gender, lighting, time of day and day of week are associated with different types of crashes classified by the vehicles involved in crashes. The study shows the importance of analyzing mid-block crashes using the bidirectional data by vehicle type in urban divided arterial roads with high truck volume.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".