Investigating factors affecting injury severity in bicycle–vehicle crashes: a day-of-week analysis with partial proportional odds logit models
Bibliographic record
Abstract
Cyclists are vulnerable road users and prone to experience severe injury when accidents occur. Both driving and riding behaviors of drivers and cyclists could vary in different days of week, which further influences the injury severity of crashes involving cyclists. This study aims to investigate the factors that affect injury severity in crashes involving cyclists on weekdays and weekends separately using police-reported data ranging from 2007 to 2018 in North Carolina. The impact of cyclist, driver, vehicle, road, environment, and crash characteristics on injury severities are explored. Ordered logit model and partial proportional odds logit models are developed respectively for the injury severities of crashes on weekdays and weekends. Different sets of significant factors are identified for weekdays and weekends. For the common factors identified for both weekdays and weekends, the influencing extent of significant variables varies significantly between weekdays and weekends. Older-aged cyclists, riding direction, pickup, older-aged drivers, male drivers and periods of 0–5:59 and 10–14:59 are only found significant on weekdays while speed limits of 45–55 mph, piedmont areas, commercial development, head-on, and non-roadway locations are only found significant for injury severities of crashes on weekends. Speed limits, time of day, alcohol usage are found to have different or even opposite impacts on the injury severities of crashes on weekdays and weekends.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".