Comparison of Crash Severity Risk Factors at Signalized and Stop-Controlled Intersections in Urban and Rural Areas in Alabama
Bibliographic record
Abstract
Understanding the factors that affect crash severity at intersections is essential to develop strategies to alleviate safety deficiencies. This paper identifies and compares the significant factors affecting crash severity at signalized and stop-controlled intersections in urban and rural areas in Alabama using recent five-year crashes. A random forest model was used to rank variable significance and a binary logit model was applied to identify the significant factors at both intersection types in urban and rural areas. Four separate models (urban signalized, urban stop-controlled, rural signalized, and rural stop-controlled) were developed. New variables that were not previously explored were used in this study, such as the roadway type (one-way vs. two-way) and traffic control functioning (yes or no). It was found that one-way roadways were associated with a reduction in crash severity at urban signalized intersections. In all four models, rear-end crashes showed lesser severity than side impacts. Head-on crashes, higher speed limits, and curved sections showed higher severity in urban signalized and stop-controlled intersections. In rural stop-controlled intersections, right-turning maneuvers had a severity reduction. Female drivers showed 15% and 45% higher severity likelihood (compared to males) at urban and rural signalized intersections, respectively. Strategies to alleviate crash severity are proposed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".