Advancing the Methodology for Identifying Crash Contributing Factors in Implementing a Systemic Approach for Prioritizing Sites for Safety Improvements
Bibliographic record
Abstract
The traditional approach of network screening to identify roadway sites for safety improvements has issues, such as the tendency for safety countermeasures to be targeted only at locations that have experienced a high frequency of crashes. The systemic safety approach, on the other hand, proactively identifies risk factors for focus crash and site types, then reviews the road network and identifies locations for implementation of appropriate countermeasures based on the prevalence of these risk factors. Using crash data from Ohio collector road segments, the focus site type, as a case study, this research aimed to advance the systemic approach methodology by first developing a severity index (SI) that considers the frequency and cost of crashes in each severity level. An advanced method that integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) algorithms was then proposed and applied to identify contributing factors to focus crash types by determining those affecting SI for a focus site type. The technique produced relative importance scores that were then used to rank and compare contributing factors for the focus crash types. Then, road segments were not only identified, but also prioritized for safety improvements targeting the focus crash type based on the prevalence of critical contributing factors and their importance scores. The example demonstrated that locations without crashes may still be categorized high priority for safety improvement with the systemic safety approach. The proposed methodology incorporates advanced concepts but is nevertheless implementable beyond the research community.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".