Advancing the methodology for predicting the safety effects of highway design and operational elements
Bibliographic record
Abstract
Road traffic crashes are one of the major causes of deaths and injuries around the world killing approximately 1.2 million people and injuring over 50 million every year. One of the primary goals of transportation agencies around the world is to reduce crashes as well as minimize the potential for human error and provide a forgiving road environment. Estimating the safety effects of highway design and operational elements is essential in achieving this goal. This research is divided into two components aimed at advancing the methodology for estimating these effects. The first component looks at evaluating the potential of cross-sectional analysis for developing crash modification factors/functions (CMFs/CMFunctions) used to represent the effects of safety treatments on crashes. First, the cross-sectional approach was used to investigate the safety effects of horizontal curvature on rural two-lane highways, which would be impossible to evaluate with before-after data. Second, this approach was further evaluated using databases of sites that were actually treated and similar, but untreated reference sites. The treatment databases for this part consisted of combination rumble strips (center line plus shoulder), wet reflective pavement markings and intersection conflict warning systems. The results from the cross-sectional analysis were then compared to recent studies where empirical Bayes before-after analysis was conducted with the same dataset used for the cross-sectional analysis. The results were promising in that the effects from the cross-sectional and before-after studies were reasonably comparable in each case. In addition, it was possible in some cases to relate the CMF to application circumstances by developing CMFunctions, providing results that could not be achieved in the before-after studies. The second component of this research involved development of roundabout crash predictions using conflicting volumes and delays, which could only be estimated from turning movement counts that are rarely collected at roundabouts. The object was to determine whether the considerable extra effort to collect these data would be worthwhile. The developed models were compared to the traditional models based on approach flows. The results suggest that collecting turning movement data, which is also required for capacity analysis, would be worthwhile for evaluating roundabout safety.
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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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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 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".