Safety Effect of Diverging Diamond Interchanges on Adjacent Roadway Facilities
Bibliographic record
Abstract
Recent evidence has shown that the diverging diamond interchange (DDI) design improves the safety of the ramp terminals and the overall safety of the interchange. What is still not known is the safety effect that a DDI has on adjacent intersections and the speed-change lanes (SCLs) at freeway entrances and exits. This study addressed this void by examining DDI installations in Missouri. The early and many DDI implementations in Missouri provided a rich data set with which to conduct this study. Twelve major signalized intersections adjacent to the DDI ramp terminals were examined. Thirty-two SCL facilities, 16 freeway entrances, and 16 exits from 11 DDI sites also were examined. A manual review was done to assign 4,073 crash reports to corresponding facilities. The empirical Bayes (EB) method was used to estimate the safety effect of the DDI on adjacent facilities. No evidence showed that the DDI design had any effect, positive or negative, on the crashes that occurred at the entrance or exit SCLs. After DDI implementation, the changes were not statistically significant for SCL crash frequency, property damage only (PDO) crashes, and total crashes. For signalized intersections next to the DDI ramp terminals, the EB analysis showed a 6.5% decrease in fatalities and injuries, which was not statistically significant. The analysis also showed a 19.5% increase in PDO and a 12% increase in total crashes, albeit statistically significant only at the 90% confidence level. In summary, no strong evidence was found that DDIs affected safety, either negatively or positively, on adjacent SCLs or intersections.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".