Multistate Safety Evaluation of Intersection Conflict Warning Systems
Bibliographic record
Abstract
Intersection conflict warning systems (ICWSs) were evaluated under the FHWA Evaluation of Low-Cost Safety Improvements Pooled Fund Study. The ICWS strategy is intended to reduce the crash frequency by alerting drivers of conflicting vehicles on adjacent approaches at unsignalized intersections. The evaluation used a multistate database of geometric, traffic, and crash data for rural four-legged, two-way, stop-controlled intersections equipped with ICWSs in Minnesota, Missouri, and North Carolina. To account for potential selection bias and regression to the mean, an empirical Bayes before–after analysis was conducted by using safety performance functions (SPFs) for reference groups of similar intersections without ICWS installation. These SPFs also controlled for changes in traffic volumes over time and time trends in crash counts unrelated to the strategy. The aggregate results indicate statistically significant crash reductions at the 5% level for all crash types for two-lane-at-two-lane intersections and four-lane-at-two-lane intersections. For two-lane-at-two-lane intersections, the crash modification factors (CMFs) for total crashes, fatal and injury crashes, and right-angle crashes are 0.73, 0.70, and 0.80, respectively, and for four-lane-at-two-lane intersections, they are 0.83, 0.80, and 0.85, respectively. The benefit–cost (B:C) ratio estimated with conservative cost and service life assumptions is 27:1 for all two-lane-at-two-lane intersections and 10:1 for four-lane-at-two-lane intersections with post-mounted warning signs. The results suggest that the strategy, even with conservative assumptions on cost, service life, and the value of a statistical life, can be highly cost-effective. As this strategy is evolving, this study reflects installation practices to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".