Factors Contributing to Median Encroachments and Cross-Median Crashes
Bibliographic record
Abstract
Previous studies of contributory factors associated with cross-median crashes have typically focused on median width and average daily traffic (ADT). A few studies have looked at the influence of geometry and cross-sectional elements. These studies did not explore many other design and operational factors that may contribute to cross-median crash frequency or severity (e.g., interchange ramps, interchange spacing, mixture of vehicle types, peak-period volumes, peak-period duration, land use, access control, driver workload, posted speed, or presence of speed transition zones). All median-related incidents begin with a median encroachment. Reducing median encroachments will reduce both cross-median crashes and fixed-object crashes in the median. Consequently, analyzing median encroachments should provide additional insight into the causes of cross-median crashes. There is also a knowledge gap regarding countermeasures appropriate for the various factors contributing to median encroachments and cross-median crashes. Although installing a barrier will greatly reduce cross-median crashes, it will also increase fixed-object crashes and the crash risk of maintenance personnel. Other countermeasures besides barriers exist, and knowing which ones effectively address the contributory factors on a highway will allow an engineer to develop a more effective design. This report identifies design and operational factors that contribute to the frequency and severity of median encroachments and cross-median crashes. It also identifies countermeasures for addressing those contributory factors. For this project, the research team reviewed the literature on median encroachments and cross-median crashes. Based on a survey of states, Canadian provinces, and turnpike/toll road authorities, the team compiled a list of design and operational factors likely to contribute to median encroachments and cross-median crashes. The research team then collected data to determine the relative contribution of each of the factors to median encroachments and cross-median crashes. Appendix D of the report provides recommended guidelines for reducing the frequency and severity of median-related crashes. This material is designed to be easily incorporated into a transportation agency’s design manual.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".