Spatial Evaluation of Roadway Infrastructure for Safety Improvement on Expressways
Bibliographic record
Abstract
Identifying hazardous locations on highways is an essential step for safety improvement programs and projects since it provides decision makers with a logical and scientific basis for the allocation of resources in a cost-effective manner. There have been numerous studies conducted to develop suitable methodologies for identifying hazardous locations; however, most of them have not considered spatial interactions which are inherent in traffic accidents. In this paper, we use the GIS-based geographically weighted regression (GWR) that can model crash outcomes and identify hazardous locations on expressways while reflecting the effect of spatial dependency and heterogeneity on the outbreak of traffic accidents. This method has been applied to a case study at Gyeongbu Expressway in Korea with 3-year crash data. Koenker's studentized Bruesch-Pagan and Moran’s I tests confirm the spatial relationship among crash data. The findings indicate that it is proper to model crash frequency with GWR for identifying hazardous locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".