Safety Analysis of Signal-Coordinated Arterials with Multiple Heterogeneities
Bibliographic record
Abstract
In the corresponding research available, the safety impact remains controversial in implementing signal coordination on arterials, which calls for an in-depth exploration with the appropriate statistical methods. Based on the traffic data from Ann Arbor City (Michigan, USA), the paper proposes a safety evaluation model considering the multiple heterogeneities. In terms of arterials with the coordinated signalization, modeling results imply that (1) the multivariate heterogeneity shows the strongest interaction on crash frequency, followed by the spatiotemporal and structural heterogeneities, and (2) the spatial variation is unrelated to the temporal change among crashes in the denoted traffic analysis zones (TAZs). In an attempt to alleviate the coupled crash risks along the coordinated arterials, the study emphasizes the necessity of dividing the subcontrol traffic areas in real time according to the correlative degree of crash distribution. Meanwhile, the modeling framework with multiple heterogeneities can be applied for the safety analysis of other urban roads.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".