A Framework for Intersection Traffic Safety Screening with the Implementation of Complex Network Theory
Bibliographic record
Abstract
The traffic safety screening could provide guidance for determining the use of resources for traffic safety improvements and is critical for the traffic management. To account for the impacts of traffic safety and the effects of intersection as a node in the roadway network, a framework with six indicators and four strategies is proposed for intersection traffic safety screening. The traffic flow has been incorporated into the process of developing three indicators from the perspective of the complex network theory to measure the node importance. For the assessment of traffic safety, other than the observed and estimated crash frequency and modified time-to-collision, a distance that describes the nonlane-based movements has been proposed from the perspective of traffic safety. A multilayer entropy-weighted VIKOR (MEW-VIKOR) approach is proposed to compute the ranking results, and four strategies have been developed to better account for the effects of the six indicators simultaneously. A roadway network with 28 intersections in Shenzhen has been adopted to verify the effectiveness of the proposed framework for intersection traffic safety screening. The results indicate that the proposed framework with two layers could represent the features of traffic safety and the characteristics of node importance and satisfy the expectation from the public, government, and research institutes. With an appropriate threshold setting, the ranking results are consistent with the intersection safety investigation and contribute significantly to the reduction of false-positive and false-negative cases in identifying the black spot 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".