A hybrid machine learning-enhanced MCDM model for transport safety engineering
Bibliographic record
Abstract
Delivering reliable decision recommendations and policy inferences is essential for multi-criteria decision-making (MCDM) processes, particularly for transport safety engineering. This study proposes a hybrid machine learning-enhanced MCDM model that integrates distance correlation-based criteria importance through intercriteria correlation (DCRITIC), weighted aggregated sum product assessment (WASPAS), and K-means clustering, referred to as the DCRITIC-WASPAS-K-means model. In particular, we incorporated a machine learning tool (i.e., a graph-based technique) into the model to effectively and robustly select initial centroids. This integration addresses the uncertainty in traditional k-means clustering, which arises from varying initial centroids and its sensitivity to outliers, especially in datasets with noisy or skewed data points, and, more importantly, reduces the number of iterations and runtime cost. This approach improves the robustness and reliability of decision outcomes, thereby supporting more credible and actionable policy interventions. A case study involving transport safety engineering in the Organization of American States (OAS) region validates the model's practical utility. Comparative analyses demonstrate its superior performance in ensuring consistent decision outputs and communicating policy implications effectively. The proposed framework provides public administrators, policymakers, and government agencies with a reliable, scalable, and data-driven tool for strategic planning and resource allocation in uncertain environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".