Machine learning nested MCDM model to enhance decision reliability for transport safety engineering
Bibliographic record
Abstract
Ensuring robust and defensible decision is a critical attribute of multi-criteria decision-making (MCDM) activities, particularly in public sector decision-making (e.g., transport safety engineering). To this end, this study introduces an advanced machine learning embedded MCDM model that integrates the preference selection index (PSI), an alternative ranking order method accounting for two-step normalization (AROMAN), and a Gaussian mixture model (GMM), i.e., PSI–AROMAN–GMM, aiming to provide a reliable decision support system in transport safety engineering. In particular, the proposed model incorporates a machine learning algorithm (i.e., t-distributed stochastic neighbor embedding (t-SNE)) to reduce the computational load and enable efficient handling of large datasets. Specifically, this approach addresses the challenge faced by conventional GMM in uncertain initialization and identification of distinct natural clusters, and it also resolves the issue of GMM in setting correct number of Gaussian components to avoid the model overfitting or underfitting. Through a case study on transport safety engineering for G20 countries, multilevel empirical comparisons validate the robustness of the proposed model, highlighting its practicality and efficiency in informing reliable decisions and policy insights. Overall, this study provides decision-makers, practitioners, and engineers with a comprehensive framework for handling real-world socio-economic activities, especially in transport safety engineering across varied national contexts, with substantial reliability and applicability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".