An integrated approach based on system dynamics and ANP for evaluating sustainable transportation policies
Why this work is in the frame
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Bibliographic record
Abstract
Transportation systems are growing and complex systems. The presence of multiple, correlated, dynamically changing elements in this system with dependence and feedback add further complexity to the problem. In this paper, we present an integrated approach based on system dynamics (SD) simulation and analytic network process (ANP) for evaluating sustainable transport policies. Five policies namely trip sharing (TRS), trip rate reduction (TRR), reducing the length of the road network (LRN), car ownership (CAO), and average kilometres travelled (AKT) are evaluated against three criteria namely congestion level (CONG), fuel consumption (FULC), and emission (EMIS). The data for the policies is generated via system dynamics simulation. ANP is used to rank the evaluation criteria and the alternatives (sustainable transportation policies). A numerical study is provided. The results of our study reveal that trip sharing based policies perform better in comparison to the other policies for achieving sustainability in a transportation system.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it