Decision-making in bus-transit systems: A comprehensive approach based on stochastic multi-criteria acceptability analysis
Bibliographic record
Abstract
The shift to sustainable transportation presents challenges regarding the acquisition or replacement of bus fleets. The need to consider multiple, conflicting and incommensurate factors such as environmental impact, cost-effectiveness, and technological issues makes the decision-making process more complex, time-consuming, and possibly ineffective, thus requiring an adequate multi-criteria evaluation framework. To this end, this study employs the stochastic multi-criteria acceptability analysis method, assessing the feasibility of transitioning to eco-friendly bus fleets by comparing diesel, hybrid, and electric buses while addressing uncertainty. Using the bus transportation system of Sherbrooke (Canada) as a case study, computational simulations generate energy consumption data and define bus system configurations. The case study evaluates five concrete alternatives over twelve criteria. The results show that an electric bus system with an overnight charging strategy outperforms other options (in 32 % of the cases) due to its reliability, cost-effectiveness, and mixed use of the bus fleet. Conversely, the diesel bus alternative consistently ranks lowest due to poor performance on economic and environmental criteria.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".