Decision-making in bus-transit systems: A comprehensive approach based on stochastic multi-criteria acceptability analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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