Optimizing travel costs of feeder-integrated public transport system: A methodology
Bibliographic record
Abstract
Canada, with a substantial contribution from the personal transport sector, is a major per capita greenhouse gas emitter. This study advocates a sustainable 3-echelon transportation system, integrating Public Transit (PuT) and demand-responsive transit (DRT) for door-to-door service. Electric autonomous DRT vehicles serve the first and third legs of travel, while the second leg relies on PuT. The goal is to identify routes for commuters simultaneously optimizing user, operator, and emission costs. A novel evolutionary algorithm , guided by fuzzy inference systems, optimizes travel costs. The algorithm is calibrated, and its performance is validated against benchmark instances. The proposed optimization framework demonstrates superior performance, achieving quick convergence even for large instances with over 5,000 billion possible routes. Near-optimal routing solutions for sizable scenarios with approximately 100 commuters, 250 PuT nodes, and 50 DRT vehicles can be computed within approximately 20 min.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".