Sustainable public bus transit systems: Proof-of-concept
Bibliographic record
Abstract
Providing sustainable transportation services has turned into ongoing concern all around the world. As such, public transit services, which are widely accepted as a more sustainable transit mode, have attracted attention. Among all public transit systems, public bus transit systems are arguably the most affordable, flexible, and popular. However, there have been few attempts towards addressing the problem of sustainable public bus transit network design and operation planning. There is no globally accepted definition of a sustainable bus transit system. In addition, the literature does not include a method for evaluating the sustainability of such systems. This paper begins with a literature review of the cost/benefit factors and evaluation criteria considered in previous studies. Then, we categorize these criteria into three major groups according to the three principles of sustainability: the environmental, social, and economic dimensions. In the next step, the final list of criteria in each sustainability dimension is presented, considering different filtering measures. We also identify various beneficiaries related to public bus transit systems. Finally, we propose a mathematical approach that ranks and weights the identified criteria from the perspective of each beneficiary, as well as collectively for all beneficiaries. This method can be used to select the final list of criteria for evaluating the network design and operations planning of a sustainable public bus system. This proposed approach is based on the Group Best Worst Method (GBWM). To assess the applicability of the proposed model, the GBWM is implemented on a small sample in the cities of Calgary, Canada, and Beijing, China, and the findings for both cities are examined. The outcomes indicate that the definition of sustainability in public bus transit services is localized concept rather than universal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".