Framework for Assessing Public Transportation Sustainability in Planning and Policy-Making
Bibliographic record
Abstract
Transportation plays a key role in urban sustainability planning and urban greenhouse gas emission reductions. Globally, cities have established sustainability agendas and policies to guide the shift from traditional private automobile dependent transportation systems towards an increased use of public transportation, cycling, and walking. While the surrounding physical urban form and governance structures condition public transportation services, there are also many other factors to consider when discussing sustainability. As such, comprehensive planning and policy-oriented assessment frameworks that are independent of local conditions are still largely missing in literature. This paper presents a Public Transportation Sustainability Indicator List (PTSIL) that provides a platform for an integrated assessment of environmental, economic, and social dimensions of sustainability through an indicator-based approach. To demonstrate its use, the PTSIL is applied to analyze the policy documents of public transportation agencies in Helsinki, Finland, and Toronto, Canada. The results show that while both cities achieve relatively high scores in all dimensions, there is still high variability among individual indicators. The PTSIL presents a missed stepping stone between descriptive definitions of transportation sustainability and case specific sustainability performance assessments, offering an opportunity within the planning and policy-making sectors to review, assess, and develop public transportation services comprehensively.
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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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".