The sustainable transport planning index: A tool for the sustainable implementation of public transportation
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
Abstract The transportation sector contributes significantly to global greenhouse gas emissions, so it is crucial to assess and measure the sustainability of transportation systems. In this context, this study was conducted to develop an integrated index through the use of the multi‐criteria decision analysis method. The method combines existing discrete indexes into one comprehensive evaluation of public transportation, resulting in the sustainable transport planning index (STPI). In the STPI model, sustainability of transportation systems is assessed based on social, economic, and environmental factors that support the implementation of zero emission busses. The weight of each indicator is determined through the analytical hierarchy process, where expert judgment is used to assess the relative importance of each indicator. Normalization of indicators is performed to ensure comparability and consistency. The final STPI index is calculated as the weighted average of the normalized indicators. The STPI model reduces bias in the decision‐making process by considering multiple aspects and utilizing a structured approach to transport planning. The results of this method can provide valuable insights for decision‐makers, public transport agencies, government ministries, the private sector, and other stakeholders. As case study model, the STPI model was applied to the public transport system of the United Kingdom from 2007 to 2019, however; the methodology and lessons learned are applicable to all countries that are in the process of assembling data sets to weigh trade‐offs and inclusions in relation to sustainable transit such as accessibility and health impacts.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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