Towards Sustainable Transportation in Urban Areas: A Case Study
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
This study aims to develop a sustainable transportation development strategy in Semarang City. Collecting data using interviews, and questionnaires. Source of data from keyperson with purposive sampling technique. Keypersons consist of the Head of Sub-Division of Transportation and Water Resources Planning, Expert Staff of Transportation and Water Resources Planning, Head of Pollution Control and Environmental Conservation Division of the Environment Service, Operational Manager of Trans Semarang BRT, Expert Staff of the Public Service Agency of the Regional Technical Implementation Unit (UPTD). BRT Trans Semarang, Civil Engineering Lecturer in the Transportation Sector. Data were then analyzed using the Analytical Hierarchy Process (AHP) technique. The findings revealed that the development of transportation system facilities and infrastructure is the top priority for policy. The second priority is improving environmental quality and Government policy turns out to be the next strategic priority. The practical significance of this research is that the determination of strategic priorities can be applied to other cities that have characteristics as metropolitan cities and have a commitment to carry out sustainable transportation in order to achieve effective and optimal results.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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